19 items tagged "intelligent organization"

  • 'Unleashing' the CIO in modern businesses

    'Unleashing' the CIO in modern businesses

    If digital transformation is, in part, a technology transformation; then the CIO should play a major role. It's simple logic, given the CIO’s responsibilities and resources. Simple logic should create simple advice. When it comes to advice for the CIO, none of it is simple.

    On the surface we talk about the role of the CIO in digital transformation. Beneath that surface we use terms and mental models that keep the CIO trapped in a back office functional role. It's subtle, but effective. Too many people, like industry analysts and executives, still think the CIO is just the most senior guy from the IT department.

    Thinking about bias before, during, and after reading something. Reading for bias was the best skill I learned in college. Everything has bias and there are some ways analysts, consultants and others subtly bias and marginalize CIOs. They include:

    Applying a bottom up argument – starting with the CIO’s current responsibilities and how they should expand. This defines the new role as in terms of ‘both and’, their new role and their old role. The dig is this: you cannot do your new role until you discharge your old one. CEO’s, CFO’s and other leaders prove themselves and move into new roles. The CIO’s role on the other hand makes advancement contingent on continuing with their current responsibilities.

    Discussing IT as a function – maintain a difference between the business and IT. Explaining the CIO or IT roles in terms of enablement, support or providing implies an arm’s length and separate relationship. Keeping things separate and functional creates a persistent 'us and them' bias. If technology is the business, then can there really be a separate IT function?

    Applying the same questions to every trend – shows that the CIO’s role will not change. The standard questions revolve around: being more strategic, saving more money, going faster, fixing legacy technology, getting their teams with the right skills, etc. These are concerns they had 20 years ago. If they are the same now, then how can the role be different now? It’s a subtle way of saying that CIOs are not really doing their job well.

    Declaring the role as an outsider – by stating what they need to become. In this case, the phrase ‘the CIO needs to become a business leader’ presumes that they are not.  A search of that phrase produced about 53,400,000 results (0.64 seconds) in Google. If a CIO is not a business leader, then they must be just the IT guy. 'You are not one of us', is the sign of disrespect even in the face that its untrue.

    Industry analysts can see these points as recognizing reality. CIO reporting relationships, membership in boards, roles and responsibilities are what they are. Yes, and assuming they will stay that way subtly marginalizes the CIO role in the C-suite and business. The articles, analysis and research coming out today is eerily similar to similar lines of argument made about the challenges of the internet, cloud, analytics etc.

    A recent McKinsey Digital article 'The CIO challenge: modern business need a new kind of tech leader' illustrates these observations. This article is a recent example of a long term trend. I am using it to illustrate the ways in which we subtly keep the CIO in their place.

    The article argues that ‘now is the worst time to be an average CIO.’ They then discuss the need for CIOs to go beyond simply managing IT to leveraging technology to create value for the business. The authors argue that CIOs need to deliver on three vectors of holistic transformation and exhibit five traits of a transformational CIO. The authors, in good faith, have defined their view on what CIOs need to do in order to become a new kind of tech leader. The comments in (parenthesis) highlight how these points support a marginalized view of the CIO.

    The vectors are in summary:

    • Reimagining the role of technology in the organization – establishing the role of technology as a business and innovation partner.
    • Reinvent technology delivery – IT needs to change how it functions by embracing agile, improving IT services, etc.
    • Future-proof the foundation – keeping pace with rapid technology advances and supporting a flexible architecture with modular platforms, data ubiquity and cybersecurity.

    The five traits include:

    • Business Leader – with the imperative that the CIO needs to understand business strategy and business from the inside and out. CIOs need to take responsibilities for initiatives that generate revenue.  CIOs need to get on boards to build networks outside of their organization.
    • Change Agent – digital transformation requires infusing technology into every strategy discussion and processes across the organization. CIOs need to partner with business leaders and articular the why, risks and plans beyond IT.
    • Talent Scout – address technology skills gaps on traditional teams. This requires attracting tech starts, building internal talent and retaining both.
    • Culture Revolutionary – creating a culture that supports talent and technology talent. CIO’s in this view are responsible for building a true engineering community, modelling and supporting true collaboration.
    • Tech Translator – a need to change from IT transformations described as expensive, time consuming and short on value. The authors see this as a trust and education issues making CIOs responsible for clarifying the business implications of technology decisions.

    There is nothing intrinsically wrong with these vectors or traits. It’s the way they are presented, what they presume, their context and the way they make the argument that illustrate how we keep the CIO in their place.

    Everyone should read for bias. Every writer should recognize the bias. Writers should further consider how what they write holds people back.

    Author: Mark P. McDonald

    Source: Gartner

  • 5 Best practices to attract (and retain) talent for your organization

    5 Best practices to attract (and retain) talent for your organization

    By applying these best practices, you can bring on talent that keeps pace with innovation, shifting customer needs, and new technologies.

    It’s no secret that there is currently a massive technology talent shortage. As this Wall Street Journal article notes, tech leaders and recruiters alike increasingly feel the pressure to stay competitive, some even going so far as to offer perks like six-figure bonuses and the ability to work from anywhere they want. Hiring tech talent is a massive pain point across many organizations, and it’s at the top of most IT leaders’ to-do lists.

    Aside from outsized perks, what can organizations do to address the talent shortage? The key lies in looking for talent in new places and uncovering ways to connect with and inspire candidates before, during and after the interview process. Here are five ways to identify, hire, and retain the right team.

    1. Partner with schools

    If you’re not already doing so, build relationships with schools and make it a priority to partner with them to fuel student interest in your company. Current students are the future of your business, so working with universities early and often can both grow and keep your talent pool specialized.

    Many schools have begun implementing programs to directly address the shortage of technology talent, including degree programs in industries like cybersecurity and cloud. MIT, for example, among many others, now offers programs that match specific business needs like 'Ethics of AI' and 'Modeling and Optimization for Machine Learning'. Some cloud providers are teaming up with schools to offer programs and specialized degrees, and we have also seen great success in partnering with universities to sponsor research in engineering departments.

    2. Look to untapped pools of talent

    Beyond looking to recent graduates, consider untapped pools of talent to diversify your workforce. While often overlooked because of 'lack of relevant technical experience', veterans offer skills that could greatly impact your existing teams, including strong leadership, productivity and decision-making capabilities. We can look to companies like Salesforce for inspiration: Its veteran program Vetforce connects the military community with open IT positions.

    Another pool of talent often left behind are those who have taken time off and want to restart their careers, including parents with new children or those who had to care for a loved one in a time of need. Returnship programs for example. These programs help professionals with five or more years of work experience, and who have been out of the paid workforce for a minimum of two years, to bridge their transition back into the workforce. We have found excellent, talented employees through this channel.

    3. Ask the right questions

    Once you have a candidate in mind, ask the right interview questions to determine their potential fit on your team. My favorite interview question is 'What isn’t on your resume that you’d like to share?' A resume tells 'what' you did. But it doesn’t tell 'how' you did it. These stories often provide the most critical insight into a candidate. I want to hear how a candidate has overcome adversity and what they learned from their challenges. I prize candidates’ perseverance and determination rather than a list of accomplishments or schools they went to. Tell me what you did with what you had. With the technology industry changing at a rapid pace, we need candidates who are comfortable being uncomfortable in the name of positive change.

    4. Think beyond money with the job offer

    If you think you can entice today’s talent pool just with compensation, think again. Career growth opportunities now rank as the most important factor when looking for a new job. Offering plenty of opportunities for employee training and growth will not only entice potential candidates, but it will also keep current employees on board. We offer an array of training and certification programs so our employees can build marketable skills in enterprise cloud technology. These programs should be all about choice, enabling employees to design the mix of in-person, online, or video training that meet them wherever they work today. Large, high-growth companies can also offer candidates the ability to easily move between different teams at the company, learn from new groups and cross-pollinate ideas.

    5. Simplify redundant tasks

    Identifying areas where a company can simplify to boost productivity can be an equally important step to the above. For example, automating existing, repetitive IT tasks can help free up time to focus on more innovative, creative projects. At our company, we’re using the power of machine learning (ML) and natural language processing to augment our IT helpdesk and customer support services. Using ML technologies, more than 30% of all service requests are automatically resolved, freeing up both time and budget for value-creating activities.

    When it comes to hiring and retaining the best talent, it can feel like you’re in a losing race against a continually changing technology environment. But by keeping these best practices in mind, you can bring on talent that keeps pace with innovation, shifting customer needs, and new technologies.

    Author: David Sangster

    Source: Informationweek

  • Aligning your business with your data team

    Aligning your business with your data team

    It’s important for everyone at a company to have the data they need to make decisions. However, if they just work with their data team to retrieve specific metrics, they are missing out. Data teams can provide a lot more insights at a faster rate, but you will need to know how to work with them to make sure that everyone is set up for success. 

    Data teams can be thought of as experts at finding answers in data, but it’s important to understand how they do that. In order to get the most value out of your collaboration, you need to help them understand the questions that matter to you and your team and why those questions need to be answered. There are a lot of assumptions that get built into any analysis, so the more the data team knows about what you are looking for, the more knowledge they may find as they explore data to produce their analysis. Here are four tips to make more productive requests from members of your data team: 

    Approach data with an open mind

    It’s important to treat the data request process as an open-ended investigation, not a way to find data that proves a point. A lot of unexpected insights can be found along the way. Make your goal to ask questions and let your data team search for the answers. This approach will allow you to get the best insights, the type of unknowns that could change your decision for the better. If you put limitations on what you’re asking the data, you’ll end up putting limitations on the insights you can get out of your inquiry. 

    To really dig into this, think about how questions are answered scientifically. Scientists treat any bias as an opportunity for the insight to be compromised. For example, let’s say you are looking to improve customer satisfaction with your product. Requesting a list of customers with the highest and lowest NPS scores will give you a list of people who are happiest or most frustrated, but it is not going to let you know how to improve customer satisfaction. This request puts too much attention on the outliers in your customer base rather than identifying the key pain points. That’s part of the picture, but not all of it. If you’re trying to create a program that targets your goal, let your data team know the goal, give them a few optional starting points, and see what they come back with. They might surprise you with some sophisticated analysis that provides more insight and helps you launch a fantastic program. 

    Start with a conversation, not a checklist

    The single biggest mistake a line-of-business professional can make when requesting data is to present a data expert with a list of KPIs and tell the data team to just fill in the blanks. This approach misses so much of the value a data team can provide. Modern data teams have technology and abilities that allow them to go much further than just calculating numbers. They can guide analytical exploration through flexible, powerful tools to make sure you’re getting the most valuable insights out of your data.

    Instead of a list of metrics, think about starting your data request as a meeting. You can provide the business context needed and a list of questions that you want answered. You can even present some initial hypotheses about what those numbers may look like and why they might move in one direction or another. This is a great way to kick off the conversation with your data counterpart. From there, you can benefit from their experience with data to start generating new and more informed questions from their initial inquiries. The data team’s job is to get you information that helps you be more informed, so give them as much context as possible and let them work as a problem solver to find data-driven recommendations.

    Data should recommend actions, not just build KPIs reports

    A lot of standard business KPIs measure the results of company efforts: revenue, lead conversion, user count, NPS, etc. These are important statistics to measure, but the people tracking them should be very clear that these numbers track how the company is moving, not why it is moving that way. To make these data points actionable, you need to take analysis further. Knowing that your NPS is going up or down is useless if it doesn’t inform a customer team about the next step to take. 

    A good data team will map important KPIs to other data and find connections. They’ll comb through information to find the levers that are impacting those important KPIs the most, then make recommendations about how to achieve your goals. When you get a list of levers, make sure to understand the assumptions behind the recommendations and then take the right actions. You can always go back to those KPI reports to test if the levers are having the intended effect.

    Data requests are iterative, give the data person feedback

    Communication about data should not end when the data has been delivered to you. It’s important to dig into the analysis and see what you can learn. Instead of reporting that data or taking action on it right away, you should check with your dashboard creator to make sure that he or she can verify that you’re reading all of the data properly and that the next steps are clear. There are a lot of ways to misinterpret data, a good way to prevent mistakes is to continue communicating.

    Even if you’ve gotten the right takeaways from the data, it’s still good to consult with your dashboard creator and go over your interpretation of the information so they know how you read data. You may need a follow-up meeting to restart with the overall question you want to answer, then see what additional data needs to be collected or what modifications are needed to make the report or dashboard work best for your intended use-case.

    Author: Christine Quan

    Source: Sisense

  • Cognitive diversity to strengthen your team

    Cognitive diversity to strengthen your team

    Many miles of copy have been written on the advantages of diverse teams. But all too often this thinking is only skin deep. That is it focusses on racial, gender & sexual orientation diversity.

    There can be a lot more benefit in having team ;members who actually think differently. This is what is called cognitive diversity. I’ve seen that in both the teams I’ve lead and at my clients’ offices. So, when blogger Harry Powell approached me with his latestbook review, I was sold.

    Harry is Director of Data Analytics at Jaguar Land Rover. He has blogged previously on th Productivity Puzzle and an Alan Turing lecture, amongst other topics. So, over to Harry to share what he has learnt about the importance of this type of diversity.

    Reading about Rebel Ideas

    I have just finished reading 'Rebel Ideas' by Matthew Syed. It’s not a long book, and hardly highbrow (anecdotes about 9/11 and climbing Everest, you know the kind of thing) but it made me think a lot about my team and my company.

    It’s a book about cognitive diversity in teams. To be clear that’s not the same thing as demographic diversity, which is about making sure that your team is representative of the population from which it is drawn. It’s about how the people in your team think.

    Syed’s basic point is that if you build a team of people who share similar perspectives and approaches the best possible result will be limited by the capability of the brightest person. This is because any diversity of thought that exists will essentially overlap. Everyone will think the same way.

    But if your team comprises people who approach problems differently, there is a good chance that your final result will incorporate the best bits of everyone’s ideas, so the worst possible result will be that of the brightest person, and will it normally end up being a lot better. This is because the ideas will overlap less, and so complement each other (see note below).

    Reflections on why this is a good idea

    In theory, I agree with this idea. Here are a few reflections:

    • The implication is that it might be better to recruit people with diverse perspectives and social skills than to simply look for the best and brightest. Obviously bright, diverse and social is the ideal.
    • Often a lack of diversity will not manifest itself so much in the solutions to the questions posed, but in the selection or framing of the problems themselves.
    • Committees of like-minded people not only water down ideas, they create the illusion of a limited set of feasible set of problems and solutions, which is likely to reduce the confidence of lateral thinkers to speak up.
    • Strong hierarchies and imperious personalities can be very effective in driving efficient responses to simple situations. But when problems are complex and multi-dimensional, these personalities can force through simplistic solutions with disastrous results.
    • Often innovation is driven not simply by the lone genius who comes up with a whole new idea, but by combining existing technologies in new ways. These new 'recombinant' ideas come together when teams are connected to disparate sets of ideas.

    All this, points towards the benefits of having teams made up of people who think differently about the world. But it poses other questions.

    Context guides the diversity you need

    What kinds of diversity are pertinent to a given situation?

    For example, if you are designing consumer goods, say mobile phones, you probably want a cross-section of ages and gender, given that different ages and genders may use those phones differently: My kids want to use games apps, but I just want email. My wife has smaller hands than me, etc.

    But what about other dimensions like race, or sexual preference? Are those dimensions important when designing a phone? You would have thought that the dimension of diversity you need may relate to the problem you are trying to solve.

    On the other hand, it seems that the most important point of cognitive diversity is that it makes the whole team aware of their own bounded perspectives, that there may be questions that remain to be asked, even if the demographic makeup of your team does not necessarily span wide enough to both pose and solve issues (that’s what market research is for).

    So, perhaps it doesn’t strictly matter if your team’s diversity is related to the problem space. Just a mixture of approaches can be valuable in itself.

    How can you identify cognitive diversity?

    Thinking differently is harder to observe than demographic diversity. Is it possible to select for the former without resorting to selecting on the latter?

    Often processes to ensure demographic diversity, such as standardised tests and scorecards in recruitment processes, promote conformity of thought and work against cognitive diversity. And processes to measure cognitive diversity directly (such as aptitude tests) are more contextual than are commonly admitted and may stifle a broader equality agenda.

    In other words, is it possible to advance both cognitive and demographic diversity with the same process?

    Even if you could identif different thinkers, what proportion of cognitive diversity can you tolerate in an organisation that needs to get things done?

    I guess the answer is the proportion of your business that is complex and uncertain, although a key trait of non-diverse businesses is that their self-assessment of their need for new ideas will be limited by their own lack of perspective. And how can you reward divergent thinkers?

    Much of what they do may be seen as disruptive and unproductive. Your most obviously productive people may be your least original, but they get things done.

    What do I do in my team?

    For data scientists, you need to test a number of skills at interview. They need to be able to think about a business problem, they need to understand mathematical methodologies, and they need to be able to code. There’s not a lot of time left for assessing originality or diversity of thought.

    So what I do is make the questions slightly open-ended, maybe a bit unconventional, certainly without an obviously correct answer.

    I expect them to get the questions a bit wrong. And then I see how they respond to interventions. Whether they take those ideas and play with them, see if they can use them to solve the problem. It’s not quite the same as seeking out diversity, but it does identify people who can co-exist with different thinkers: people who are open to new ways of thinking and try to respond positively.

    And then try to keep a quota for oddballs. You can only have a few of them, and they’ll drive you nuts, but you’ll never regret it.

    EndNote: the statistical appeal of Rebel Ideas

    Note: This idea appeals to me because it has a nice machine learning analogue to it. In a regression you want your information sets to be different, ideally orthogonal. If your data is collinear, you may as well have just one regressor.

    Equally, ensembles of low performing but different models often give better results than a single high-performing model.

    Author: Paul Laughlin

    Source: Datafloq

  • Continuous improvement requires continuous intelligence

    Continuous improvement requires continuous intelligence

    Business leaders must take the initiative to leverage their data using new technologies and approaches to adapt and succeed in the digital world.

    The digital age has presented businesses with a significant challenge: adaptation. Organizations can only hope to survive in this new era if they are able to adapt to the new reality of doing business.

    For the past few years, adaptation efforts have fallen under the umbrella of digital transformation. It is now widely understood that organizations must engage the groundswell of digital data and refine it into a byproduct that can inform decisions or instantaneous actions. However, because digital data flows continuously, the data engagement model should also be continuous, leveraging advances in machine learning, AI, IoT, and analytics. This sort of continuity will catalyze organizations to adapt and thrive in the new digital reality.

    Continuous intelligence

    Because IT has historically focused on batch processing, the concept of continuous processing is fairly new to most organizations. Continuous intelligence waits for nothing. Not data collection periods, not resource availability, not processing time. It is the non-stop generation of insight and actions based on operational data stores as well as streams of data and events generated in the moment. It is the ability to harness an ever-changing environment where the data is constantly flowing and the insights and actions are perishable.

    According to Gartner, success can only be achieved in a world that is constantly changing by implementing a continuous approach. Gartner suggests that continuous intelligence is at the heart of fast-paced digital business and process optimization. However, continuous intelligence is not only about IT architectures. Successful implementation requires a change in managerial approach as well.

    New leadership approach

    Conway’s Law gave us the insight that system designs reflect the communication structures of the organizations that design them. Because designing continuous intelligence requires new architectures, it is critical that the organizations designing them reflect the architectural intent.

    Most organization structures today assume they are performing in a batch-processing world. One team works to complete a task before handing it off to the next team, there is no continuity of visibility or activity. Initiating a continuous intelligence effort with the limitations inherent in a batch processing management model will produce feet but no wings.

    To fully implement the continuous intelligence approach, business leaders need to adapt to agile management methodologies. Just as the DevOps world engages continuous integration across teams, so must the larger IT organization engage in a more active and constant way. The rate of engagement is necessarily radically higher, that is the only way for the broader team to understand what’s going on in the organization. This approach will facilitate initial success and be the foundation for staying ahead in an era of new, dynamic technologies and continuous change.

    The need for speed

    One of the fundamental changes to the IT stack required for continuous intelligence is a new data processing layer designed to perform at extremely low levels of latency. Regardless of whether the data already exists in operational data stores or arrives in event-based streams, the concept of continuity is at odds with latency.

    Our traditional systems of record do not have this design point, nor should we expect them to. They will continue to do their job well while a new, complementary data processing layer is added.

    Innovations in IoT, machine learning, and AI assume both constancy and immediacy. Business value has become inextricably linked with real-time action. New applications require speed and scalability in the underlying data processing to produce responses as well as to 'feed the beast' to inform models. The money is in the microseconds, whether the data is at rest or in motion.

    Digitization has permanently changed the business landscape. Continuous intelligence is achievable. Business leaders must take the initiative to leverage their data through new technologies and approaches to adapt and succeed in the digital world.

    Author: Kelly Herrell

    Source: TDWI

  • Democratized software: a tool for all parts of a business

    Democratized software: a tool for all parts of a business

    We are witnessing the next wave of software: one that democratizes its use and allows all parts of a business to participate in making it work.

    My background in consulting gave me the opportunity to work with many unique businesses in diverse industries and geographies. Despite their differences, they all wanted to solve the same challenge: how to serve customers and accelerate growth?

    Today I lead one of those companies, and not surprisingly I find myself with the same needs my clients had years ago. I, too, want to serve our customers better and grow our business faster and am looking for the smartest and most innovative ways to do so.

    My time in consulting coincided with the rapid rise of application software and I lived through the transition from on-premise to cloud-based software. While the hosting mechanics changed, the software was similar. In both worlds, CIO shops and large consulting organizations configured application software and designed best-practice business processes to drive adherence to internal process and policy. Process design decisions were controlled centrally and rolled out to business users. A focus on the customer was often secondary to consistency, efficiency and adherence.

    Democratized software, by contrast, allows more people, especially those on the front-line with customers, to own and solve their customers’ needs. Business users can adapt and extend the core business processes without disrupting business and IT integrity. Using low-code (or 'average-joe code') applications, businesspeople can create workflows and business processes without depending on their IT organizations. Problem solving is placed in the hands of the people who identified and live with their customers’ needs and empowers them to act faster.

    We saw this transition happen over the past decade with websites. IT teams grew tired of managing the dynamic nature of websites and put the power to change them in the hands of marketing teams. The technology shift from (for example) Java scripting to WordPress accelerated this change. We now see the opportunity to achieve this same dynamic with other business functions. Whether the software is labeled low-code, forms management, workflow automation, robotics process automation (RPA), or any other number of things, business users can essentially build applications to manage their work without negotiating for, and consuming, precious IT resources.

    As a result, enterprise software is changing and becoming more powerful and flexible for its end-users. At the same time, CIOs and their teams are more comfortable empowering business users to solve for themselves while maintaining the controls and governance to protect the enterprise. Luckily, this flatter and more dynamic environment is one embraced (and even insisted upon) by the new generation of employees and leaders.

    There is another aspect of traditional application software that’s being challenged. For as long as I have worked with CIOs and business leaders, there has been a debate between choosing best-of-breed point solutions vs. fully integrated packages. People often want the advanced functionality associated with best-of-breed, but they don’t want the headaches associated with building and maintaining the integration of these solutions.

    In the old days, larger enterprises moved toward integrated ERP packages to avoid these integration challenges. This left them with less flexible software designed primarily to drive process adherence and control with its users. ERP is about managing the transactions and data associated with core business processes. These core processes don’t embrace the constantly changing needs of customers. They aren’t inherently customer centric. It’s all about consistency, efficiency and process adherence. Today, businesses need to adapt to the needs of their customers, and to connect these core business processes more directly to their customers.

    There is great consumer parallel from the media industry, where customers have subscribed to Comcast or DirectTV for their content needs. These companies assume the challenge of aggregating access and content. The advancement of technology (specifically network wireless access and bandwidth) now allows consumers to curate their own content interests across a broader network. Consumers can subscribe to the content that matters to them, such as ESPN, HBO, Disney+, and Netflix, and often pay less than their current subscription contracts. The same is possible with today’s flexible SaaS software. CIOs can buy multiple packages and allow business owners to solve customer needs in the field. The advancement in technology makes integrations and information security much easier than before while providing this flexibility.

    We are witnessing the next wave of software: one that democratizes its use and allows all parts of a business to participate in making it work. As this happens, we will witness software segment convergence that brings many software categories together to engage customers more completely, and that allows enterprises to adapt and flex to a dynamic customer experience.

    Author: David T. Roberts

    Source: Informationweek

  • Forrester: Insights to help prepare your organization for 5G

    Forrester: Insights to help prepare your organization for 5G

    5G Presents immense innovation potential

    5G promises to usher in not just new use cases in every industry but also new business models.

    Some of the most relevant use cases across industries, such as those enabled by AR/VR and massive IoT, fit right into improving customer experience and digital transformation. As a change agent, 5G is among the most important technological enablers in this decade and the next. Therefore, investing and taking a deep look at 5G is critical at this time.

    5G Will develop rapidly through 2020 but is still developing nonetheless

    The 5G wireless arms race is fueled by the immense potential, so technology development is intense. Almost all current 5G announcements are regional siloed pilots and enhancements upon 4G LTA rather than actual 'at-scale' 5G standalone deployments. Manufacturers and operators have been aggressively pushing their 5G strategies. However, many challenges and uncertainties are still open: cost of network, monetization of use cases, regulatory challenges and, most importantly, the lack of mature standards.

    2018-19 Was a major leap in 5G standards, but beware the hype

    Through the 3GPP standards body, the industry had agreed to complete the non-standalone (NSA) implementation of 5G New Radio by December 2017, and this facilitated large-scale trials based on the specifications.

    Various sources cite numerous estimates about 5G. According to the International Telecommunications Union (ITU), commercial 5G networks are expected to start deployment after 2020. By 2025, the GSM Association (GSMA) expects 5G connections to reach 1.1 billion, which is estimated to be about 12 percent of total mobile connections. One Ericsson study estimates that 5G-enabled industry digitalization revenues for ICT players will be US$1.3 trillion in 2026. Still, current 5G reality is far from the profound expectations established by its proponents.

    Structuring your 5G thinking

    At Forrester, we have a deep bench of experts who are closely monitoring the developments and hype around 5G.

    Here is a simple framework:

    1. First, understand the background, technology, and the physical and business challenges behind practical implementations of 5G to cut through the hype. 
    1. There is a lot of talk about coverage in rural areas. In fact, bridging the digital divide is often touted to be a big plus of 5G. However, every early investment and the motivation behind it seem to suggest that at least until 5G achieves deployment scale, the digital divide may get worse. 
    1. Further, thoroughly assess your own 5G needs. Many current use cases probably do not need 5G. Hence, clearly understanding and nailing your use cases is an important vision to have. 
    1. Understand how 5G will transform your network operations, impact apps, and customer experience
    1. Finally, ask the right questions to your service provider on 5G timelines, cost, strategy, coverage, and implementation to understand what you can expect and to plan your investments in the coming months.

    Author: Abhijit Sunil

    Source: Forrester

  • Getting the most out of your data as a team

    Everyone wants to get more out of their data, but how exactly to do that can leave you scratching your head. Our BI Best Practices demystify the analytics world and empower you with actionable how-to guidance.

    Answering the big questions

    In the right hands, data is the ultimate means to answer important business questions. The problem is that when data is used incorrectly, it still provides answers (just bad ones). The best way to avoid decisions made based on that bad information is to improve the relationships between the employees analyzing data and the employees acting on that information. 

    Today, data questions often involve someone from a line-of-business team and someone from a data team. Both people bring their own individual expertise to the collaboration, but these projects can easily fall into the trap of unclear expectations and insufficient communication. 

    Before building a dashboard to answer questions, data experts need to sit down with their line-of-business counterparts and have a discussion about the purpose of that dashboard. Here are a few tips for data experts to get the most out of that meeting: 

    Go into every dashboard with an open mind

    It’s crucial to start every new data inquiry with a fresh mind. Every assumption the data experts make is an opportunity for the overall insight to lose value. On the other side, provided assumptions and business context can accelerate the data work for a faster time to value. The goal of every dashboard creator in a data request meeting should be to fully understand the data consumer’s needs and workflow. That means listening to their individual context and letting the investigation go where the data leads. If you drive the conversation or ask leading questions, you’ll end up at the resolution that you want, not necessarily the one that’s most valuable.

    Get to know the individual requesting the data

    Like most collaborative projects, empathy is critical to success. I like to start data request meetings by asking the data customer to walk me through their typical day. In some cases, it even helps me to shadow them for a while. What I’m looking for is a complete picture of the way that individual uses data. There might be pain points or missed opportunities for data to be used that I can help integrate. Asking someone about their typical day is also an easy way to get them communicating openly. It disarms them and puts the focus on the personal connection rather than a business problem.

    Understand the business value behind a data request

    An easy way for a data inquiry to get off track is for the dashboard creator to receive a request and cut straight to building a dashboard. Sometimes a request for a specific metric might miss the bigger question that data can solve. For example, a CS person might say  'give me churn' but what they mean is 'we need to find a way to minimize churn'. If you were to build a chart that simply listed churn over time, you’d miss all the other data points that correlate with churn. It’s often the data team’s job to connect data from different teams and maximize business value. Narrowing in on one team’s KPIs is an easy way to take your eye off the real goal. If you understand what is at the heart of the data request, it’ll be easier to work backward and find the right questions for the data to answer.

    Work in pairs if possible

    It’s true that two heads are generally better than one, but this concept is less about brainpower than it is about having one set of hands dedicated to note-taking. While a second data expert can definitely help understand the bigger questions in a meeting about data, it is invaluable to the flow of the conversation to have someone designated as a note-taker. Since the conversation moves at the speed of the slowest participant, it is extremely disruptive to have one person focusing on both moving the conversation forward and also documenting the important information for later. If a paired approach isn’t possible, it’s always an option to record the conversation, but I also recommend sketching out potential charts during these sessions, and that doesn’t translate well through audio.

    Start general, then get specific

    It’s likely that the initial dashboard request will be for a very pointed metric. This isn’t a bad way to start, but it’s the data expert’s job to guide the bring the focus to a bigger general question that this data can solve and uncover complementary data that can be included in that dashboard. Consider the example of churn from earlier. Presumably, the company’s goal is to maximize revenue and the inquiry into churn is being done to that end. It’s probably valuable to first create a variety of charts that illustrate the effect of churn on revenue. From there, it might also be useful to consider churn for individual cohorts, find factors that correlate in some way with churn or locate levers that will improve churn. Organizing the questions you ask from general to specific will also help you organize the charts on your dashboard when it’s time to create that asset.

    Build an unsorted list of questions to answer with data

    At the end of the meeting, the best thing to come away with is a list of questions that can be translated into data queries. An easy way to build this list is to open a document and just list the big questions that come up as you have exploratory conversations and try to understand the bigger business issue. If you can work as a pair, your note taker can do this part too. When the meeting is over (or maybe afterward if you recorded the conversation and want some time to go back and review notes), you can submit the list of questions to the dashboard requester and make sure that you have everything covered. Once that list is approved, you can start mapping each of the questions into a chart that will be organized and placed on your final dashboard.

    Think long instead of short

    Building a dashboard isn’t a straight line: there’s a lot of back and forth, questioning, editing, and iterating between the initial request and the finished product. These tips will help you build a strong foundation for this process and leave you in the best position for creating a dashboard that can really help your users make smarter decisions. Try embracing a collaborative process; you’ll be glad you took the time, asked the hard questions.

    Author: Christine Quan

    Source: Sisense

  • How to be a digital leader in a time of large-scale technology transformation

    How to be a digital leader in a time of large-scale technology transformation

    Following interviews with hundreds of industry executives, McKinsey recently shared fivecornerstones that are enabling organizations across every industry to integrate and capitalize on advanced technologies such as analytics, AI (artificial intelligence), machine learning, and the Internet of Things.

    These cornerstones of large-scale technology transformation include: 

    1. Developing technology road maps that strategically focus investments needed to reinvent legacy businesses and create new digital ones
    2. Training managers to recognize new opportunities and build in-house capabilities to deliver technologies
    3. Establishing a modern technology environment to support rapid development of new solutions
    4. Focusing relentlessly on capturing the strategic value from technology by driving rapid changes in the operating model
    5. Overhauling data strategy and governance to ensure data is reliable, accessible, and continuously enriched to make it more valuable 

    When it comes to the latter, McKinsey says that while every executive understands data will yield a competitive advantage (MicroStrategy’s Global State of Enterprise Analytics Report 2020 shows that 94% believe data and analytics are important to digital transformation and business growth), few have put in place the business practices to capitalize on it. For most, the data is messy and hard to access, and current technologies cannot scale to take advantage of a fast-growing wealth of data sources. 

    Constellation Research founder and Disrupting Digital Business author Ray Wang says this is what's driving the growing divide between digital leaders and laggards, one where digital leaders are taking 40-70% of market share in the future of data in digital transformation.

    'Digital leaders are folks that understand the impact of data', says Wang. 'They understand how to ask the right business questions. They understand that integration is important. They understand why data quality is needed. They understand why testing is so important before releasing something, because if you don't properly test, what you end up with is a lot of bad insights and next best actions which reduces the confidence in the data. They understand the human factors behind data and data design'.

    'They're always looking for new data sources and how to get those data sources to work', continues Wang. 'And they're always trying to figure out how to empower people with not just data, but the ability to make better decisions'.

    'Using data effectively in digital transformation is not easy. To make it work, you've got to have your data house in order. Build a foundation to support strong governance, data prep, streaming, and agility'.

    Author: Tricia Morris

    Source: Microstrategy

  • How to translate IIoT investments to ROI

    How to translate IIoT investments to ROI

    A digital transformation takes time, sometimes a considerable amount. This means it can be difficult to quantify ROI, at least in the short term. Return on investment for IIoT (Industrial Internet of Things) relies entirely on the data collected with the technology, and how it’s applied. The information itself may be incredibly valuable, but that won't matter if it's used ineffectively, further reducing the leverage.

    Real-time insights provide more of a direct influence on operations, offering minimal boons to a variety of business facets. That said, measuring real ROI is about the big picture and how all those smaller wins come together to provide a wholly effective strategy.

    It’s difficult to ascertain the ROI of IIoT and gauge whether or not you’re on the right track in the first place.

    Spending on IoT remains high for many industries, but the ROI is still up in the air. About 72% of construction business operators include new tech adoption as part of their strategic plan or vision for the future. Despite that, only 5% see themselves on the cutting edge of adoption. Here are some tips that can help you better plan industrial IoT adoption, while also getting the most out of the new technologies:

    Choose an objective

    Industrial IoT is an incredibly broad field that encompasses nearly every device, machine and process that exists today, and beyond. Just because the technology can be outfitted to work with every system in a facility doesn’t mean that’s what should happen.

    Before moving forward with any form of implementation, every organization should choose an objective for its IIoT campaign. What is the technology going to achieve? Should it be used to improve manufacturing efficiency? Will it help sync up workers across the plant floor? Is it better suited for fleet management and asset tracking?

    While it would be great to have multiple potential solutions in place, it would be nearly impossible to verify the ROI after doing so. By selecting a single objective and following through, data teams can adopt a more systematic approach that provides more accurate insights. In the end, it allows decision-makers to see firsthand whether IoT is a proper investment and worth pursuing on a larger scale.

    If nothing else, deploying IIoT with the intent to eliminate bottlenecks in existing processes is a great place to start.

    Go process by process

    With all the hype surrounding digitization and modern technologies, it's easy to get swept up in the tide. Overhauling every aspect of a business to honor advanced digital solutions may seem like a great idea, initially. The reality is that taking it all on at once is likely going to fail. For instance, switching to a paperless operation while simultaneously installing new IoT sensors on the factory floor will cause more confusion than positive support.

    As Harvard Business Reviews’ Digital Transformation of Business report states, merely spending more on cutting-edge technologies does not guarantee a positive outcome.

    The real winners will be the 'companies that both identify which core business capabilities they need to differentiate and make a commitment to transform these core business capabilities with the right digital technology'.

    Instead, take a look at the processes and systems currently in place and identify what will see the most significant boon from digitization. Choose one or two, and then get to work. Once the ball is rolling, it’s going to take time and resources to implement the proper solutions. New technologies will need to be installed, which means old equipment and tools might need to be phased out or upgraded. Employees will need training, and they may also need their own set of improved tools. Leadership will need to come up with new strategies for working with upgraded systems>, communicating with their workers and taking action.

    It’s a long, demanding process. Not something that happens overnight. That’s precisely why it’s best to take it one step at a time and focus on a single process or solution. Once a particular department or task is honed, then it’s time to move on to other digitization projects within the company.

    Choose a reliable vendor

    With new technologies it’s best to work with a vendor or specialist that already has considerable experience. Yes, it’s possible to develop an in-house IoT solution that’s also managed by a proprietary IT crew. It’s also a lot more costly and more likely that problems will arise as a result.

    Third-party vendors have more resources at their disposal merely because it’s what they do, exclusively. They tend to have more robust IT and security solutions, along with the appropriate human resources to keep everything safe. They can handle installation, upgrades and repairs, which takes the responsibility away from the leading organization. They also provide comprehensive support for when problems or questions do arise.

    Implement predictive operations with IIoT

    Predictive maintenance is something relatively new in the industrial field, made possible thanks to IIoT and the real-time insights it can deliver. Data can reveal hidden details about working machinery, output, potential errors and more. Collectively, it provides a detailed report about performance, allowing decision-makers to pinpoint what areas of the operation are lacking. They can take action, sooner rather than later, to correct any issues and replace ailing equipment.

    It’s a process that should be deployed across the entire operation instead of solely for maintenance. It can be used for a lot more than just predicting when equipment is going to fail, too. Employing machine learning and analytics applications can reveal when and how supplies are going to thin out, demand trends, and much more. Another term for this is business intelligence. Predictive operations throug big data analysis are one facet of business intelligence, albeit an incredibly lucrative one.

    Invest in IoT for predictive operations and ROI will innately improve.

    Improving ROI even before it can be measured

    These tips offer just a few ways that organizations can improve the ROI of IIoT implementation, even through preplanning. It may be difficult to quantify the real value of the technology upfront. Nonetheless, honoring these processes can help realize the bigger picture, which is something business leaders always demand.

    Author: Megan Nichols

    Source: Datafloq

  • Integrating security, compliance, and session management when deploying AI systems

    Integrating security, compliance, and session management when deploying AI systems

    As enterprises adopt AI (artificial intelligence), they'll need a sound deployment framework that enables security, compliance, and session management.

    As accessible as the various dimensions of AI are to today's enterprise, one simple fact remains: embedding scalable AI systems into core business processes in production depends on a coherent deployment framework. Without it, AI's potential automation and acceleration benefits almost certainly become liabilities, or will never be fully realized.

    This framework functions as a guardrail for protecting and managing AI systems, enabling their interoperability with existing IT resources. It's the means by which AI implementations with intelligent bots interact with one another for mission-critical processes.

    With this method, bots are analogous to railway cars transporting data between sources and systems. The framework is akin to the tracks the cars operate on, helping the bots to function consistently and dependably. It delivers three core functions:

    • Security
    • Compliance and data governance
    • Session management

    With this framework, AI becomes as dependable as any other well-managed IT resource. The three core functions each need to be supported as follows.

    Security

    A coherent AI framework primarily solidifies a secure environment for applied AI. AI is a collection of various cognitive computing technologies: machine learning, natural language processing (NLP), etc. Applied AI is the application of those technologies to fundamental business processes and organizational data. Therefore, it's imperative for organizations to tailor their AI frameworks to their particular security needs in accordance with measures such as encryption or tokenization.

    When AI is subjected to these security protocols the same way employees or other systems are, there can be secure communication between the framework and external resources. For example, organizations can access optical character recognition (OCR) algorithms through AWS or cognitive computing options from IBM's Watson while safeguarding their AI systems.

    Compliance (and data governance)

    In much the same way organizations personalize their AI frameworks for security, they can also customize them for the various dimensions of regulatory compliance and data governance. Of cardinal importance is the treatment of confidential, personally identifiable information (PII), particularly with the passage of GDPR and other privacy regulations.

    For example, when leveraging NLP it may be necessary to communicate with external NLP engines. The inclusion of PII in such exchanges is inevitable, especially when dealing with customer data. However, the AI framework can be adjusted so that when PII is detected, it's automatically compressed, mapped, and rendered anonymous so bots deliver this information only according to compliance policies. It also ensures users can access external resources in accordance with governance and security policies.

    Session management

    The session management capabilities of coherent AI frameworks are invaluable for preserving the context between bots for stateful relevance of underlying AI systems. The framework ensures communication between bots is pertinent to their specific functions in workflows.

    Similar to how DNA is passed along, bots can contextualize the data they disseminate to each other. For example, a general-inquiry bot may answer users' questions about various aspects of a job. However, once someone applies for the position, that bot must understand the context of the application data and pass it along to an HR bot. The framework provides this session management for the duration of the data's journey within the AI systems.

    Key benefits

    The outputs of the security, compliance, and session management functions respectively enable three valuable benefits:

    No rogue bots: AI systems won't go rogue thanks to the framework's security. The framework ingrains security within AI systems, extending the same benefits for data privacy. This can help you comply with today's strict regulations in countries such as Germany and India about where data is stored, particularly data accessed through the cloud. The framework prevents data from being stored or used in ways contrary to security and governance policies, so AI can safely use the most crucial system resources.

    New services: The compliance function makes it easy to add new services external to the enterprise. Revisiting the train analogy, a new service is like a new car on the track. The framework incorporates it within the existing infrastructure without untimely delays so firms can quickly access the cloud for any necessary services to assist AI systems.

    Critical analytics: Finally, the session management function issues real-time information about system performance, which is important when leveraging multiple AI systems. It enables organizations to define metrics relevant to their use cases, identify anomalies, and increase efficiency via a machine-learning feedback loop with predictions for optimizing workflows.

    Necessary advancements

    Organizations that develop and deploy AI-driven business applications that can think, act, and complete processes autonomously without human intervention will need a sound deployment framework. Delivering a road map for what data is processed as well as how, where, and why, the framework aligns AI with an organization's core values and is vital to scaling these technologies for mission-critical applications. It's the foundation for AI's transformative potentialand, more important, its enduring value to the enterprise.

    Source: Ramesh Mahalingam

    Author: TDWI

  • Pyramid Analytics: Main lessons learned from the data-driven drilling and production conference

    Pyramid Analytics: Main lessons learned from the data-driven drilling and production conference

    It was great to be at the data-driven drilling and production conference in Houston on June 11 and 12. The conference was well attended by hundreds of oil and gas (O&G) professionals looking to use technology to minimize downtime, enhance safety, and deliver digital transformation throughout their businesses.

    We talked to dozens of attendees looking to educate themselves about modern data collection and ingestion methods, better information management and integration processes, E&P automation & control systems, more efficient change management, and drilling optimization techniques, and advanced and predictive analytics.

    As an analytics and BI vendor, we were there to learn more about how practitioners are using advanced analytics, particularly AI and machine learning, to extract more value out of their data.

    Three key themes

    In our conversations with attendees and other vendors, three key themes emerged:

    • The persistence of data silos

      No surprise here: data silos aren’t going anywhere. The upstream organizations we spoke to struggle with data sharing across departments. It’s a common scenario for users to have limited access to distributed data. It is also common for upstream organizations to perform analytics using numerous tools (many of the individuals we spoke to freely admitted to using three or four different BI tools). This perpetuates the cliché: there is no single version of the truth. The result is duplicate data, duplicate efforts for reporting, duplicate logic and business rules, and more. As a result, collaboration and efficiency suffer.
    • AI and ML operationalization remain elusive

      Many of the professionals we talked to lack effective systems for putting advanced analytics into production. Here’s a common scenario. A line-of-business user will throw data scientists a set of data and say, 'here’s the data, do your magic'. The data isn’t always optimized, so data scientists often spend time prepping the data before they can even analyze it. Then they analyze the data using standalone ML software applications before outputting a flat file and sending it to a business analyst to reload into one of several desktop-based BI applications. This results in a perpetual cycle of extracting, importing, analyzing, exporting, re-importing, and re-analyzing data. The whole process is cumbersome and inefficient; meaningful insights derived from AI and ML initiatives remain limited.

    • It’s hard to move beyond legacy analytics systems 

      For many O&G companies, there is a strong desire to adopt new data and analytics technologies; they acknowledge legacy tools simply aren’t equipped to quickly accommodate newer sources of data and perform advanced and prescriptive analytics. However, the difficulty of migrating from legacy systems often holds some people back, no matter how siloed their data environment is. Many organizations have had their current desktop-based analytics solutions in place for years, and in some cases decades. However, the huge store of analytic models, dashboards, and reports they have created over the years cannot be easily migrated or re-created. 

    The three challenges identified above are tough. But that doesn’t make trying to solve them any less urgent. And from our perspective, this doesn’t make them any less solvable. The price of inaction is too high. No one can stand on the sidelines while the technology environment changes.

    Author: Brigette Casillas

    Source: Pyramid Analytics

  • Some expert advice on gaining organizational trust

    Some expert advice on gaining organizational trust

    Take a moment and ask yourself, what is your definition of trust and how do you know when you are trusted?

    Did the answers come quickly, or not? If you don’t have a ready definition, don’t worry, most people don’t. It’s just one of those things that we have an impression about. We know it when we feel it. Here’s the bad news. It’s hard to know when you are trusted and even harder to know how to build it. And on average, we need to earn a lot more trust than what we currently have. The good news? It’s possible to earn trust with the right plan.

    What’s that you ask? Doesn’t ‘planning for trust’ sound Machiavellian? I can imagine that it must, but here’s more good news. The plan not only can help you build trust, but it can also help you make a few friends along the way. The same tactics you use to build trust, are some of the same you might use to build relationships and gain friends. The problem is that we leave too much to chance and we don’t always know what works. Don’t do that. Instead, do this:

    1. Understand the context in which you want to earn trust. If you are a CIO working on getting a seat at the table or build out a digital transformation plan, that context is far different than the context of traditional IT and how you may have built your brand so far.
    2. Recognize that trust is developed based on your expertise and knowledge within that context.
    3. Acknowledge that trust is also developed based on the rapport and relationships you build accordingly.

    Trust is contextual

    The determination of trustworthiness happens within a particular context. A pediatrician is someone trusted to treat a sick child, but not to manage a problematic investment portfolio. A CIO and their team may have built trust in the context of building and running robust and predictable back-office IT systems, but they may be untrusted to create innovative and flexible solutions for end consumers. Paradoxically, past success is not an indication of future success. Studies have shown that people who have been successful in the past are actually more likely to perform worse in a new context. They can become overconfident and less open to feedback.

    Trust is a measure of expertise and rapport

    CIOs may try to develop trust by first demonstrating expertise in all things digital. However, unlike consultants or contractors, who are external parties and can rely on expertise alone (part of an open social system), the CIO must rely on expertise plus rapport (part of a closed social system), but rapport leads the way. Focusing on expertise can diminish perceptions of warmth. Instead of coming across as an expert, CIOs risk coming across as overconfident or arrogant. Building rapport requires empathy, listening, curiosity, and genuine interest. Compare that to how one demonstrates expertise using logic, opinions, and arguments. It is wiser to have built some rapport before asserting opinions and arguments.

    Author: Ed Gabrys

    Source: Gartner

  • The 5 dimensions that help your business with a successful technological transformation

    The 5 dimensions that help your business with a successful technological transformation

    Businesses that have mastered the ability to change quickly share one common denominator: technology is transforming their business. Technology can be a transformative engine that gives your organization the power to learn, adapt and respond at the pace of change.

    Today’s IT leaders have many tools to enable speed and flexibility, including Lean IT, Agile, DevOps and Cloud First among others. However, these concepts alone rarely deliver the technology transformation that organizations need because companies are tempted to think of transformation as a predominately organizational journey. Organizations need to think much more holistically in order to lead a technology transformation and enable a flexible and efficient business.

    There are five essential components, the 5 dimensions, that can lead to a successful technology transformation. Each dimension allows you to learn something unique about your organization, somewhat similar to an archeologist digging through an archeological tell. The 5 dimensions can be used to drive a holistic technology transformation that fits your historical and cultural context.

    Here's a brief look at the 5 dimensions and how they can serve you:

    1. Business alignment 

    Far too many organizations build their technology strategies by aligning with the tactics of their business operations. The result is strategic dissonance, as IT resources are not correctly prioritized to meet strategic business priorities. This misalignment leads to new architectural debt. Today's tech leaders need to understand the organization's business model and build a technology strategy that unlocks and empowers that model, ensuring alignment along the way.

    2. Architectural debt 

    Most organizations suffer from technical debt: systems built for expediency instead of best practices. Architectural debt, on the other hand, is the systemic root cause in the creation of technical debt. A recent survey by IDG and Insight Enterprises found that 64% of executives cited legacy infrastructure and processes as a barrier to IT and digital transformation. ‘Legacy infrastructure and processes’ is just another way of describing architectural debt. Debt is an important concept for technology organizations because it constrains flexibility and results in an IT organization managed by the inertia of their systems. If you want to lead an IT or digital transformation, you must quantify your architectural debt and pay down (minimize) or pay off (eliminate) that debt in order for your transformation to be both successful and sustainable.

    3. Operational maturity 

    IT organizations exist on a spectrum of maturity, classified into three distinct phases: operators, automators, and innovators. Operational maturity is a critical enabler of an organization’s ability to execute their vision or goals. There is a high correlation between business value and operational maturity. Mature IT organizations are focused on high quality, business value-added activities. An IT organization’s capabilities directly correlate with its phase of maturity along our spectrum. You must look at the people, processes, technologies and artifacts to understand where change must occur in order to increase operational maturity.

    4. Data maturity

    Clive Humby, U.K. mathematician and architect of Tesco's clubcard, famously said in 2006 that 'Data is the new oil… It’s valuable, but if unrefined it cannot really be used'. Nearly a decade later, The Economist called data the world’s most valuable resource. Many organizations are sitting on mountains of unrefined data, uncertain how they should be storing, processing or utilizing that valuable resource. Top-performing organizations that are using data to drive their business and technology decisions have a distinct competitive advantagetoday and tomorrow.

    5. Organizational dexterity 

    Your organization’s capacity for innovation and change directly correlates with its dexterity. To quote Peter Drucker: 'In times of turbulence, the biggest danger is to act with yesterday’s logic'. Organizations falter when they have institutionalized a culture of yesterday’s logic. An agile organization isn’t just a decentralized organization, it’s an organization that has the capability to learn and unlearn, demonstrates complex problem solving, emotional intelligence and much more.

    We live and work in turbulent times, with more volatility on the horizon. Is your technology ready? How about your organization? The 5 dimensions play a critical role in building a holistic understanding of your organization. Seeing the whole picture enables you to build a pragmatic path forward that leads to a true technology transformation.

    Author: Alex Shegda

    Source: Information-management

  • The future of cybersecurity threatened by the emergence of IoT devices

    Imagine being able to communicate effortlessly with the devices around you. This means having your devices fully automated and connected by sharing data through the use of sensors. This will definitely improve the quality of life and make our day to day activities much easier. This will also make businesses more efficient and facilitate in driving new business models.

    Well, there is no need to imagine as this is already a reality. These are the wonders of the innovation brought about by the Internet of Things (IoT), which simply refers to the network of devices, such as vehicles and home appliances, that contain electronics, software, sensors, actuators and connectivity that allows them to connect, interact and exchange data. The emergence of IoT brings about numerous benefits, but also poses a huge threat to security as it creates new opportunities for all the information it gathers to be compromised.

    Cybersecurity is already at the top of the agenda for many industries, but the scale and scope of IoT deployments escalate security, making it harder than ever to protect businesses and consumers from cyber attacks. intelligent organizations already need to protect their data and information, but cybersecurity is growing more important than ever with the emergence of IoT devices. Although IoT developments have made life easier on so many levels, it has also brought about serious security implications, as the scale of connected devices greatly increases the overall complexity of cybersecurity, while the scope of the IoT which isn’t operating as an independent device but an ecosystem magnifies these challenges — any data breach can cause significant damage to a whole business database.

    As HP found out, 70% of the Internet of Things devices are vulnerable to external attacks. With the technical vulnerability of most of these devices, it can only escalate these threats. Also, with its constant evolution and little attention to security, the potential for damaging cyber attacks can only tend to increase in the future. The implementation of IoT networks opens up the grid to malicious cyber attacks and any form of compromise in the network could lead to great data leakage.

    8 IoT threats to cybersecurity in the future

    8 IoT threats to cybersecurity in the future

    1. Complexity

    Variation of devices connected to a network is accompanied by risks worsening cybersecurity worries with its diverse and wide ecosystem.

    2. Volume of Data

    With IoT’s great need of data to work, it opens up nearly every part of our lives to the Internet, posing an important threat to the possibility of data manipulation. As a result, we must consider what this kind of access to the Internet means for your digital and personal security, as the availability of numerous access points leads directly to an increase in the risk of a breach or hack.Unified attacks can bring down a system or a network of data that is relied upon by millions. IoT is an incredible idea with the potential to change our lives dramatically but brings with it a flurry of concerns that will stretch your abilities and require you to be on your toes at all times.

    3. Continuous Expansion

    The IoT evolution doesn’t seem like slowing down anytime soon and, in fact, it continues to evolve and expand rapidly. This makes it difficult for cybersecurity to keep up with the pace.

    4. Over-Dependence On the Cloud

    With the cloud infrastructure, IoT has a heavy reliance on the cloud for safety, which makes cyber attacks to be targeted to the cloud. With this knowledge, it’s important to look for more ways to reduce those threats. More monitoring will be highly needed for cloud configuration, as well as logging. This monitoring can also be done with the use of external tools — These includes antivurus softwares and VPNs needed to be reviewed and compared carefully. These reviews and comparisons will enable you to choose the tool best suited for your device and needs, while the use of these tools will go a long way in securing your internet connections.

    5. Privacy Issues

    The issue of privacy is generated by the collection of personal data in addition to the lack of proper protection of the data.

    6. Deficiency In Authentication

    This area deals with ineffective mechanisms being in place to authenticate to the IoT user interface and/or poor authorization mechanisms whereby a user can gain a higher level of access than allowed with regard to their weak authentication mechanisms. For example, there is usually a large amount of data that is not sufficiently encrypted and these data are transmitted via wireless networks, many of which are public and lacking in security.

    7. Insecurity

    Over the past two years,AT&T’s Security Operations Center has logged a 458% increase in vulnerability scans of IoT devices. The risk with this is that the IoT device could be easier to attack, allowing unauthorized access to the device or its data. Most IoT manufacturers concentrate more on the efficiency of the device and less on the security, making devices vulnerable to cyberattacks. It is also difficutl to secure these devices after they become an end product, which only increases the challenges of cybersecurity.

    8. Industrial IoT

    According to Forcepoint, in 2019 attackers will break into industrial IoT devices by attacking the underlying cloud infrastructures. This target is more desirable for an attacker, as access to the underlying systems of these multi-tenanted, multi-customer environments represents a much bigger payday.<

    What does the future hold?

    Due to the aforementioned IoT-related weaknesses, which give cybercriminals more access to manipulate connected devices, it’s clear that IoT is painting a scary future for cybersecurity. However, it’s noteworthy that no system can ever be perfect. A continuous effort has to be put into work in order to provide more effective cybersecurity measures to ensure more safety in our day-to-day use of the IoT devices around us.

    Author: Joseph Chuckwube

    Source: SAP

  • The risk of undervaluing creativity

    The risk of undervaluing creativity

    Agencies’ creative perspective, the very currency of the business, is at risk and can only be realized by shifting billions from tech to fund creative differentiation.

    “The value of agency creativity is at risk of disappearing”

    The marketing industry is woefully out of balance, from agency/client relationships to new business requirements and compensation. The healthy tension of creativity that once balanced the needs of the brand with the needs of its customers, the commercial effectiveness of the work versus its cultural impact, and the needs of agency economics versus the client’s growth is all eroding. These are now one-sided issues. The tension is no longer healthy. Nowhere is this more evident than in agency economics. Agencies today barely grow at the current rate of inflation. Insourcing, margin compression, cost-cutting, new competitors, and tech priorities threaten the existence of agencies and undermine their value.

    “Customer experience has stagnated”

    Strong evidence of creativity’s languish is already underway. Customer experience has stagnated. Forrester’s Customer Experience Index (CX Index™), a study of 100,000 consumers and 300 brands that has been run for more than a decade and acts as a barometer for CX performance, is flat for the fourth consecutive year. Most brands are stuck in the middle, struggling to improve over competitors. Zero brands are rated to have an excellent experience. Forrester determined that there are four types of CX performance: the Languishers, Lapsers, Locksteppers, and Laggards. No brand is performing well. Worse still, for every 1-point drop in CX Index score, companies lose 2% of their returns. It’s only a matter of time before companies’ growth is impacted.

    “We’ve commoditized the brand and homogenized experiences”

    The issue is that the work looks, feels, and behaves too similar. The industry obsession for meeting every customer need and want for ease and convenience by using technology has left little room for creative differentiation. That has come at a cost. The front door to your brand is a web or app experience that is virtually indistinguishable. Fashion experiences look the same. Quick-service restaurant and coffee apps allow you to order ahead and skip the line. All airline apps allow travelers to check in, manage travel, and use a mobile device as their boarding pass. What can make one brand different from another when the experience is built from the same common technology platform, designed to solve the same user or category need, and programmed for the same two devices? Creativity.

    “We’ve overfunded technology and underfunded creativity”

    Unfortunately, just when creativity is needed the most, business leaders are investing in it the least. Forrester forecasts that spending for adtech, martech, data, and analytics will grow between 9% and 11% through 2022. Agency spending will only grow a mere 2.4%. And client budgeting and priorities are only part of the problem. Agencies are underfunding creativity, too. As of 2014, agencies had spent $12 billion-plus for data and technology resources and acquisitions. While the agency data platforms do power media and audience activation, all but one integrates with the creative process. And creative departments remain skeptical and dismissive.

    “It’s time to fund creative differentiation”

    Forrester developed an ROI for creative agency investment that determined that moving a portion of the marketing budget out of technology and into agency creativity will bring a higher return on investment compared to currently projected spending levels. This serves as a six-year growth plan for CMOs that ultimately helps achieve 20% growth for the entire industry. These are not new dollars but rather a reallocation of currently projected spending that maintains significant adtech and martech investments.

    “It’s time to reinvent creativity”

    To deliver clients the growth they need and customers the experiences they demand, agencies must innovate their structures, capabilities, workforce, and process. Structurally, data, technology, media, and creative should all come together and put creative problem-solving at the center. This means the newly acquired data, tech, and operating agencies should also come together. And especially, it means agencies leaders will need to make consolidation and coordination a priority. Tough decisions must be made in the name of agency brand coherence and a model that is easier for clients to engage. Training today’s workforce to be tomorrow’s data-, technology-, and creative-literate is critical. And creative departments must embrace data- and tech-driven creativity.

    We’re living during one of the most interesting times in the history of the industry, with the opportunity to shape and define it. A whole new era of amazing marketing is only possible if we fund the balance of creativity and technology. Take up the mantle to modernize the industry. Reinvent the creative process.

    Author: Jay Pattisall

    Source: Forrester

  • Using the right workforce options to develop AI with the help of data

    Using the right workforce options to develop AI with the help of data

    While it may seem like artificial intelligence (AI) has hit the jackpot, a lot of work needs to be done before its potential can really come to life. In our modern take on the 20th century space race, AI developers are hard at work on the next big breakthrough that will solve a problem and establish their expertise in the market. It takes a lot of hard work for innovators to deliver on their vision for AI, and it’s the data that serves as the lifeblood for advancement.  

    One of the biggest challenges AI developers face today is to process all the data that feeds into machine learning systems, a process that requires a reliable workforce with relevant domain expertise and high standards for quality. To address these obstacles and get ahead, many innovators are taking a page from the enterprise playbook: where alternative workforce models can provide a competitive edge in a crowded market. 

    Alternative workforce options

    Deloitte’s 2018 Global Human Capital Trends study found that only 42% of organizations surveyed said their workforce is made up of traditional salaried employees. Employers expect their dependence on contract, freelance and gig workers to dramatically increase over the next few years. Acceleratingthis trend is the pressure business leaders face to improve their workforce ecosystem as alternative workforce options bring the possibility for companies to advance services, move faster and leverage new skills. 

    While AI developers might be tempted to tap into new workforce solutions, identifying the right approach for their unique needs demands careful consideration. Here’s an overview of common workforce options and considerations for companies to select the right strategy for cleaning and structuring the messy, raw data that holds the potential to add rocket fuel to your AI efforts:

    • In-house employees: The first line of defense for most companies, internal teams can typically manage data needs with reasonably good quality. However, these processes often grow more difficult and costlier to manage as things progress, calling for a change of plans when it’s time to scale. That’s when companies are likely to turn to alternative workforce options to help structure data for AI development.
    • Contractors and freelancers: This is a common alternative to in-house teams, but business leaders will want to factor in extra time it will take to source and manage their freelance team. One-third of Deloitte’s survey respondents said their human resources (HR) departments are not involved in sourcing (39%) or hiring (35%) decisions for contract employees, which 'suggests that these workers are not subject to the cultural, skills, and other forms of assessments used for full-time employees'. That can be a problem when it comes to ensuring quality work, so companies should allocate additional time for sourcing, training and management.
    • Crowdsourcing: Crowdsourcing leverages the cloud to send data tasks to a large number of people at once. Quality is established using consensus, which means several people complete the same task. The answer provided by the majority of the workers is chosen as correct. Crowd workers are paid based on the number of tasks they complete on the platform provided by the workforce vendor, so it can take more time to process data outputs than it would with an in-house team. This can make crowdsourcing a less viable option for companies that are looking to scale quickly, particularly if their work requires a high level of quality, as with data that provides the intelligence for a self-driving car, for example.
    • Managed cloud workers: A solution that has emerged over the last decade, combining the quality of a trained, in-house team with the scalability of the crowd. It’s ideally suited for data work because dedicated teams develop expertise in a company’s business rules over time by sticking with projects for a longer period of time. That means they can increase their context and domain knowledge while providing consistently high data quality. However, teams need to be managed in ways that optimize productivity and engagement, and that takes something. Companies should look for partners with tested procedures for communication and process.

    Getting down to business

    From founders and data scientists to product owners and engineers, AI developers are fighting an uphill battle. They need all the support they can get, and that includes a dedicated team to process the data that serves as the lifeblood of AI and machine learning systems. When you combine the training and management challenges that AI developers face, workforce choices might just be the factor that determines success. With the right workforce strategy, companies will have the flexibility to respond to changes in market conditions, product development and business requirements.

    As with the space race, the pursuit AI in the real world holds untold promise, but victory won’t come easy. Progress is hard-won, and innovators who identify strong workforce partners will have the tools and talent they need to test their models, fail faster and ultimately get it right quicker. Companies that make this process a priority now can ensure they’re in the best position to break away from the competition as the AI race continues.

    Author: Mark Sears

    Source: Dataconomy

  • What to expect for data governance in 2020?

    What to expect for data governance in 2020?

    Data governance always has been a complicated issue for most organizations. That won’t change in a big way in 2020. In fact, the increasing prevalence of technologies like artificial intelligence (AI) and machine learning (ML) may show up some of the pains even more. Don’t take that to mean that companies aren’t becoming more mature in their approach to Data Governance, though.

    AI, ML, the Internet of Things (IoT), and full process digitization will be a focus for organizations in 2020. Companies see them as required capabilities in the future and so are willing to invest in more digital innovation. 'This is expanding the governance lens and I’m seeing AI Governance becoming a reality in leading organizations', said Kelle O’Neal, founder and CEO of First San Francisco Partners. This trend shows that companies are seeing value in Data Governance so they’re extending successful practices into other areas of their business, she said.

    Organizations are realizing that AI is only successful when built upon a solid data foundation, thus driving the need for data governance, agreed Donna Burbank, managing director at Global Data Strategy:

    'I’ve had venture capital organizations approach us to train their AI startups in the foundations of data governance as a condition for investment', she said. 'I see that as an extremely positive sign pointing to the widespread need and adoption of data governance principles'.

    And yet poor data quality resulting from problems with data governance bedevils AI and ML outcomes and there’s no sign that that won’t be the case next year too.

    'Artificial intelligence and machine learning have been way oversold. Data quality gets in the way of getting good results and organizations spend way, way more time cleaning things up', said Thomas C. Redman, Ph.D., 'the Data Doc' and President of Data Quality Solutions. He estimates that more than 80% of AI and ML programs continue to fail because of this.

    Governance defined …Yet?

    One question that many companies will continue to grapple with in the new year is figuring out just what data governance is. In simple terms, said Redman, it’s a senior oversight function whose leaders advise the board or senior management about whether a data-related program is designed in the best interest of the company and is operating as designed. And as he sees it, no one is doing that yet.

    'There’s all talk about data as the most important asset, but having that oversight level would be essential if that statement were correct', he said. It’s not about plugging in various tools but about thinking of just what data governance is … and what it isn’t:

    'The term ‘governance’ is being used for everything from moving data from here to there to something about how you operate analytics. That’s not the proper use of the term'.

    Getting roles and responsibilities right is critical, he said. Data governance should be business-led and IT supported, Burbank remarked: 

    'All areas of the business need to have accountability for the data in their domain and establishing data stewardship roles is critical to ensuring accountability at all levels of the organization from strategic to tactical'.

    Chief Data Officer (CDO) roles are becoming more common, and the office of the CDO does best when it reports up through a business function like operations, strategy, or shared services, said O’Neal, or even finance if that team is influential in driving enterprise programs that result in corporate growth.

    Organizations that have matured their data governance practices will grow from a program culture to a data culture, which is one:

    'Where new employees start learning about data governance as part of their new-hire training, and data governance and management are part of the conversation at the board level', said O’Neal.

    What will data governance look like in 2020?

    It’s true that there haven’t been drastic changes in how far we’ve come with data governance over the past year, but O’Neal finds that companies are showing progress:

    'More and more companies are moving from ‘what is data governance and why should I do it,’ past creating a strategy, into not just implementation but also operationalization, where their data governance is really embedded with other project, decision-making, and ‘business as usual’ operations', she said.

    In terms of a formal, structured approach, the DAMA DMBoK is gaining wide acceptance, which is a positive step in aligning best practices, Burbank said:

    'While data governance is certainly not a ‘cookie cutter’ approach that can be simply taken from a book, the DMBOK does offer a good foundation on which organizations can build and customize to align with their own unique organizational needs and culture'.

    In 2019, Global Data Strategy supported data governance for a diverse array of sectors, including social services, education, manufacturing, insurance, building, and construction. 'It’s no longer just the traditional sectors like finance who understand the value of data', she said.

    Big value in small wins

    It’s really hard to impose Data Governance frameworks on big data at enterprise scale. It is better to start with small data first and Redman is optimistic that more companies will do so in 2020.

    'Practically everyone sees the logic in small data projects', he said. 'Suppose that only half of a hundred small data projects succeed, that’s a huge number of wins', with positive implications for cost savings and improvements in areas like customer service. And solving more of these leads to learning about what it takes to solve big data problems. 'If you build the organizational muscle you need doing small data projects you can tackle big data projects'.

    Following the classic rule of thinking big and starting small in order to have the proper data governance framework and foundation in place is what works, Burbank said. Establishing small 'quick wins' shows continual value across the organization.

    Tools to help

    2018 saw astounding growth in the data catalog market, O’Neal said. Data catalogs provide information about each piece of data, such as location of entities and data lineage. So, if you haven’t thought about that yet, it’s time to do that this year, she said.

    The good news is that the modern tools for Metadata Management and data cataloguing are much more user-friendly and approachable, according to Burbank:

    'Which is a great advancement for giving business users self-service capability and accountability for metadata and governance'.

    Redman noted that 'you can love your data governance tools, and I do too. But if you approach the problem wrong it doesn’t matter what tools you have'.

    What’s up next

    In 2020, the organizations that are able to get their own data governance in order will reach out to others in the industry to establish cross-organization data governance and data sharing agreements:

    'For example, organizations in the social services or medical arena are looking to provide cohesive support for individuals across organizations that provide the best level of service, while at the same time protecting privacy', Burbank said. 'It’s an interesting challenge, and an area of growth and opportunity in the data governance space'.

    There’s an opportunity this year for companies that are moderately mature in data governance to think about how to embed practices in business processes and decision-making structure of the organization. Places to look for embedment opportunities, O’Neal commented, are new project initiation and project management, investment approval and funding, customer creation and on-boarding, product development and launch, and vendor management/procurement.

    Expect data analytics and BI to continue to be large drivers for data governance:

    'As more organizations want to become data-driven', Burbank said, 'they are realizing that the dashboards used to drive business decision-making must be well-governed and well-understood with full data lineage, metadata definitions, and so on'.

    Author: Jennifer Zaino

    Source: Dataversity

  • Why agile learning is essential to your business

    Why agile learning is essential to your business

    The digital deadlock is affecting many organizations today, big and small, and across all industries. Vast amounts of technology investments are being poured into the engines of aggressive digital strategies, but are delivering little in the way of progress. In fact, many are 'stuck in their journey'. What’s holding them back?

    IDC has looked into this very closely in the past few years and has found that the ´digital skills gap´ (when demand for IT skills is not met with adequate supply) is a top obstacle for those organizations in their digital agenda. Interestingly, the challenge is not only in recruitment, but most crucially in bringing up to speed the current workforce with the new skills. Employees are not learning fast enough.

    Our surveys show that the impact of the skills gap is broadly felt across the organization, from quality performance to customer satisfaction to business revenue growth. In fact, IDC estimates it will affect 90% of all European companies, resulting in $91 billion in lost revenue in 2020.

    The skills gap is now a board-level issue, and employers are determined to tackle the problem themselves by reskilling their own workforce. If colleages and professional schools are not providing an adequate supply of IT professionals, corporate training programs and internal mobility could fill the gap.

    This is a significant shift by employers in their training practices and policies. After a decade of austerity following the global financial crisis in 2008, they have now realized that learning means business.

    Is the workforce ready for the jobs of the future? Welcome to agile learning

    IDC believes agile learning is the way forward for any digital organization because it aligns skills and required training with business value and strategy. It is permanently evolving, keeping pace with new market needs and technology developments.

    From content, format, to channels of delivery, agile learning is business relevant while driving superior employee experience.

    Agile learning has the following common traits:

    1. Employee focused: Training needs to be applied to the task and woven into the flow of work (easily digestible). It ultimately has to help employees “get the job done” and achieve better performance (impactful). This could include the consumption of bite-size content (even in minutes), any time and by multiple channels, to fit work demands.
    2. Business relevant: Training cannot be decided unilaterally by the employee, manager, or HR. It has to be a cross-functional effort to ensure that career development goals and training needs are aligned with business requirements: the right materials, to the right employees, at the right time.
    3. AI/ML enabled: Training can be enhanced by intelligent technologies in multiple ways. AI/ML can help employees by providing career pathway recommendations; for employers, it can identify training that addresses the skills gap. In the not-so-distant future, intelligent technologies will be able to measure the impact of training on performance and business outcomes, helping to make it business relevant.

    Agile learning will be ingrained in our work culture moving forward, helping us to become more competent in our jobs (upskilling) or even to move into new jobs (reskilling). It can also prepare us for the new jobs of the future, those that have not even be created yet. In this respect, IDC expects micro-degrees to become increasingly popular.

    Micro-degrees can be useful to equip employees, reasonably quickly, for new jobs such as a flying car developer or an algorithm bias auditor. Developed in partnership with academia, industry, and employers, micro-degrees complement lectures with on-the-job training.

    Agile learning affects us all. As the retirement age rises, we should be able to expect significant mobility throughout our careers. Agile learning will be part of a lifelong learning work culture, mandated by the C-suite and instilled into the organization.

    To quote the World Economic Forum’s Future of Jobs Report 2018, 'By 2025, 75 million current jobs will be displaced by the shift in the division of labour between humans, machines and algorithms, but 133 million new jobs will be created as well'.

    Your current job might be one of those 75 million. Act now to enjoy the Future of Work with the other 133 million.

    Author: Angela Salmeron

    Source: IDC UK

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