4 items tagged "data strategy"

  • 3 Reasons to implement a data strategy in your sales processes

    3 Reasons to implement a data strategy in your sales processes

    Sales managers are resilient folk. For many, adapting to leaps in technology, economic volatility, and radical shifts in buying behavior is the norm. Often they emerge stronger and better equipped to succeed. Not surprisingly then, this Covid era, with any number of unforeseen business challenges has prompted many sales managers to examine themselves and their teams and to commit to up their game. One given in this tumultuous time is a data strategy.

    1. Must have a data strategy for sales 

    The veil of comfort of a pre-Covid world, where growth is infinite, resources are boundless, and the only perceived limit to success is one’s level of ambition…. for many, that veil has been lifted. And for some it has revealed some blemishes that in more comforting times would be easier to ignore. One such organizational blemish, for many, is the lag in their business to adopt and employ a data strategy that can empower its sales people and improve results.

    Let’s face it. Doing what you did yesterday is a good approach if you believe that tomorrow will look similar to today. Not many sales managers share this view of the world anymore. Things are changing, they are changing fast, and many sales organizations that haven’t adopted a data strategy find themselves slow to react and at a disadvantage to their competitors.

    2. Data helps sales team understand subtle changes in customer behavior

    The contrast in talking to sales organizations with a data solution and those without is striking. Sales organizations committed to data, use buying trends and behaviors of their best customers to educate and inform the rest of their customers as well as increase add on sales and wallet share across their customer base. In a few mouse clicks, a rep can see what upsell and add on opportunities exist and prioritize their calling efforts.

    Data driven sales organizations can react to the pulse of their customers, often times pro-actively to head off issues before a customer is fully at-risk. Subtle changes in purchasing behavior can reveal at risk accounts and trends that the salesperson can address pro-actively to retain a customer rather than trying to win them back after they leave.

    3. A data strategy is necessary to compete in a shrinking marketplace

    Further, as competition for a lesser number of customers in the marketplace heats up, data driven sales organizations have 360 degree view of their customer that allows them to share insights, improve customer experience and add value to every interaction. Customers have come to expect a higher level of communication and experience from their vendors that mirrors what they have experienced online. 

    Companies that have not embraced a data strategy for sales find themselves at a tremendous disadvantage. In these rapidly changing times, sales managers of those organizations may be asking themselves how long they can afford to wait before they level the playing field for their team. 

    Author: Mark Giddens

    Source: Phocas Software

  • Key components of developing the right data strategy

    Key components of developing the right data strategy

    What does your company do? 

    That was a trick question. It doesn’t matter what you think your company does, it’s going to have to turn into a data company soon, if it hasn’t started already, in addition to continuing to provide your core product or service. This may sound daunting, but it’s a good challenge to have and one that will ultimately improve your offering, delight your customers, increase stickiness and adoption, and keep you competitive in a changing data landscape. 

    In this article you will read a brief overview a data strategy's key components: what a data strategy has to encompass, vital considerations when dealing with data, and who the main players are when it comes to executing your data strategy.

    Data strategies for the uninitiated

    First off, 'So, what even is a data strategy anyway?' Everyone knows that data is important for organizations to make money, but just having a bunch of data is useless without a data strategy. A data strategy deals with all aspects of your data: where it comes from, where it’s stored, how you interact with it, who gets to see what, and who is ultimately in charge of it. This sounds like a tall order and you may be thinking 'Oh man! Is that my job?' Depending on your company’s level of data maturity, it might not be any one person or department’s job (yet). But you do need to start coming up with answers to all of these tough questions.

    “Everybody is going to assume that somebody else is taking care of the data, and the result is, nobody does”. - Charles Holive, Managing Director for Sisense’s Strategy Consulting Business.

    That’s a bad situation, and you definitely need to know who’s in charge of what data. However, one of the first questions you need to answer as you build your strategy is 'So, what do we want to do with all this data? Why? And how will this make us money/delight our customers?' Those answers ultimately have to come from the business unit that has the idea for making money/delighting customers in the first place: 'Internal data is owned by the function that creates it. It all sits within IT, but sales should own sales data, marketing should own the marketing data…' 

    These departments should also own the efforts to use that data to create new revenue, engagement, etc. A common misconception when it comes to data strategies is that they should be these all-encompassing, top-down initiatives that come from an all-seeing, all-knowing Chief Data Officer (more on this later), when actually you can, and should, build your strategy piece by piece and that the process should be driven by the areas who have the data in the first place. Whatever the initiative is (surfacing user data to inform them about their buying habits, etc.), the department with the data and the idea for using it should drive it. This increases ownership within the department and prevents the 'whose job is this?' question.

    Diversifying your data

    Once you’ve got your initiative in mind, it’s important to think about what data you need for it. The two main kinds of data your company has will be the data you generate and own and the data your customers generate, which you are only the custodians of (they own it). Whatever you plan on doing with data, this is the time to make sure that you are legally within your rights (consult your company’s legal department, counsel, etc.) and make sure that your user agreement contracts are properly worded to allow you to do what you want with the data you have. 

    There’s a third type of data your company can and should be thinking about for your data projects, and that’s third-party data, which can be used to add context to your datasets. More and more companies want to augment the context of their data. In healthcare, for instance, a hospital only has access to about 2% of the data on its patients, which is created while they are physically in the hospital. They are missing the other 98% of the data that is generated everywhere else. Their eating habits, buying habits, some of this could be useful to help provide better care. 

    As the outlook on data shifts from a company-centric to an ecosystem-spanning view, more and more companies will buy, sell, trade, and partner with other companies for access to the data they want and need to augment their datasets, deliver more value, and maintain a dominant position in their industries.

    Key players for implementing your data strategy

    Now that you know where the data strategy starts, who’s responsible for implementing it at the department level, and how to safely and responsibly use the data you’ve got, it’s time to talk about the key players within your organization who will help keep everything running smoothly. These are the business unit stakeholders, data professionals pulling the data together, and maybe the Chief Data Officer if your organization has one. The first one, we already covered: whoever came up with the idea for how to use your data (and whatever data you can get access to) should own the execution of that plan.

    They’ll need support from your company’s data experts: the IT department and data engineers (if you have them). These folks will walk the team executing the plan through the specifics of where the data is and how to access it. Additionally, they’ll make sure that the company has the analytics platform needed to pull it all together and present meaningful insights to your users. They may even be instrumental, along with product team members, in helping create embedded analytics that will live right inside your product or service.

    Lastly, we should discuss the Chief Data Officer (CDO). As previously discussed, this person is not the be-all-end-all of your data strategy. Many businesses, right now, may not even have a CDO, but when you do get one, they will wear a lot of hats within the organization. Their first job will be to look at all the data your company has and how it’s all being used and make sure that the processes in place make sense and are working. They will also check in with legal and make sure that data is being used in a way that’s compliant and that all user agreements are properly worded to protect users and the company. The CDO will also look for ways to augment your data holdings (through buying, partnering, etc.) to keep expanding the ways your company can use data to increase revenue. 

    Data strategies and culture

    A final, vital aspect of the CDO’s role is a cultural one: they have to assess the organization and make sure that everyone using data has a mindset that prioritizes the security of the data, but also the opportunity that it represents for the company. Every company is becoming a data company and the financial incentives are too huge to ignore: ´The market for monetizing data and insights is getting so big. Depending on what you read, it’s between 20 and 36 billion dollars over the next three or four years´. 

    Business teams need to understand this and be serious about getting the most out of their data. Dragging your feet or being half-hearted about it will not do: 'If someone says ‘the way I’ve made money before is the way I will make money tomorrow,’ I say ‘well, I’m not going to invest in your company.’ I know five years from now, someone’s going to get to your data and create much more value than you do with your transactions'. 

    Encouraging a culture of experimentation is key to finding new ways to use data to drive revenue and keep your company competitive. Charles suggested finding ways to make building new apps and projects with data as easy as possible, so that people across the company can build quickly and fail quickly, to find their way to solutions that will ultimately pay off for users and the company. 

    What will your company do?

    By now your head is probably spinning with all the potential challenges and opportunities of your data strategy (whether you had one when you started reading this article or not). If your team isn’t doing stuff with data right now, start asking the hard questions as to why that is and how you can change it. If your company doesn’t have the tools to build the analytics functionality you need, figure out how to get them. Whatever you have in your imagination, start building it. If you don’t, someone else will. 

    Author: Jack Cieslak

    Source: Sisense

  • The Growing Influence of Ethical AI in Data Science

    The Growing Influence of Ethical AI in Data Science

    Industries such as insurance that handle personal information are paying more attention to customers’ desire for responsible, transparent AI.

    AI (artificial intelligence) is a tremendous asset to companies that use predictive modeling and have automated tasks. However, AI is still facing problems with data bias. After all, AI gets its marching orders from human-generated data -- which by its nature is prone to bias, no matter how evolved we humans like to think we are.

    With the wide adoption of AI, many industries are starting to pay attention to a new form of governance called responsible or ethical AI. These are governance practices associated with regulated data. For most organizations, this involves removing any unintentional bias or discrimination from their customer data and cross-checking any unexpected algorithmic activity once the data moves into production mode.

    This is an especially important transformation for the insurance industry because consumers today are becoming far more attuned to their personal end-to-end experience in any industry that relies on the use of personal data. By advancing responsible, ethical AI, insurers can confidently map to the way consumers want to search for insurance and find insurance policies, and they can align with the values and ethics that govern this kind of personal search.

    What Does Inherent Bias Look Like in AI Algorithms Today?

    One of the more noticeable examples of human-learned, albeit unintentional, data bias today is around gender. This happens when the AI system does not behave the same way for a man versus a woman, even when the data provided to the system is identical except for the gender information. One example outcome is that individuals who should be in the same insurance risk category are offered unequal policy advice.

    Another example is something called the survivor bias, which is optimizing an AI model using only available, visible data -- i.e., “surviving” data. This approach inadvertently overlooks information due to the lack of visibility, and the results are skewed to one vantage point. To move past this weakness, for example in the insurance industry, AI must be trained not to favor the known customer data over prospective customer data that is not yet known.

    More enterprises are becoming aware of how these data determinants can expose them to unnecessary risk. A case in point: in their State of AI in 2021 report, McKinsey reviewed industry regulatory compliance through the filter of a company’s allegiance to equity and fairness data practices --and reported that two of companies’ top three global concerns are the ability to establish ethical AI and to explain their practices well to customers.

    How Can Companies Proactively Eliminate Data Bias Company-wide?

    Most companies should already have a diversity, equity, and inclusion (DEI) program to set a strong foundation before exploring practices in technology, processes, and people. At a minimum, companies can set a goal to remove ingrained data biases. Fortunately, there are a host of best-practice options to do this.

    • Adopt an open source strategy. First, enterprises need to know that biases are not necessarily where they imagine them to be. There can be a bias in the sales training data or in the data at the later inference or prediction time, or both. At Zelros, for example, we recommend that companies use an open source strategy to be more open and transparent in their AI initiatives. This is becoming an essential baseline anti-bias step that is being practiced at companies of all sizes. 

    • Utilize vendor partnerships. Companies that want to put a bigger stake in the ground when it comes to regulatory compliance and ethical AI standards can collaborate with organizations such as isahit, dedicated to helping organizations across industries become competent in their use and implementation of ethical AI. As a best practice, we recommend that companies work toward adopting responsible AI at every level, not just with their technical R&D or research teams, then communicate this governance proliferation to their customers and partners. 

    • Initiate bias bounties. Another method for eliminating data bias was identified by Forrester as a significant trend in their North American “Predictions 2022” guide. It is an initiative called bias bounties. Forrester stated that, “At least five large companies will introduce bias bounties in 2022.”
      Bias bounties are like bug bounties, but instead of rewarding users based on the issues they detect in software, users are rewarded for identifying bias in AI systems. The bias happens because of incomplete data or existing data that can lead to discriminatory outcomes from AI systems. According to Forrester, in 2022, major tech companies such as Google and Microsoft will implement bias bounties, and so will non-technology organizations such as banks and healthcare companies. With trust high on stakeholders’ agenda, basing decisions on accountability and integrity is more critical than ever.

    • Get certified. Finally, another method for establishing an ethical AI approach -- one that is gaining momentum -- is getting AI system certification. Being able to provide proof of the built-in governance through an external audit goes a long way. In Europe, the AI Act is a resource for institutions to assess their AI systems from a process or operational standpoint. In the U.S., the NAIC is a reference organization providing guiding principles for insurers to follow. Another option is for companies to align to a third-party organization for best practices.

    Can an AI System Be Self-criticizing and Self-sustaining?

    Creating an AI system that is both self-criticizing and self-sustaining is the goal. Through the design itself, the AI must adapt and learn, with the support of human common sense, which the machine cannot emulate.

    Companies that want to have a fair prediction outcome may analyze different metrics at various subgroup levels within a specific model feature (for example gender) because that can help identify and prevent biases before they go to market with consumer-facing capabilities. With any AI, making sure that it doesn’t fall into a trap called a Simpson’s Paradox is key. Simpson's Paradox, which also goes by several other names, is a phenomenon in probability and statistics where a trend appears in several groups of data but disappears or reverses when the groups are combined. Successfully preventing this from happening ensures that personal data does not penalize the client or consumer who it is supposed to benefit.

    Responsible Use of AI Can Be a Powerful Advantage

    Companies are starting to pay attention to how responsible AI has the power to nurture a virtuous, profitable circle of customer retention through more reliable and robust data collection. There will be challenges in the ongoing refinement of ethical AI for many applications, but the strategic advantages and opportunities are clear. In insurance, the ability to monitor, control, and balance human bias can keep policy recommendations meant for certain races and genders fairly focused on the needs of those intended audiences. Responsible AI leads to stronger customer attraction and retention, and ultimately increased profitability.


    Companies globally are revving up their focus on data equity and fairness as a relevant risk to mitigate. Fortunately, they have options to choose from to protect themselves. AI offers an opportunity to accelerate more diverse, equitable interactions between humans and machines. Solutions can help large enterprises globally provide hyper-personalized, unbiased recommendations across channels. Respected trend analysts have called out data bias a top business concern of 2022. Simultaneously, they identify responsible, ethical AI as a forward-thinking solution companies can deploy to increase customer and partner trust and boost profitability.

    How are you moving toward an ethical use of AI today?

    Author: Damien Philippon

    Source: TDWI

  • Why cloud solutions are the way to go when dealing with global data management

    Why cloud solutions are the way to go when dealing with global data management

    To manage geographically distributed data at scale worldwide, global organizations are turning to cloud and hybrid deployments.

    Enterprises that operate worldwide typically need to manage data both on the local level and globally across all geographies. Local business units and subsidiaries must address region-specific data standards, national regulations, accounting standards, unique customer requirements, and market drivers. At the same time, corporate headquarters must share data broadly and maintain a complete view of performance for the whole multinational enterprise.

    Furthermore, in many multinational firms, data is the business. In worldwide e-commerce, travel services, logistics, and international finance for example. So it behooves each company to have state-of-the-art data management to remain innovative and competitive. These same organizations must also govern data locally and globally to comply with many legislated regulations, privacy policies, security measures, and data standards. Hence, global businesses are facing a long list of new business and technical requirements for modern data management in multinational markets.

    For maximum business value, how do you manage and govern data that resides on multiple premises, clouds, applications, and data platforms (literally) worldwide? Global data management based on cloud and hybrid deployments is how.

    Defining global data management in the cloud

    The distinguishing characteristic of global data management is its ever-broadening scope, which has numerous drivers and consequences:

    Multiple physical premises, each with unique IT systems and data assets. Multinational firms consist of geographically dispersed departments, business units, and subsidiaries that may integrate data with clients and partners. All these entities and their applications generate and use data with varying degrees of data sharing.

    Multiple clouds and cloud-based tools or platforms. In recent years, organizations of all sizes have aggressively modernized and extended their IT portfolios of operational applications. Although on-premises applications will be with us into the foreseeable future, organizations increasingly prefer cloud-based applications, licensed and deployed on the software-as-a-service (SaaS) model. Similarly, when organizations develop their own applications (which is the preferred approach with data-driven use cases, such as data warehousing and analytics), the trend is away from on-premises computing platforms in favor of cloud-based ones from Amazon, Google, Microsoft, and others. Hybrid IT and data management environments result from the mix of systems and data that exist both on premises and in the cloud.

    Extremely diverse data with equally diverse management requirements. Data in global organizations is certainly big, but it is also diverse in terms of its schema, latencies, containers, and domains. The leading driver of data diversity is the arrival of new data sources, including SaaS applications, social media, the Internet of Things (IoT), and recently digitized business functions such as the online supply chain and marketing channels. On the one hand, data is diversifying. On the other hand, global organizations are also diversifying the use cases that demand large volumes of integrated and repurposed data, ranging from advanced analytics to real-time business management.

    Multiple platforms and tools to address diverse global data requirements. Given the diversity of data that global organizations manage, it is impossible to optimize one platform (or a short list of platforms) to meet all data requirements. Diverse data needs diverse data platforms. This is one reason global firms are leaders in adopting new computing platforms (clouds, on-premises clusters) and new data platforms (cloud DBMSs, Hadoop, NoSQL).

    The point of global data management in the cloud

    The right data is captured, stored, processed, and presented in the right way. An eclectic portfolio of data platforms and tools (managing extremely diverse data in support of diverse use cases) can lead to highly complex deployments where multiple platforms must interoperate at scale with high performance. Users embrace the complexity and succeed with it because the eclectic portfolio gives them numerous options for capturing, storing, processing, and presenting data in ways that a smaller and simpler portfolio cannot satisfy.

    Depend on the cloud to achieve the key goals of global data management. For example, global data can scale via unlimited cloud storage, which is a key data requirement for multinational firms and other very large organizations with terabyte- and petabyte-scale data assets. Similarly, clouds are known to assure high performance via elastic resource management; adopting a uniform cloud infrastructure worldwide can help create consistent performance for most users and applications across geographies. In addition, global organizations tell TDWI that they consider the cloud a 'neutral Switzerland' that sets proper expectations for shared data assets and open access. This, in turn, fosters the intraenterprise and interenterprise communication and collaboration that global organizations require for daily operations and innovation.

    Cloud has general benefits that contribute to global data management. Regardless of how global your organization is, it can benefit from the low administrative costs of a cloud platform due to the minimal system integration, capacity planning, and performance tweaking required of cloud deployments. Similarly, a cloud platform alleviates the need for capital spending, so up-front investments are not an impediment to entry. Furthermore, most public cloud providers have an established track record for security, data protection, and high availability as well as support for microservices and managed services.

    Strive to thrive, not merely survive. Let’s not forget the obvious. Where data exists, it must be managed properly in the context of specific business processes. In other words, global organizations have little choice but to step up to the scale, speed, diversity, complexity, and sophistication of global data management. Likewise, cloud is an obvious and viable platform for achieving these demanding goals. Even so, global data management should not be about merely surviving global data. It should also be about thriving as a global organization by leveraging global data for innovative use cases in analytics, operations, compliance, and communications across organizational boundaries.

    Author: Philip Russom

    Source: TDWI

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