4 items tagged "innovation"

  • 5 Predictions for Artificial Intelligence in 2016

    AIGet ready to work alongside smart machines

     At Narrative Science, we love making predictions about innovation, technology and, in particular, the rise of artificial intelligence. We may be a bit too optimistic about the timing of certain technologies going mainstream, but we can’t help it. We are wildly optimistic about the future and genuinely believe that we have entered a dramatically new era of artificial intelligence innovation. That said, this year, we tried to focus our predictions on the near-term. Here’s our best guess as to what will happen in 2016.

    1. New inventions using AI will explode.

    In 2015, artificial intelligence went mainstream. Major tech companies including Google, Facebook, Amazon and Twitter made huge investments in AI, almost all of technology research company Gartner’s strategic predictions included AI, and headlines declared that AI-driven technologies were the next big disruptor to enterprise software. In addition, companies that made huge strides in AI, including Facebook, Microsoft and Google, open-sourced their tools. This makes it likely that in 2016, new inventions will increasingly come to market from companies discovering new ways to apply AI versus building it. With entrepreneurs now having access to low-cost quality AI technologies to create new products, we’ll also likely see an explosion in new startups using AI.

    2. Employees will work alongside smart machines.

    Smart machines will augment work and help employees be more productive, not replace them. Analytics industry leader, Tom Davenport, stated it well when he predicted that “smart leaders will realize that augmentation—combining smart humans with smart machines—is a better strategy than automation.”

    3. Executives will demand transparency.

    Business leaders will realize that smart machines throwing out answers without explanation are of little use. If you walked into a CEO’s office and said we need to shut down three factories, the first question from the CEO would be: “Why?” Just producing a result isn’t enough, and communication capabilities will increasingly be built into advanced analytics and intelligent systems so that these systems can explain how they are arriving at their answers.

    4. Artificial Intelligence will reshape companies outside of IT.

    AI-powered business applications will start to infiltrate companies other than technology firms. Employees, teams and entire departments will champion process re-engineering efforts with these intelligent systems whether they realize it or not. As each individual app eliminates a task, employees will automate many of the mundane parts of their jobs and assemble their own stack of AI-powered apps. Teammates eager to be productive and stay competitive will follow, along with team managers who are looking to execute on cost-cutting efforts.

    5. Innovation labs will become a competitive asset.

    With the pace of innovation accelerating, large organizations in industries such as retail, insurance and government will focus even more energies on remaining competitive and discovering the next big thing by forming innovation labs. Innovation labs have existed for some time, but in 2016, we’ll begin to see more resources devoted to innovation labs and more technologies discovered in the labs actually implemented across different company functions and business lines.

    2016 will be a big year for AI. Much of the work in AI in 2016 will be the catalyst for rapid acceleration of the development and adoption of AI-powered applications. In addition and perhaps even more significant, 2016 will bring about a major shift in the perception of AI. It will cease to be a scary, abstract set of ideas and concepts and will be better understood and accepted as more people realize the potential of AI to augment what we do and make our lives more productive.

    Source: Time

  • Data, Analytics & Fuel Innovation at Celgene

    Williams-Richard-CelgeneCIO Richard Williams leads a global IT organization that’s harnessing digital, data, and analytics to support R&D innovation, drive operational excellence, and help Celgene achieve first-mover advantage in the shift to value-based, personalized health care intended to help patients live longer and healthier lives.
    An explosion of electronic health information is rocking the entire health care ecosystem, threatening to transform or disrupt every aspect of the industry. In the biopharmaceutical sector, that includes everything from the way breakthrough scientific innovations and insights occur to clinical development, regulatory approvals, and reimbursement for innovations. Celgene, the $11 billion integrated global biopharmaceutical company, is no exception.
    Indeed, Celgene, whose mission is to discover, develop, and commercialize innovative therapies for the treatment of cancer, immune-inflammatory, and other diseases, is aggressively working to leverage the information being generated across the health care system, applying advanced analytics to derive insights that power its core business and the functions that surround and support it. Long known for its commitment to external scientific collaboration as a source of innovation, Celgene is investing to harness not only the data it generates across the enterprise, but also the real-world health care data generated by its expanding network of partners. Combined, this network of networks is powering tremendous value.
    CIO Richard Williams sees his mission—and that of the IT organization he leads—as providing the platforms, data management, and analytics capabilities to support Celgene through the broader industry transition to value-based, personalized health care. At Celgene, this transformation is enabled by a focus on the seamless integration of information and technology. A cloud-first platform strategy, coupled with enterprise information management, serves as the foundation for leveraging the data generated and the corresponding insights from internal and external health care data.
    Williams recently shared his perspective on the changes wrought by enormous data volumes in health care, the role of IT at Celgene, and the ways IT supports life sciences innovation.
    Can you describe the environment in which Celgene is currently operating?
    Williams: We are living in an exciting era of scientific breakthroughs coupled with technology convergence. This creates both disruption and opportunity. The explosion and availability of data, the cloud, analytics, mobility, artificial intelligence, cognitive computing, and other technologies are accelerating data collection and insight generation, opening new pathways for collaboration and innovation. At Celgene, we’re able to apply technology as never before—in protein homeostasis, epigenetics, immuno-oncology, immuno-inflammation, informatics, and other fields of study—to better understand disease and develop targeted therapies and treatments for people who desperately need them.
    How does IT support scientific and business innovation at Celgene?
    At its core, Celgene IT is business aligned and value focused. Rather than looking at technology for technology’s sake, we view information and technology as essential to achieving our mission and business objectives. As an integrated function, we have end-to-end visibility across the value chain. This enables us to identify opportunities to leverage technology investments to connect processes and platforms across all functions. As a result, we’re able to support improvements in R&D productivity, product launch effectiveness, and overall operational excellence.
    This joint emphasis on business alignment and business value, which informs everything we do, is manifest in three important ways:
    First is our emphasis on a core set of enterprise platforms, which enable us to provide end-to-end visibility rather than a narrower functional view. We established a dual information- and cloud-first strategy to provide more comprehensive platforms of capabilities that can be shared across Celgene’s businesses. The cloud—especially with recent advances in security and analytics—provides tremendous scale, agility, and value because it allows us to standardize and create both consistency and agility across the entire organization regardless of device or access method. It’s our first choice for applications, compute power, and storage.
    Second is our focus on digital and the proliferation of patient, consumer, scientific, and it is creating. Health care data is growing exponentially—from something like 500 petabytes (PB) of data in 2013 to 25,000 PB by 2020, according to one study.
    To address this opportunity, we’ve initiated an enterprise information management (EIM) strategy through which we are targeting important data domains across our business and applying definitions, standards, taxonomies, and governance to data we capture internally and from our external partners. Establishing that consistency is critically important. It drives not only innovation, but also insight into our science, operations, and, ultimately, patient outcomes. Celgene is at the forefront in leveraging technologies that offer on-demand compute and analytic services. By establishing data consistency and influencing and setting standards, we will support our own objectives while also benefiting the broader industry.
    Third is our support for collaboration—the network of networks—and the appropriate sharing of information across organizational boundaries. We want to harness the capabilities and data assets of our partners to generate insights that improve our science and our ability to get better therapies to patients faster. Celgene is well-known in the industry for external innovation—how we partner scientifically—and we are now extending this approach to data and technology collaboration. One recent example is our alliance with Medidata Solutions, whose Clinical Cloud will serve as our enterprise technology and data platform for Celgene clinical trials worldwide. Celgene is also a founding commercial member of the Oncology Research Information Exchange Network, a collaboration of cancer centers spearheaded by M2Gen, a health informatics solution company. And we have teamed with ConvergeHEALTH by Deloitte and several other organizations for advanced analytics around real-world evidence and knowledge management, which will also be integrated into our data platform.
    You’re building this network-enabled, data-rich environment. But are your users prepared to take advantage of it?
    That’s an important aspect of the transformation and disruption taking place across multiple industries. Sure, IT can make information, technology, and insights available for improved decision-making, but the growing complexity of the data—whether it’s molecular structures, genomics, electronic medical records, or payment information—demands different skill sets.
    Data scientists are in high demand. We need to embed individuals with those specialized skills in functions from R&D to supply chain and commercial. At the same time, many more roles will require analytics acumen as part of the basic job description.
    As you build out your platform and data strategies, are you likely to extend those to your external alliances and partners?
    External collaboration enabled by shared data and analytics platforms is absolutely part of our collaboration strategy. If our informatics platforms can help our academic or commercial biotech collaborators advance the pace of their scientific evaluations, clinical studies, and commercialization, or they can help us with ours, that’s a win-win situation—and a differentiator for Celgene. We are already collaborating with Sage Bionetworks, leveraging Apple ResearchKit to develop an app that engages patients directly in innovation aimed at improving treatments for their diseases. We’re also working with IBM Watson to increase patient safety using cognitive computing to improve drug monitoring. As the power of collaborative innovation continues, collaboration will become more commonplace and lead to some amazing results.
    As you look out 12 to 18 months, what technologies might you want to bolt onto this platform or embed in your EIM strategy?
    The importance of cognitive computing, including machine learning and artificial intelligence, will continue to grow, helping us to make sense of the increasing volumes of data. The continued convergence of these technologies with the internet of things and analytics is another area to watch. It will result in operational insights as well as new, more intelligent ways to improve treatments for disease.
    What advice do you have for CIOs in health care or other industries who may not be as far along in their cloud, data, and analytics journeys?
    A digital enterprise is a knowledge- and information-driven enterprise, so CIOs should first focus on providing technologies and platforms that support seamless information sharing. In the process, CIOs should constantly be looking at information flows through an enterprise lens—real value is created when information is connected across all functions. Next, it’s increasingly important for CIOs to help build a technology ecosystem that allows the seamless exchange of information internally and externally because transformation and insight will occur in both places. Last, CIOs need to recognize that every job description will include data and information skills. This is an especially exciting time to be in IT because the digital capabilities we provide increasingly affect every function and role. We need to help people develop the skills they need to take advantage ofwhat we can offer now and in the future.
    Source: deloitte.wsj.com, November 14, 2016
  • Digital Transformation Requires Total Organizational Commitment

    shutterstock 127574942By now you’ve surely heard that moving forward, every company will be a software company, and that shift is happening now as companies large and small scramble to transform into digitally-driven organizations.

    Wherever you turn, businesses are facing tremendous disruptive pressure. What’s interesting is that the theory about how firms should be dealing with this massive change is itself in flux, transforming if you will, as organizations come to grips with the idea that the most basic ways they do business are being called into question.

    Just over a year ago when I researched this topic, I found that the general method for dealing with disruption was developing pockets of innovation inside a company using labs or incubators to prime the innovation pump. Today, when I explore the same issues, I’m finding that companies are taking a much more comprehensive approach that has to do with reviewing every department and business process in the organization.

    The issue with the lab or incubator concept is how you move the kind of innovative thinking from that internal innovation test bed into the organization at large. The reasoning behind isolating innovation was sound enough, because those fledgling ideas would very likely be sucked up into the vacuum of existing business policies where they get lost forever in a haze of bureaucratic negativity. If you want to kill innovation, you just keep saying “no.”

    The new thinking says you have to start looking at the big picture from the first day and you have to consider the impact that these changes are going to have on the entire organization. You have to figure out how to grease the skids of creativity so they don’t get slowed down by HR, legal, IT and by all the systems and departments that have been put in place to protect and limit these kinds of changes inside large organizations. Now the idea is to teach those well-meaning naysayers to get the heck out of the way and for them to also find new ways of achieving their goals and requirements as the organization marches forward into a digitally driven future.

    Thinking Bigger Picture

    As we’ve seen through the experience of implementing individual enterprise systems such as content management, ERP or CRM trying to get a large organization moving in the same direction across departments is a huge challenge. When you suddenly put your whole business model on notice, a pocket of innovation is just too incremental to deal with that scale of change.

    Aaron Levie, CEO at Box is co-teaching a course this semester at Stanford with professor Rob Siegel called The Industrialist’s Dilemma where they explore the kinds of issues large established organizations face as they maneuver through these massive changes.

    “What happens when you take a business that’s good at analog stores, and software can deliver new disruptive experiences? How do they respond? No product is more physical and analog than a retail store or car. We are seeing those [delivery models] inverted and flipped over by technology,” says Levie.

    When I spoke to Edward Hiaett, SVP of services at Pivotal and in charge of Pivotal Labs, at Web Summit in October, 2014, his company was one of those organizations working on the pockets of innovation approach, but he said his company’s thinking has evolved.

    When he looks at a firm like one of his clients, Ford, he sees a company that has to completely change the way it does business. In the next decade it’s possible that many people won’t own cars in the traditional sense. In fact they might not even be driving them anymore as self-driven cars become more widespread. That means the whole firm has to start examining all of its long-established systems around how they design, deliver, market and sell automobiles.

    And they need to start looking at these systems now before the delivery model changes, Hiaett says. It doesn’t mean it changes all at once, but if Ford is in the midst of pivoting from a business selling cars to one that’s in the ‘the mobility business’, it’s clearly going to have a major impact on all of the company’s long-established business processes.

    Clarity Of Vision

    This means that the executive suite has to have a clear plan for the future, and a way to put the company on the road toward delivering on that vision. They can’t hide the innovation team in the basement. They need to inject innovative thinking into every process in the organization and that requires reconsidering every process, says Michael Krigsman, founder of cxotalk.com, a weekly web-based talk show on which Krigsman interviews leading tech industry executives.

    “The successful executives are able to embrace change. This is a very key point and it’s really the most difficult thing about this. With the exception of startups, every company has an established business model and way they do business. Product lines, services and employees have been optimized for standard processes,” Krigsman told TechCrunch.

    Executives require a particular set of skills and approaches as the organization shifts:


    Levie says that he sees CIOs with these kinds of traits in his job as Box CEO, but he says he has seen organizations held back when there isn’t a unified front in the C suite.

    “I think the majority of companies recognize how disruptive these trends are. A small percentage recognize this at the CEO level and board level. Me personally in building and selling enterprise software, we interact with a large percentage of CIOs that get it, but don’t always have the support from CEO and that makes it harder without top-down support,” Levie explained.

    It’s A People Problem

    As we tend to do in this business, we have been attacking this type of change by throwing different technologies at it, and while technology can certainly help, it requires a much more personal approach by management, one that takes the people who have to implement these massive changes into account.

    Just last week Accenture released its annual 2016 Technology Vision Report and the consulting juggernaut says the success of any company going through fundamental digital transformation is understanding that it’s first and foremost a people issue.

    Finding ways to help people across this digital divide and the culture shock that rapid change brings is going to be just as important as the technology we use to get there, says Marc Carrel-Billiard, global tech R&D Lead on digital transformation at Accenture.

    “When we talk to clients, we usually start by talking about technology, but [typically] after 15 minutes, we shift gears. We start talking about people and the digital culture shock they are in. If [clients] want to be digital, it’s not just about technological change because it’s coming [regardless]. Companies need to think about people or it will not work at all,” he said.

    How To Get There

    One lesson we should have learned after all these years of trying to implement incremental change management is that it’s always been about people and managing how to deal with these changes. Today, the speed of change is coming so quickly, and the requirements are so daunting, that it’s easy to get overwhelmed. It requires companies to shift their mindset completely, Krigsman says.

    “Companies that do this well are able to adopt a beginner’s mind set, taking an approach of looking at things from a fresh perspective,” he explained.

    This could involve, for example, having fewer impediments for customer service by implementing systems so that information flows more seamlessly from one department to another and across systems.

    “What does that mean to customers and internal processes? It comes down to being willing to experiment and look at things through a new lens,” Krigsman said.

    In fact, he recommends that companies partner with startups, which tend to be smaller, more nimble and creative. “The reason for that, the big challenge is how do you inject new thinking. And that’s a very hard thing to do because it comes down to several things, the ingrained behaviors of people who have been doing this job this way for a long time,” he said.

    By injecting new thinking into a company, employees can start to see that there are different ways to handle those standard business practices and can begin to incorporate that type of creative thinking into their organizational philosophy.

    When you consider that 88 percent of the Fortune 500 companies in 1955 are now gone, it’s not hard to see that change has always been with us, but the rate of change is accelerating dramatically due in large part to the disruption brought about by digital transformation.

    “The cool thing is that incumbents recognize that the same assets that can hold them back, can also be used to compete in a different guise,” Levie said. That means it’s not all gloom and doom, companies just have to start thinking much more creatively about their digital future and the effect that will have across the organization.

    Source: TechCrunch

  • Ten ways to drive value from big data

    bigstock-Big-data-concept-in-word-tag-c-49922318Just think what $48 billion could buy. In the private sector, that could buy a lot of R&D and innovation; the lifeblood of a successful and growing economy. In the public sector, think of the boost it could give to education, healthcare and defence.

    What is that figure and why is it relevant?

    According to a new report from PricewaterhouseCoopers, that is the sum of money the Australian economy left on the table last year and wasted due to its inability to fully leverage the potential of data-driven innovation.

    $48bn: that’s the equivalent of 4.4 per cent of gross domestic product and about the same as the entire (and struggling) Australian retail industry.

    Little wonder that big data is top of the CIO priority list on almost every report you read. So why then do people still question the value of big data?

    The challenge is that, while the principle of getting value out of all the data that is now available in the world in order to gain new insight, better serve customers and find new markets seems simple, doing so is actually hard in practice.

    How do you realise commercial value from your data and not end up on a hunt for fool’s gold?

    1. Realise you’re on a journey

    When you begin to seek value from big data you will need a healthy degree of realism. It is hard to get to a revenue stream right away. More likely it will be a journey, starting with projects that add value to the existing business before arriving at opportunities that create whole new revenue streams.

    A good starting point is to leverage how you already make money. Consider your current revenue streams and how you could approach them differently to add new value, and what insights will be required to achieve that.

    2. Do you have the right team

    It is common to get caught up in the excitement of big ideas, but are you ready for it? It’s not to say the concept won’t become reality, but in many instances, it may become apparent that you are six-12 months away from it becoming viable.

    From experience, those companies that have gone at projects too early have failed to realise the value, and then stalled in their big data activities as a whole due to the failure, or have given the idea away in their attempts. A key element of being ready is having the right team in place; that is tenacious, will persist in the face of various obstacles, brave enough to take the initiative, and inventive being able to create new value from data.

    3. Selling your data could be like selling your soul

    As big data skills are in short supply, some companies decide to sell their data and achieve revenue from it that way. Unless you understand how you could monetise your data yourself, you are at risk of commoditising your own information.

    Before you go this route, focus on figuring out how your data can augment your unique value proposition, and don’t give up on creating new value from your data just because the first experiment doesn’t work. Also be aware of the potential risks of selling your data or the insights into it. These include issues around the security of the data once it is no longer under your control, the chance of re-identification of anonymised information, and the potential impact on your reputation if it is used for unintended purposes.

    4. Is there a wider ecosystem you could be part of?

    There are, however, increasing instances of companies and industries collaborating around, or selling data. For example, in the pharmaceutical industry we have seen organisations working as a consortium around the creation of new data sets from clinical research, as a way to overcome the prohibitive cost they would have faced doing it alone.

    The advice here would be not to rule options out — especially if they might enable you to do things with data that you couldn’t do before, and as a result, move up the value chain or closer to the end consumer.

    5. Don’t chase fool’s gold

    Use data, and especially social data, wisely. While providing great insight into the digital DNA of customer decision making, developing accurate models for sentiment analysis is hard, due to the large amount of false positives that exist.

    The nature of social means that many companies, at any time of the day or night, can have somebody saying something negative about them. How do you know when is this out of the norm?

    This level of understanding is something that often develops over time, and is an enrichment and maturity process in your analytics. Get it right and you can make money by developing an understanding of the soft signals, but unless you have a historic wealth of data in that area, gaining that sort of intelligence is hard to come by.

    6. Understand the customer contract

    You need to know what your trust relationship is with your audience. At the advent of the concept of 1-1 marketing, the customer understood the idea that by giving a bit more information they could get a more personalised treatment — and they didn’t mind it.

    However, with the arrival of big data, some customers feel that companies are going too far in collecting and using intimate details gathered without their prior permission. It also depends on the company.

    Customers expect the likes of Google and Shazam to use data to make recommendations. You just need to understand where the line needs to be drawn.

    7. Realise the goalposts are always shifting

    Change is inevitable and rapid. Your key competitors today may well be superseded by companies you never dreamt would fit in your market. However, it’s not all about the big guys. The internet of Things is going to be a great leveller, particularly in the field of ‘controls’ which is enabling new, smaller players to nip in and seize customer data and ownership. This is giving them the power to disintermediate traditional providers.

    8.Be prepared to unlearn

    In some cases you might find that the data shows that your assumptions are incorrect or that your activity is not the success you hoped it would be.

    For example, many companies seek to make money from content, but analysis shows it is a highly crowded market and there is little money in it other than for the likes of Google and Facebook.

    Companies need to understand what the implication is for their business. Does content add value to your customers, is it expected by your customers? Rather than there being money in the content itself, is there value that can be derived from better understanding the digital signature of your end users in being able to see what caused someone to click on an ad or what sort of people are visiting your website?

    9. Don’t confuse perfection with monetisation

    This is extremely important. When programmers and IT people talk about data, they often talk about perfection because we are very deterministic. We want to say ‘if this, then that’; if you get this data, then you will achieve exactly that result.

    It is hard to achieve perfection. Consider s case where you find you can cut the cost of a process by 30 per cent in a relatively short time frame. Or you might have the potential to cut your costs by double that, but it would most likely take you several years. Is the wait worth the lost opportunity cost? In a big data world, the best practice approach would be to experiment — try something and iteratively improve it instead of trying to get perfection out of the gate.

    10. Remember, David doesn’t always beat Goliath

    while new entrants can outmanoeuvre established organisations, most often David doesn’t beat Goliath. In fact, as often, the incumbent can use data to create barriers to entry, due to the fact that they have a significant advantage from the large volumes of historic customer information and transactional data they hold.

    However, to realise the benefit, established operations need to digitise or datify all of this information before their rivals do, and potentially seek out new data streams to compliment what they already have.

    For new entrants, many of the key business opportunities exist where there is a breakdown in a process or supply chain in an area that really matters to the customer — think Uber or Airbnb. Look for what is ‘broken’ in areas that are not already heavily digitised.

    The reality is that there is a whole range of data out there, offering new ways to get insights, drive value and compete. It is essential that you understand the potential and get excited about the opportunity. Think really broadly about the data that is out there, both inside and outside of the organisation, see what there is that could add value, without ending up on a hunt for fool’s gold.

    Source: The Australian Business review

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