Q&A with Gartner analyst Ted Friedman

Vice President and Distinguished Analyst, Gartner Research


Many organizations are now making big investments in data and analytics — how can they scale those investments broadly across the business?

Digital transformation revolves around data. It’s now well known that to be successful in any industry you need to be focused on data and analytics, you need to be making investments in these areas, and you need to be putting in place the fundamentals. We’ve seen lots of organizations do exactly that.


But now many organizations are striving to extend those investments. They may have delivered an individual project or one very focused initiative, but now the goal is to amplify that and scale the value of data and analytics more broadly throughout the enterprise and beyond, and make the initiatives core to the business.


It means finding ways to infuse data and analytics into the next highest priority business processes or line of business, or to form the basis of new customer experience, transformative business models and revenue streams. It means driving these investments more deeply in the areas where you have already been focused.


This means you need to take the best practices you have gained in your initial data and analytics initiatives and capabilities, and transplant them to other parts of the business. It means getting more people and more teams on board with using those capabilities. It means extending the reach of your data and analytics beyond the borders of your enterprise to engage customers, suppliers, and other third parties that you interact with to likewise gain benefits from or engage with the data and analytics capabilities that you’ve developed.


There’s also an element of scaling relating to organizational structure and leadership. Growing the size and the reach of your data and analytics teams, bringing more of the right skills into those teams, and engaging more roles in a distributed way across the business to be a virtualized part of those teams.


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What are the latest trends and some of the new innovations in data and analytics architectures and technologies?

We know that a lot of organizations working on data and analytics struggle with getting the right tools and technologies in place and modernizing their architectures to keep up with the newer demands. 


Historically, data and analytics programs have been supported by rather centralized architectures and tools. We brought all the data to a central location and then processed it in order to create some value. But in the face of modern pressures and demands that approach is now breaking down. There’s now too much data, it’s already too distributed, it’s too diverse, it’s too complex.


Value now needs to be generated at the point of creation of the data for timely response. This is one of the main reasons why distributed architectures are now becoming more required and more popular.


Secondly, the increase in self-service and broad distribution of analytics capabilities to everyone in the enterprise. The nature of data and analytics technologies is shifting such that less technical individuals’ right across the business can now engage with those tools and generate value from them. This is one of the main reasons why we see considerable growth in areas like self-service analytics tools, self-service data preparation technologies, and data governance-related tools that are enabling data stewards anywhere in the business to do more in creating trusted, high-value data.


Thirdly, an increase in the adoption of new and disruptive technologies such as data virtualization as a means to connect to highly distributed data and having the ability to access it on-the-fly. Also given the demands of digital business, the use of advanced analytics is also on the increase. While this doesn’t take away from the fact that the more traditional styles of analytics are still important, given the scale of everything today, technologies such as AI, data science and machine learning capabilities, natural language processing and blockchain are needed to cope with exponentially increasing volumes, complexity and pace.


And finally, architectures and technologies is the increasing adoption of a metadata-driven approach to data and analytics. That is to say, the importance of grounding all data and analytics architectures and technologies in a solid foundation of metadata. Having the ability to capture all of the knowledge about what data you have, where it resides, how it’s all related, who uses it, why, when and how — and then using that insight to provide more personalized, automated and properly governed solutions to the business.


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What’s happening with the chief data officer role — are CDOs succeeding?

The latest CDO survey shows we’re continuing to see the growth and expansion of the chief data officer role as more and more organizations either adopt or create this role in their enterprises. For the most part, we’re seeing the CDO role succeed, and we have good examples where CDOs are achieving success by generating value for their enterprises.


The CDO role is becoming more diverse. It’s not only focused on driving data governance, it’s also about owning analytics and having a solid appreciation of how data and analytics can support the organizations’ vision, goals and aspirations. One of the findings from the Gartner CDO survey reveals that two-thirds of CDOs now have responsibility for both data and analytics. 


CDOs most likely to succeed view themselves as champions of change. They’re not only looking at data governance and risk, but more importantly, they’re leading the development of their enterprises’ data and analytics capabilities to innovate and create new data commercialization and monetization opportunities. Those focused solely on the internal, operational benefits of data and analytics are tending to be somewhat less successful than those who diversify their strategy and also drive top-line and transformational benefits.


Successful CDOs tend to be those that have the full support, buy-in and backing of their leadership teams, even to the degree of reporting directly to the CEO. They’re supported with the budget, the people and the resources needed to succeed. This places them in a far stronger position to not only scale data and analytics throughout the enterprise but also to have the headroom and resources needed to innovate and identify new opportunities to commercialize and monetize their data assets. 


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Why is artificial intelligence (AI) such a hot topic these days?

It’s about closing the gap. Digital business, big data and the Internet of Things has fueled an exponential growth in data. Until recently humans equipped with traditional data and analytics capabilities have done pretty well. But as the transformation to digital accelerates at a pace, human know-how and traditional data and analytics capabilities are no longer enough. In order to keep up and close the capability gap, we need something more. AI holds the promise of being the technology that gets us there. That’s one reason why AI is such a hot topic at the moment — everybody’s desperate to find a way to close the gap.


In addition, AI has now reached a level of maturity where it's no longer theoretical, it’s no longer something only for academia or for rocket scientists. AI has now come out of the lab and reached a state where you can get real business value from it.


Finally, vendors in the data and analytics market have begun to inject various kinds of AI into their technologies to address data and analytic complexity and expand insights to more people across the enterprise. As a result, AI is now more generally available to organizations — as part of their enterprise applications and the analytic content they consume every day. For example, vendors in the analytics markets and related spaces have begun to include machine learning in their tools to automate tasks and time to unbiased insights. We’re seeing elements of machine learning, which is one style of AI, transforming how content is created using modern analytics and BI tools, data science and machine learning platforms, data preparation tools and data integration technologies.


While we’re not quite there yet, the democratization of AI is on the horizon. As more organizations realize its potential to add value beyond cutting costs by replacing people, we expect to see a growth in the use and application of AI in data and analytics across all industries.


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How are organizations finding ways to monetize their data assets?

We’re getting more and more questions from Gartner clients about how might they monetize the data that they are holding to create new revenue streams, as well as about what other organizations like themselves are doing to monetize data.


Organizations are looking around their data landscape, and they’re finding collections of data and insights about data that they believe are rather unique in their industry. In so doing, they are realizing that the data and insights they hold may be of value to their peers as well as organizations in other industries. So they’re starting to create business units to market and sell or license those data assets.


Very often it is organizations in a different industry sector to your own that could use your data and insights to be disruptive or innovative in their industry sector.


Data and analytics leaders need to help their organizations assess the economic value of data – that’s part of the discipline we call “Infonomics.” It’s about managing and accounting for information with the same rigor and formality as traditional business assets — financial, physical, human or intangible.


In his recently published book, “Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage,” Douglas Laney, Vice President and Distinguished Analyst with Gartner’s Chief Data Officer Research team, provides numerous examples of organizations monetizing information. Doug explains why organizations that fail to treat their information as a business asset to gain commercial value will not survive in the digital era.


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How should data and analytics leaders respond to heightened concerns about not only security and privacy of data but also compliance with new data protection regulations such as the EU’s General Data Protection Regulation (GDPR)?

High-profile security breaches are hitting the headlines on almost a daily basis. Equifax, Experian, Yahoo, Deloitte, CEX, Bupa, Three Mobile and Sports Direct are just a few recent examples. Alongside the need to comply with new data regulations such as the GDPR, which the EU is imposing, concerns about security and privacy of data are increasing.


Historically, data and analytics leaders have not been so concerned about security and privacy as these issues have been the responsibility of risk and security teams. But we’re starting to see each of these two areas care a lot more about what the other is doing and find reasons to collaborate.


Because of the new capabilities data and analytics leaders are bringing to their organizations, particularly as they capitalize on digital business opportunities, they now need to include security and privacy in their list of priorities. They need to collaborate with the security and risk colleagues to make sure security and privacy controls are embedded in all of their data and analytics initiatives. They also need to make sure that security and privacy are part of their data governance efforts.


It’s also fair to say that historically, data and analytics specialists thought of data governance as a way of making sure that the quality of data was as good as it could be. Data quality was important and indeed still is and always should be. But that’s no longer enough. In this modern era where the collection, sharing and connectedness of personally identifiable data is the norm, data and analytics leaders can’t only think about quality of data. They need to give due attention to security and privacy of their data assets as well.


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