Increasingly, CIOs say to me that the value of what they are doing is instantiated through data and analytics. But how do you build an analytics capability that works for the business as a whole? This is the question that I asked recently of the #CIOChat. Their answers should have value to everyone involved in the data or analytics.
CIOs had a variety of opinions regarding the operating models for analytics teams. Some believe they should depend upon a company and its industry. These CIOs suggest that a decentralizing analytics organization is best. In addition, they believe that the future of IT and of your business is very, very distributed. For this reason, they want to bring about self-service capabilities and allow subject matter experts to make data and analytics actionable. They suggest, interestingly enough, that what should possibly be centralized is data modeling and machine learning because these skills are hard to acquire and keep.
Other CIOs suggest that for larger and more federated organizations, analytics should be a distributed function. These CIOs believe – as Tom Davenport suggested in “Analytics at Work” – that analytics should be managed for the entire business. They stress continuous effort is needed to leverage data but believe there is value in embedding data scientists into every business unit.
In general, these CIOs say analytics teams should be managed for the entire business to ensure maximum ease of sharing and improvement of data collection and data security. Importantly, they argue that data integrity and cleanliness need to be owned by a central team while the entire organization should be empowered to leverage analytics. They suggest that most analytics have been historically app specific. Yet, they say analytics needs to become a core competency of organizations.
Tom Davenport argues in his book for a single corporate team that is farmed out to projects for the enterprise and its business units. This prevents what some CIOs worry about – a siloed analytics organization chart. When this occurs, there is no telling how much effort is duplicated in the analytics space or how many standards are followed. CIOs suggest that it is important to understand that distributed and silos are not the same. You can be distributed and still leverage knowledge across organizations. CIOs suggest, for some, another way of solving the issue is to establish an analytics enablement and governance group that helps coordinate decentralized efforts around the organization. The goal here should be to share knowledge, costs, know-how, and toolkits while tackling shared goals. CIOs say this kind of “Center of Excellence” thinking can accelerate business results.
CIOs shared openly that everything they do has to do increasingly with analytics. So, it is essential to have the right context when understanding where to go with analytics. Some CIOs say that they are building a group that will help to manage analytics across their organization, but pockets of specialized expertise remain. This appears to be in between stage 3-4 in Tom Davenport’s Maturity Model – stage 3 establishes “governance of technology and architecture for analytics” and stage 4, “manages analytical priorities and assets at an enterprise level” (“Analytics at Work”, Tom Davenport, page 53). CIOs insist for distributed analytics to work, there needs to be alignment on data governance, tools, and solutions. Otherwise, there will be multiple versions of truth, and ergo confusion.
Making the problem more difficult, CIOs say they are seeing more apps and infrastructure with their own embedded analytics. Regardless, CIOs say the trick is to leverage data and analytics across all business workflows. Distributed analytics must align how data and analytics are governed.
While some CIOs cringe at the notion of ‘data ownership', they believe the need for role-based permissions (a component of a data strategy) for data access. CIOs suggest that data husbandry is critical and few have an effective strategy. It is critical that you define "owner" in the context of data governance. As well, there should be no duplicate owners – only users working from trusted data sources. They see access as an analytic enabler. Appropriate corporate policy needs to establish acceptable use of the data within and external to organizations.
CIOs say that they believe the real value from analytics comes through the integrity of the data and having an enterprise data strategy regardless of where the analytics teams live. To what extent do organizations have an enterprise data strategy? At minimum this should include policy, standards, definitions, models, migration, integrations, security, and access control. Data strategy needs to drive analytics.
CIOs suggest analytics and data should be focused on real-time producing results and answers that propel the business forward. Delighting the customer should be a significant use-case. However, CIOs felt the right answers are related to the business questions that an organization is trying to solve whether customer or general line-of-business operations.
Some CIOs say the future is here, however, unevenly distributed. They see increasing focus on operations, especially finance. Other CIOs say that many organizations think customer experience is only about reporting and analytics. Ugly dashboards and reports cannot be the endpoint as it has become clear that winning customer experience is a much bigger thing. Is this an area for CIO influence?
CIOs suggest that the big question is how capable and how applicable built in analytics are. They say that many software solutions include descriptive analytics and a mix of advanced analytics. But since analytics is not the business of these vendors, they do not tend to be very good or flexible. These CIOs think businesses need to apply analytics in marketing, sales, and customer-facing product and services. Ops, they say, has already had a lot of reporting tools and long history of finding inefficiencies.
CIOs need to be able to strategize, deliver, and influence effectively regardless of distributed, federated, centralized, or hybrid models. Actionable data should inform continuous process improvement which every business unit or line of business should use for decision support, prioritization, and allocation of resources.
CIOs are candid that there is a lot of dashboarding with predictive analytics. Lots, they say, are trying to do text mining. They say financial services organizations are the main organizations doing time series; nevertheless, the analytical approach needs to be appropriate to the situation. One CIO said that they haven’t seen very many organizations doing streaming or real-time analysis. They most often see descriptive analytics; however, they stress that many organizations are still early on their analytics journeys.
According to the CIOs, success with analytics requires experts in analytical approaches (data scientists) and experts in process improvement. They emphasize that the wrong approach can lead to bad insights and believe that analytics need to be about leveraging business outcomes as their compass.
They stress that openness is the value that they want around access for the data and analytics that are created. In other words, they want “Information Democracy”, a term coin by Bernard Liataud, the former CEO of Business Objects. With regards to questions about access, role-based permissions, CIOs want these defined by business process owners.
Increasingly, CIOs see little value in using gut feel and historical data. They are clear that historical data is less interesting than real-time data. However, they suggest that some historical trends matched against real-time data provide insight opportunities depending upon the industry and business functions.
Many CIOs say that their organizations are moving to SaaS solutions that provide a data store, model analytics, comparative data, and an analytical expertise all at a single price. Further, they believe that many organizations do not have the technical chops to do real time or streaming analytics at scale or to even act from real-time analytics.
CIOs suggest that driving value is possible when those most knowledgeable with data and the nuances of the data ensure that valid business questions are being asked and answered. It is essential that data be designed so it can roll up into a strategic view.
CIOs, however, worry about analytical leaders in the space who are driving a desire to emulate Google and Walmart. CIOs say this is a necessary evolution for the effective data use, and businesses that don't figure out how to leverage analytics for competitive advantage will be choking on data dust and fumes soon. For this reason, it's becoming a cost of doing business, not really distinguishing on its own – what distinguishes a business is what the leadership does with higher velocity data and information.
At the same time, CIOs are candid about the challenges. They say that the volume of accumulated data is getting so large that batch processing is an increasing a problem. CIOs say many organizations are trying to discern what questions to ask, what questions add value, and how to identify a question that leads to actionable results. If you aren't thinking about strategic differentiation, you will be disrupted quickly. It's a key issue and strategic differentiator for us moving forward.
One CIO suggested that new business models and desire to target different markets and customer personas are driving the need for data and analytics. These are baseline capabilities before organizations can really do IoT, AI, or machine learning.
In sum, CIOs say analytics capability is table stakes. It's a negative differentiator if missing, neutral if have a market-appropriate baseline, and positive if producing advanced insights beyond competition.
Much has changed with CIOs and analytics in the last few years. I remember a discussion a few years back where CIOs want to stay far away from data governance. With data and analytics now central to the IT mission and the emergence of the CDO function, I expect the importance of data and analytics to grow.