IBV study on analytics, part 12: A business driven strategy for analytics

Global Banking Industry Marketing, Big Data, IBM

This is part 12 of our series on the findings and text from the IBM Institute for Business Value’s latest study and paper “Analytics: A blueprint for value - Converting big data and analytics insights into results” from my colleagues Fred Balboni, Glenn Finch, Cathy Rodenbeck Reese and Rebecca Shockley. 

In our last post, part 11, we began to discuss where our nine levers of capabilities fit in terms business application and introduced an approach to analytics with a business-driven blueprint. This approach defines how and why the organizations will use technology through three lenses: Strategy, Technology and Organization, with recommendations in each area. In this post, we dig into the first lens: Strategy.


fig15.jpgAccelerate analytics with a results-based program. Executives need to establish a business-driven agenda for analytics that enables executive ownership, aligns to enterprise strategy and business goals and defines any new business capabilities needed to deliver new sources of revenue and efficiencies. Moreover, they need to create a funding process that prioritizes projects that align with those goals. To facilitate the necessary activities within each Strategy lever (Sponsorship, Source of value and Funding) we offer executives the following recommendations (see Figure 15):


An effective analytics strategy will establish the strategic intent of data and analytics investments by creating explicit connections between the enterprise’s strategic goals and the analytic activities it outlines. 

Organizations whose line-of-business executives are personally involved in the development and management of an analytics strategy are the most effective. This involvement includes understanding the strengths and weaknesses of the organization’s digital infrastructure (hardware, software, data and talent) and then taking proactive steps to ensure the organization is capable of using data as a strategic asset.

Equally important are executive messages that outline, with certainty, how success will be defined. Use measureable business outcomes to transition from executive strategy to line-of-business actions. With a clear strategic vision from above, each descending level of management should ask, “How can we impact that set of business outcomes?” and, “What data do we need to do it?” Effective governance at every level means understanding how independent strategies can work together to achieve that common goal.

In addition to setting the analytics strategy, successful sponsors convey an enterprise-wide sense of ownership through communication and endorsement of analytic undertakings. Working together to achieve a common objective is a key strategy in creating value from analytics.

Source of value

Organizations are recognizing the value of analytics to identify new sources of revenue and efficiencies. Most explore the growth opportunities that abound in the still-emerging digital marketplace of the twenty-first century. They are looking at new business models and strategies that capitalize on the changing information they have about customers, competitors and markets, and leveraging new technologies to create efficiencies throughout the organization.

Executives should also focus on opportunities for operational innovation. Transformations in personal technology, from the Internet to smartphones, have profoundly altered customer interactions and expectations. At the same time, business technology innovations have created new platforms for interaction with customers and suppliers, new means of understanding business outcomes in relevant timeframes and innovative ways to manage the day-to-day operations of the business.

Once an organization has set its strategic path for analytics, the next step is to determine the business capabilities needed to create value. By developing a business-driven blueprint of the capabilities needed, organizations can better streamline and focus their analytic investments. Organizations should invest in business capabilities that support the immediate outcomes set forth by the strategy and that focus on solving key business challenges. Documenting the specific use of big data and analytics to solve business problems through use cases is a highly recommended practice.


The rigorous approach required for analytics funding can often be learned from the chief financial officer’s staff. Rather than relying on best guess and assumed impacts, organizations need to invest the time to develop value-based business cases to optimize the likelihood that investments will pay off, preferably promptly. Funding requests that include justifiable costs and anticipated benefits are a minimum among most top performing organizations, many of which also require multiple scenarios to understand the range of business outcomes and proofs of concept to justify potential benefits.

The challenge is establishing a way to allocate funding to maximize growth and efficiency. Building on the business capabilities blueprint, organizations need to develop an implementation roadmap that encompasses all the proposed analytics-related activities seeking investments across the organization. An integrated roadmap reduces the risk of duplicative or conflicting investments in hardware and software, which not only result in inefficient initial investments but add a downstream expense of reconciling the components as needed to facilitate cross-enterprise data sharing and analysis. 

An implementation roadmap can help the organization prioritize funding based on alignment to business outcomes. Due to the economic realities of most organizations, some desired outcomes won’t be funded. Organizations unable to prioritize data and infrastructure developments holistically risk the likelihood of misaligning dependencies and underutilizing scarce resources of analytics talent.

In our next post, part 13, we will look at recommended actions to help organizations gain the analytic capabilities associated with the Technology levers of Expertise, Data and Platform.

Catch up on the entire series so far with parts one through eleven: