Building Effective Analytics Ecosystems
As analytics becomes a C-suite imperative, many CIOs are exploring ways to establish or tap into ecosystems to extract and exploit insights from growing volumes of data.
Organizations are increasingly investing in analytics initiatives to steer day-to-day operations and improve business decision-making. According to a survey of 450 executives conducted by Deloitte Analytics,¹ companies that are significantly more likely to invest in building in-house analytics capabilities include those where data science is a priority. At these companies, senior executives understand the value of data analytics as a tool to generate insights and embed them in everyday decision-making processes.
But despite a growing emphasis on enterprise analytics, it can be difficult for a single organization to possess all of the necessary capabilities to derive strategic business value from their findings. More and more, organizations committed to analytics will integrate both internal and external resources and seek to build partnerships outside their organizations to support their efforts.
Nitin Mittal is Deloitte Consulting LLP’s practice leader for Analytics and Information Management. Previously, he led Deloitte’s Life Sciences and Healthcare Industry Analytics practice, among other roles. Here, Mittal discusses how CIOs can conceive, build, and operate their own large-scale analytics ecosystems—or tap into existing ones—to generate and scale useful insights for decision-makers throughout their enterprises.
Why is analytics and information management increasingly a core component of the C-suite agenda?
Mittal: We have gone from a world in which data was at a premium to one in which it is a commodity—and a source of profit. Companies are digitizing more of their business operations, resulting in an avalanche of new data. Analytics and information management is becoming part of the C-suite agenda because most executives recognize that their companies can benefit from uncovering relevant intelligence amidst the noise. Additionally, predictive insights that draw on data to create forward-looking models and forecast future trends can help to steer business decision-makers; increasingly, executives realize organizations that do not obtain those predictive findings can be left behind.
What is an analytics ecosystem?
Organizations have typically hired people who can extract data from a system—an ERP or HR set-up, for example—clean it, make sense of it, and enter it into reports and dashboards to generate KPIs in such areas as sales and market trends. Today, however, the diversity, complexity, and volume of data have increased immensely—companies pull data from social media, wearable computing devices, third-party data sets, and other sources. As a result, they seek skill sets beyond extracting data and compiling reports; they need data scientists, behavioral scientists, design thinkers, and user interface experts. Companies may already have some of the necessary resources internally but can also look externally to fill these roles. For example, they may embrace an open talent model and crowdsource discrete capabilities, such as the ability to create an algorithm that predicts ideal inventory based on customers’ purchasing patterns.
Additionally, an enterprise data management program has traditionally entailed implementing a monolithic data warehouse, an analytics package from a vendor, or a data lake, which retains raw data in a repository until needed. In these cases, the focus is on buying technologies. Now that analytics is becoming part of the C-suite agenda, executives are recognizing the need to embed analytical insights into the very fabric of their culture to provide direction to business decision-makers. CIOs are more inclined to stitch together or subscribe to services, applications, and platforms—often on-demand, negating the need to build and maintain an entire infrastructure team—to meet the exponential increase in business demand.
