Analytics - The New Path to Value - Keep Existing Capabilities While Adding New Ones - MIT Sloan Management Review

Analytics: The New Path to Value: How the Smartest Organizations Are Embedding Analytics to Transform Insights Into Action is an excellent research report from the 2010 New Intelligent Enterprise Global Executive Study and Research Project conducted by MIT in association with the IBM Institute for Business Value.

Here is part 6 of the Report chosen because it deals with insights, modelling and visualisations - the basis of the Creative Leadership Forum's Management Innovation Index

When executives first realize their need for analytics, they tend to turn to those closest to them for answers. Over time, these point-of-need resources come together in local line of business units to enable sharing of insights. Ultimately, centralized units emerge to bring a shared enterprise perspective — governance, tools, methods — and specialized expertise. As executives use analytics more frequently to inform day-to day decisions and actions, this increasing demand for insights keeps resources at each level engaged, expanding analytic capabilities even as activities are shifted for efficiencies. (see Figure 9.)

Figure 9

The frequency with which analytics is used to support decisions increases as organizations transition from one level of analytic capability to the next. At the same time, analytics migrate toward more centralized units, first at the local line of business level and then at the enterprise level, while the portion of analytics performed at points-of-need and with IT remain stable.

Sophisticated modeling and visualization tools, as we have noted, will soon provide greater business value than ever before. But that does not mean that spreadsheets and charts should go away. On the contrary: New tools should supplement earlier ones, or continue to be used side by side, as needed.

There are other ways that capabilities grow and deepen within an organization. Disciplines like finance and supply chain are inherently data intensive, and are often where analytics first take root. Encouraged by early successes, organizations begin expanding analytic decision making to more disciplines. In Transformed organizations, reusability creates a snowball effect as models from one function are repurposed into another with minimal modifications.

Over time, data-driven decision making branches out across the organization. As experience and usage grow, the value of analytics increases, which enables business benefits to accrue more quickly.

How Analytics Propagates Across Functions

Typically, organizations begin with efficiency goals, then address growth objectives, and lastly, design finely tuned approaches to the most complex business challenges. As this occurs, adoption both spreads and deepens. This contributes to a predictable pattern of analytics adoption by function. (see Figure 11.)

Figure 11

Analytic adoption spreads through organizations in a predictable pattern, as all respondents gained proficiency with functional analytics in the same order. The rate of adoption, as shown through proficiency, increases steadily and threshold levels support the analytic capability tiers.

Specifically, we found the following:

Aspirational. About one-half used analytics for financial management, about one-third each for operations, and sales and marketing. These selections reflect the traditional path of adopting analytics in inherently data-intensive areas.

Experienced. Analytics used for all of the above, and at greater levels. For example, the proportion of respondents likely to use it for finance increased from one-half to two-thirds. New functions, such as strategy, product research and customer service, emerged. Growth and efficiency were both met with analytics approaches.

Transformed. Analytics was used for all the same functions as above — and more, as the branching pattern spread within organizations. Fine-grained revenue and efficiency usage of analytics emerged, such as customer experience, to build on customer service and marketing capabilities.

These patterns suggest that success in one area stimulates adoption where analytics had not previously been considered or attempted. That is, in fact, how organizations increase their level of sophistication. Successful initiatives in supply chain functions, for example, encourage the human resources function to institute a pilot for data-driven work force planning and allocation.

While these findings describe the typical path, they are not necessarily the best or only one. Analytic leaders may want to advance their organization’s capabilities more quickly using nontraditional routes.

Add value with an enterprise analytics unit Organizations that first experience the value of analytics in discrete business units or functions are likely soon to seek a wider range of capabilities — and more advanced use of existing ones. A centralized analytics unit, often called either a “center of excellence” or “center of competency,” makes it possible to share analytic resources efficiently and effectively. It does not, however, replace distributed and localized capabilities; rather, the central unit is additive, built upon existing capabilities that may have already developed in functions, departments and lines of business.

We found that 63 percent more Transformed organizations than Aspirational organizations use a centralized enterprise unit as the primary source of analytics. A centralized analytics unit can provide a home for more advanced skills to come together within the organization, providing both advanced models and enterprise governance by establishing priorities and standards by:

• Advancing standard methods for identifying business problems to be solved with analytics
• Facilitating identification of analytic business needs while driving rigor into methods for embedding insights into end-to-end processes
• Promoting enterprise-level governance on prioritization, master data sources and reuse to capture enterprise efficiencies
• Standardizing tools and analytic platforms to enable resource sharing, streamline maintenance and reduce licensing expenses.

In three distinct areas — application of analytic tools, functional use of analytics and location of skills — we found that adding capabilities without detracting from existing ones offers a fast path to full benefits from analytics-driven management.

IBM CASE STUDY: Bridging Business and Analytics Skills Across the Organization

As is often the case, analytics success raises the bar to do more. As demand for useful insights has grown, a leading big-box retailer developed a sophisticated analytics environment, in which each layer — enterprise, business unit and point of need — complements rather than duplicates the specialized skills each location delivers.

Determined to leverage the structures already in place, but push them to the next level, the retailer set out to strengthen both the analytics and business skills of its practitioners. Already, analysts were working within the lines of business, knowledgeable enough to supply timely answers to ad hoc queries raised by business executives. An enterprise-wide unit also provided complex computational skills as needed, created common data definitions and crafted analytics approaches that could be duplicated across the business units.

The central unit housed the advanced analytics skills, but it was the analysts in the business units who had the advanced business knowledge and a deep understanding of the operations, objectives and economic levers required to run the business. Still lacking was the ability to bridge these two domains.

Business unit analysts now rotate into the enterprise unit, partnering with high-tech analysts to provide the business knowledge that fuels new analytics models and working collaboratively to analyze and interpret results that will be meaningful to business. At the end of the rotation, business unit analysts return with a standardized tool kit to create consistency and rigor in analysis and facilitate sharing.

This is part 6 of 10 from the 2010 New Intelligent Enterprise Global Executive Study and Research Project.