This is the second in a four-part taxonomy of analytics based on how the analytic work product is used. In the first post of this series, I covered Strategic Analytics, or analytics that support the C-suite. In this post, I will cover Managerial Analytics: analytics that support middle management, including functional and regional line managers.
At this level, questions and issues are functionally focused:
- What is the best way to manage our cash?
- Is product XYZ performing according to expectations?
- How effective are our marketing programs?
- Where can we find the best opportunities for new retail outlets?
There are differences in nomenclature across functions, as well as distinct opportunities for specialized analytics (retail store location analysis, marketing mix analysis, new product forecasting), but managerial questions and issues tend to fall into three categories:
- Measuring the results of existing entities (products, programs, stores, factories)
- Optimizing the performance of existing entities
- Planning and developing new entities
Measuring existing entities with reports, dashboards, drill-everywhere (etc.) is the sweet spot for enterprise business intelligence systems. Such systems are highly effective when the data is timely and credible, reports are easy to use and the system reflects a meaningful assessment framework. This means that metrics (activity, revenue, costs, profits) reflect the goals of the business function and are standardized to enable comparison across entities.
Given the state of BI technology, analysis teams within functions (Marketing, Underwriting, Store Operations etc.) spend a surprisingly large amount of time preparing routine reports for managers. (For example, an insurance client asked my firm to perform an assessment of actual work performed by a group of more than one hundred SAS users. The client was astonished to learn that 80% of the SAS usage could be done in Cognos, which the client also owned).
In some cases, this is simply due to a lack of investment by the organization in the necessary tools and enablers, a problem that is easily fixed. More often than not, though, the root cause is the absence of consensus within the function of what is to be measured and how performance should be compared across entities. In organizations that lack measurement discipline, assessment is a free-for-all where individual program and product managers seek out customized reports that show their program or product to the best advantage; in this environment, every program or product is a winner and analytics lose credibility with management. There is no technical “fix” for this problem; it takes leadership for management to set out clear goals for the organization and build consensus for an assessment framework.
Functional analysts often complain that they spend so much time preparing routine reports that they have little or no time to perform analytics that optimize the performance of existing entities. Optimization technology is not new, but tends to be used more pervasively in Operational Analytics (which I will discuss in the next post in this series). Functionally focused optimization tools for management decisions have been available for well over a decade, but adoption is limited for several reasons:
- First, an organization stuck in the “ad hoc” trap described in the previous paragraph will never build the kind of history needed to optimize anything.
- Second, managers at this level tend to be overly optimistic about the value of their own judgment in business decisions, and resist efforts to replace intuitive judgment with systematic and metrics-based optimization.
- Finally, in areas such as Marketing Mix decisions, constrained optimization necessarily means choosing one entity over another for resources; this is inherently a leadership decision, so unless functional leadership understands and buys into the optimization approach it will not be used.
Analytics for planning and developing new entities (such as programs, products or stores) usually require information from outside of the organization, and may also require skills not present in existing staff. For both reasons, analytics for this purpose are often outsourced to providers with access to pertinent skills and data. For analysts inside the organization, technical requirements look a lot like those for Strategic Analytics: the ability to rapidly ingest data from any source combined with a flexible and agile programming environment and functional support for a wide range of generic analytic problems.
In the next post in this series, I’ll cover Operational Analytics, defined as analytics whose purpose is to improve the efficiency or effectiveness of a business process.