Tag: Agile Analytics

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.

Conversations about analytics tend to get muddled because the word describes everything from a simple SQL query to climate forecasting.  There are several different ways to classify analytic methods, but in this post I propose a taxonomy of analytics based on how the results are used.

Before we can define enterprise best practices for analytics, we need to understand how they add value to the organization.  One should not lump all analytics together because, as I will show, the generic analytic applications have fundamentally different requirements for people, processes and tooling.

There are four generic analytic applications:

• Strategic Analytics
• Managerial Analytics
• Operational Analytics
• Customer-Enabling Analytics

In today’s post, I’ll address Strategic Analytics; the rest I’ll cover in subsequent posts.

Strategic Analytics directly address the needs of the C-suite.  This includes answering non-repeatable questions, performing root-cause analysis and supporting make-or-break decisions (among other things).   Some examples:

• “How will Hurricane Sandy impact our branch banks?”
• “Why does our top-selling SUV turn over so often?”
• “How will a merger with XYZ Co. impact our business?”

Strategic issues are inherently not repeatable and fall outside of existing policy; otherwise the issue would be delegated.   Issues are often tinged with a sense of urgency, and a need for maximum credibility; when a strategic decision must be taken, time is of the essence, and the numbers must add up.   Answers to strategic questions frequently require data that is not readily accessible and may be outside of the organization.

Conventional business intelligence systems do not address the needs of Strategic Analytics, due to the ad hoc and sui generis nature of the questions and supporting data requirements.   This does not mean that such systems add no value to the organization; in practice, the enterprise BI system may be the first place an analyst will go to seek an answer.  But no matter how good the enterprise BI system is, it will never be sufficiently complete to provide all of the answers needed by the C-suite.

The analyst is key to the success of Strategic Analytics.  This type of work tends to attract the best and most capable analysts, who are able to work rapidly and accurately under pressure.  Backgrounds tend to be eclectic: an insurance company I’ve worked with, for example, has a strategic analysis team that includes an anthropologist, an economist, an epidemiologist and graduate of the local community college who worked her way up in the Claims Department.

Successful strategic analysts develop domain, business and organizational expertise that lends credibility to their work.  Above all, the strategic analyst takes a skeptical approach to the data, and demonstrates the necessary drive and initiative to get answers.  This often means doing hard stuff, such as working with programming tools and granular data to get to the bottom of a problem.

More often than not, the most important contribution of the IT organization to Strategic Analytics is to stay out of the way.  Conventional IT production standards are a bug, not a feature, in this kind of work, where the sandbox environment is the production environment.  Smart IT organizations recognize this, and allow the strategic analysts some latitude in how they organize and manage data.   Dumb IT organizations try to force the strategic analysis team into a “Production” framework.  This simply inhibits agility, and encourages top executives to outsource strategic issues to outside consultants.

Analytic tooling tends to reflect the diverse backgrounds of the analytics, and can be all over the map.  Strategic analysts use SAS, R, Stata, Statsoft, or whatever to do the work, and drop the results into Powerpoint.  One of the best strategy analysts I’ve ever worked with used nothing other than SQL and Excel.  Since strategic analysis teams tend to be small, there is little value in demanding use of a single tool set; moreover, most strategic analysts want to use the best tool for the job, and prefer to use niche tools that are optimized for a single problem.

The most important common requirement is the capability to rapidly ingest and organize data from any source and in any format.  For many organizations, this has historically meant using SAS.  (A surprisingly large number of analytic teams use SAS to ingest and organize the data, but perform the actual analysis using other tools).    Growing data volumes, however, pose a performance challenge for the conventional SAS architecture, so analytic teams increasingly look to data warehouse appliances like IBM Netezza, to Hadoop, or a combination of the two.

In the next post, I’ll cover Managerial Analytics, which includes analytics designed to monitor and optimize the performance of programs and products.