There is no question that business analytics is now mainstream. Every business uses analytics in some way and many have thoroughly integrated analytics into their decision and management processes.Creation and use of analytics has migrated from being primarily business intelligence (BI) and IT activities to something that permeates every part of the business. The Excel jockeys in businessdepartments have become the “new breed of business analyst” described by Ted Cuzzillo in a recent article.
The underlying research for Cuzzillo’s article suggests that analytics has moved from BI-centric to departmental, distributed and sometimes personal. Yet at the same time, we see analyticsintegrated into new architectures such as the decision intelligence architecture described by Claudia Imhoff and Colin White. Inthis architecture, they classify analytics as event analytics and content analytics.
So simultaneously we see the reach of analytics extending deeper into the business, the range of analytic uses expanding into new areas, and the definition and structure of analytics growing incomplexity. At the same time, we see demand for agile analytics, interest in collaborative analytics and a trend toward commoditization of analytics.
Ask any business “What is your analytic capability?” and it is likely that they will be unable to answer the question – perhaps even unable to understand the question.
The Capability Maturity Model
To describe or quantify analytic capability in any meaningful way, we need to begin with a capability model. The SEI Capability Maturity Model from Carnegie Mellon University provides a goodfoundation because it emphasizes capability first. Other models present themselves as maturity models, but without the strong capabilities focus that is needed here.
The CMM, in fact, makes a distinction between capability levels and maturity levels as shown in Figure 1. Note that while the names for capability and maturity levels are similar at some levels,there are subtle but significant differences. Maturity levels are defined and measured as an aggregate for all processes in an enterprise. Capability levels are defined and measured for particularand targeted processes. Understanding process capability is important for process improvement, process integration and evolution to new collaborative processes.
The capability levels are defined as:
Level 0 – Incomplete: An incomplete process is one that is not performed or is only partially performed. Specific process goals are not consistently met and no enterprise goals exist for the process.
Level 1 – Performed: A performed process is one that consistently meets specific goals of the process area. It supports and enables the work needed to provide the services of the process area. Although an improvement over Level 0, performed processes are at risk due to operating without a strong connection to enterprise goals.
Level 2 – Managed: A managed process satisfies Level 1 criteria and has the basic infrastructure needed to support the process. It has enterprise goals as well as process area goals. The process is consciously planned and executed, employs skilled people, has adequate resources and involves key stakeholders. A managed process is monitored, controlled and reviewed.
Level 3 – Defined: A defined process satisfies Level 2 criteria and has the necessary degree of rigor in standards, process descriptions and procedures to be learnable, repeatable, easily audited, consistent in results and capable of producing identical results given identical circumstances.
Level 4 – Quantitatively Managed: A quantitatively managed process satisfies all Level 3 criteria, and is controlled using statistical and other quantitative techniques. Measurable targets of quality and performance are established and used to manage the process. Quality and performance are measured and managed throughout the life of the process.
Level 5 – Optimizing: An optimizing process meets all Level 4 criteria and is continuously improved through analyzing and understanding the causes of variation in the process. Statistical Process Control (SPC) methods are used to achieve both incremental and innovative improvements.
The Analytic Capability Model
The CMM described above is a good starting place, but not sufficiently specific to be applied to analytic processes. To build an analytic capability model, we need to add the dimensions that arespecific to analytics: analytic roles and analytic uses. Figure 2 adds roles to the capability model using the two primary roles that occur in analytic practice: using analytics and creatinganalytics. It is important to realize that the same individual may perform both roles – as an analytic consumer and an analytic developer.
At the intersection of each role with each level, it now becomes practical to identify specific characteristics that are indicative of that intersection. As you consider the descriptions of thecapability levels above, read the word “process” in all of its occurrences as “analytic process” – the processes by which we create and use analytics.
Using analytics at Level 0 (incomplete), for example, might involve drawing conclusions and making decisions based on a partially finished spreadsheet that may never be completed. Similarly, creatinganalytics at Level 0 might entail building that unfinished spreadsheet that was perhaps “good enough” at the time. At the opposite extreme, Level 5 analytic usage could be application ofa performance scorecard
for a BI
program to continuously align business intelligence with the business. And analytic creation at Level 5 might be use of metrics to monitor and improve data andinformation quality in a BI program.
Adding the analytic usage dimension produces the capability model shown in Figure 3. The usage dimension looks at the purpose for analytics – the kinds of analyses that are performed. Theseinclude performance management, behavioral analysis, prediction and forecasting, cause-and-effect analysis, exploratory analysis and discovery, and text and spatial analytics. Each of these is ananalytic process, thus a subject of capability assessment.
In Conclusion (or until the next article)
This completes a top-level view of a capability model for business analytics. The model has eighty-four cells, each of which needs to have detailed criteria that express the indicators and normsby which analytic capability can be measured. Adding those criteria includes a look at analytic architecture and infrastructure, encompassing topics such as business capabilities, technologicalcapabilities, organizational infrastructure, data infrastructure, system integration and more. But that’s a topic for the second article in the series.
Using the model is also a topic for a future article. It is not my intent to suggest that we should all aspire to Level 5 for all of our analytics, nor that all analytic processes need to achieve thesame capability level. The purpose is to know the level of capability that you desire – to set a goal – and then to pursue that level with tools to measure progress. Achieving thatpurpose requires classification, structure and indicators – in short, a capability model. Using the model requires data gathering, analysis and quantification, which are topics for the finalarticle of this series.
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