January 23, 2012

Departmental analytics — general observations

Department-level adoption of analytic technology isn’t the exception; it’s the norm. Reasons include:

That said, arguments for centralizing analytic technology include:

What’s more, there are IT best practices to support department-level analytics. Some of the key ones boil down to:

My conclusion is that central IT should encourage (and aid) departmental analytics. Let’s look at some details.

I think two huge categories of analytic problem are inherently departmental:

Investigative analytics is a kind of research activity — you’re looking to discover previously unrecognized patterns. There are two approaches to this — you can do it in the department that has the relevant business knowledge, or you can outsource it to a special group of “discoverers” (commonly statisticians).* Either way, this is a small team/departmental kind of activity.

*Combining the two approaches is common — a department can have its own analytically adept discoverers, whether they’re call “quants” or just “business analysts”.

Reporting/monitoring BI at least has the potential to be enterprise-wide — but commonly it isn’t, as each department has its own operational data sources and metrics. Marketing departments may watch external data that the rest of the company doesn’t worry about. But it can be true across the board. Factory operations folks may track machine tool data the rest of us barely understand.

Even if a business need is strictly departmental, there can be at least two reasons to centralize technology implementation:

Whether those reasons hold up depends a lot on what kind of analytic scenario we’re talking about.

Let’s organize that part of this discussion in line with the taxonomy from my eight kinds of analytic database posts last July.

So in most cases I’d say: Departments can manage their own investigative data marts, and so of course can SaaS vendors and third-party data providers; other analytic databases should be run by central IT. (And of course, large departments with serious local IT can fuzz those distinctions up.) Beyond that, it would seem that whoever administers the database should administer the rest of the analytic stack as well.

That still leaves us with some practical questions, such as:


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