When you first became aware of the business information warehouse concept, it was probably put forth as a tool of expedience—which it certainly is. And then you probably came to appreciate it as a radical repositioning of the end user in the information processing food chain.

But you may not have gone so far as to classify BI as a strategic instrument of paradigm-shifting importance. And this it can be, if you can bring your company’s management around to a new point of view. BI offers management a new strategic edge, a means of implementing business performance management—the dynamic measurement and pursuit of performance goals from nontraditional perspectives. The new breed of manager springing up in the corporate world touts this approach with increasing frequency. It’s up to you to provide the tools to make it a reality.

How is this achieved? It is achieved with timely, at-their-fingertips actionable intelligence, information that enables your management’s decision support system. The business information warehouse provides this, in the form of problem-specific, department-specific aggregations of data called data marts, along with a processing framework called analytics.

Life in the fast lane
The idea behind the business information warehouse is to tear down the traditional information processing bridge and build a new one. Where applications used to connect users to data, now the user is the connection between the data and the application. The idea is to create a storehouse of highly accurate and useful data that a user can access rapidly, flexibly, and easily, in order to facilitate a more responsive business environment based on evermore accurate forecasting.

Such a warehouse is obviously the culmination of decades of dreaming in the realm of business reporting. But why do we need such reports? We need them for analysis, of course, and for monitoring our business’s performance at the moment. And it is in this area that the warehouse becomes a true strategic advantage for your company. You must cultivate analytics, and you must model your system to accommodate them.

But even this is only a halfway measure. What is the analytical goal of a company that is doing performance management? It is to evaluate the performance of the company as a whole—as a dynamic, producing system—rather than as an aggregation of individual departments. To this end, we need to reconsider both our data warehouse and our approach to analytics.

Many companies implementing a data warehouse and analytic applications that make use of it do so with no forethought to coordination of those applications. This is fine for analyzing and monitoring the performance of a department, but it doesn’t do much for the company as a whole. By all means, have department-specific or business area-specific data marts and local analytics, but optimize your investment by coordinating your analytics and data marts across your entire operation. Here’s how.

Modeling for companywide coordination
What you need from your BI investment is a new paradigm for the use of information. You want to leverage the data your company has accumulated in order to optimize performance, companywide. While the department-by-department improvements realized by data mart/local analytic point solutions may be thought of as tactical activity, a companywide effort is, by definition, strategic: You want to use information in ways that changes the manner in which you do business. Let’s carry the use of military terms a step further and stop using the word information; instead, let’s call it intelligence.

In a military operation, intelligence is useless if it isn’t shared. If you’re a general trying to take the beach, you must coordinate the activity of amphibious troop carriers, naval support, air support, and deployed troops. Each of these individual units has information available to it that is locally useful. But that usefulness is severely curtailed if it is never shared (and, in particular, if it is never shared with you). Our first principle in strategic optimization of data warehouse intelligence, then, is:

Analytical intelligence gleaned from the data warehouse at the departmental level must be shared/available throughout the company.

What’s the hands-on step that makes this happen? Turn your data marts and local analytics into a business knowledge network. There are many approaches to this, and they all hinge on the software you’ve used to implement BI and your in-house network infrastructure. That’s detail work. Where you must put real planning is in the layer between the storage/communications technology and the data warehouse.

What exactly will you be passing around? This is where your attention will go. The information that should be passed between departments/business units and made available to the highest levels of management is that which meets any or all of these criteria:

  • Is it “active” information? (i.e., is it current and reliable, and do decisions hinge upon it?)
  • Does it affect performance at the departmental level, or is it information that describes performance in such a way as to affect decisions to be made at any other level or for any other department/business unit?
  • Does the information contribute to the performance measure or real-time monitoring of the company’s high-level performance goals?
  • Can the information be indicative of current or impending interruptions or degradations in either departmental or company performance?
  • Does the information affect the timeliness of performance or the response by any department or the company in general?

Once you’ve determined, department-by-department, what information feeds decision making in this manner, you have essentially classified the core intelligence knowledge for your company’s decision support system. You must now formalize the regular and timely generation of this information, using data marts and analytics, at the departmental level. This you will leave in the hands of the people who own the data (that’s why you have a data warehouse, so that they can do for themselves—just make sure they do it).

Your next step is to set up a notification system that will pass intelligence from department to department, making your on-the-fly intelligence metrics a de facto distributed system. For example, when sales determines that there’s increased demand for a product in the marketplace, the system will passively notify production, warehousing, logistics, and senior management. These notifications will almost always be to multiple recipients, because most business activity, viewed from a high level, is defined not by departmental activity but by interactions between departments.

Your second major initiative, then, is:

Make departmental performance metrics part of a distributed, companywide system by building in means of notification to other departments when data indicates an increase or decrease in performance.

Talk to the boss
Next, you’ll need a set of high-level analytics for intelligence passing from the department level to senior management. These will generate comparative data for performance metrics defined by the company’s performance as an integrated whole. In addition, you need to configure projected vs. actual metrics monitors, data displays easily understood by decision makers at high levels, to be fed by these integrated metrics.

These high-level analytics are key to your success. There are several important principles to keep in mind while they are being generated:

  • They are not defined by senior management. Rather, senior management tells you what must be measured in order for companywide performance to be effectively optimized, and you will seek out the functional parameters at the departmental level; to this end, you need an expert from each area helping to define these high-level analytics at every step.
  • No one person can tell you every factor that positively or negatively impacts company performance, yet that’s what these analytics propose to capture; you must effectively bring together your mid-level and low-level experts and let them interact extensively in order to meaningfully define these analytics.
  • They must be extensively tested; since they are part of a system intended for highly-specific forecasting, it will be important to define performance evaluation for comparative purposes at the departmental and senior management levels and to assign the appropriate parties to oversee this, for the period during which the efficacy of these high-level analytics is being determined.

Your final objective, then, is:

Create high-level analytics that will present senior management (or anyone who cares to keep track) with accurate, timely metrics for evaluating overall business performance.

Of course, this is all much easier said than done. You’ll probably put in as much meeting time and haggling over details as you would for a major conversion or implementation. The good news is that this isn’t a major conversion or implementation; you’re putting in lots of people time, to be sure, but it’s time that you’ll recover in business performance and you’ll realize those performance gains rapidly. The happy ending to the story is that it’s not going to cost you millions in new software or an overhaul of old systems:

You can build such a structure on top of what you already have, using tools you already possess.

You don’t need to reinvent the wheel or rebuild what you’ve already built. Everything described above is an add-on to what you already have in place, if you’ve designed a non-centralized, highly-granular data warehouse (as most data warehouses should be). If you’ve already invested in the data warehouse, you’re more than halfway home. And if you haven’t considered taking that investment up to this new level, consider how you’ll appear to senior management when you offer a magic wand like this, and tell them they’ve already paid for it; it’s just a matter of putting it to use.