You know you need a data warehouse. There’s no doubt in your mind. Your competition is out-forecasting you in anticipating market trends. Other companies are faster to respond than yours in an increasingly Web-driven marketplace, and you aren’t getting the efficiency gains you need to catch up. Your various departments are pinging your production system to death with queries and report requests. You need a data warehouse.
As you march down the hall to meet with your fellow decision-makers, do you know what you’re going to say? How do you sell this idea? How do you convince them to spring for this shapeless, faceless thing that has no explicit price tag and no explicit benefit? Here are some pointers.
Return on investment? Forget it.
The executive mindset of ROI-as-decision-driver isn’t going to work when you present the plan for implementing a data warehouse. Why not? Because the production efficiency and the increased market savvy that a data warehouse delivers are not things you’ve ever measured. You can’t offer a projected ROI. You can’t quantify the benefits because you’ve never experienced them in such a context. Indeed, the very reason you want a data warehouse in the first place is to develop the metrics to create a business intelligence capacity that would teach you how to explicitly measure “increased market savvy.” You’re asking for the time and money to develop an instrument that will measure itself.
You can’t even describe what you’re ultimately going to measure, once you’ve developed the analytics and performance-monitoring capability you’re seeking. Why is this? Because your high-level business performance metrics can be based on finely-detailed, lower-level metrics, and these are the province of analysts you’re going to grow in-house, at the departmental level. Only they know what their drivers will be, and even they won’t really know what the magic numbers are until they create them.
This all sounds very mystical at face value, and it is, but it’s magic that works. Here’s what you can say with confidence, and how to say it. While there are no numbers attached, it is a logical case that has no reasonable refutation.
Many-for-one, among others
Here’s what you get for your money, whatever the cost turns out to be:
- Multilevel trend analysis. Your financial people and sales and marketing forces will acquire the capacity to define and analyze trends at every level, from the entire market down to age-groups-by-region or any other fine level that matters. And they will ultimately control the level of precision of their forecasting, because they will control the quality of the data going in and the resolution of the measurements.
- Company-wide performance monitoring. This same style of analysis can be applied at the department level, business unit level, and company-wide. You can develop, and continually refine, metrics that will allow you to continuously evaluate your company’s performance.
- User-defined, user-controlled reporting. This one is highlighted to make sure you don’t jump over it, because it sounds mundane. But there is no overstating the incredible value of this capability—and, moreover, it’s the justification you’ve been looking for.
Making your case
Consider the operational reporting systems you have in place. You need look no further than Orders for an example. A mass of reports issue from this system, going to many different individuals in a number of departments. It’s often the case that any one of these users is grabbing transactional orders, data from Order History, as well as data from different databases altogether (Customer tables, etc.) in order to assemble the information required. What’s wrong with this? Well, in the first place, since the reports are largely static, and since the information is often in different databases requiring multiple queries, it’s expensive to operate this way. And this is before we even factor in the cost of developing new reports.
Your choice, then, is one-for-one application investment vs. many-for-one. That is, you need to make clear to your executive decision-makers that the ultimate take-home point of data warehouse implementation is that you’re giving your user community a single application that yields the results of many applications. The money you’d spend implementing one major new application can be spent on a data warehouse, and a great many powerful applications will spring up. Isn’t this the kind of bargain we all shop for?
This, incidentally, leads you to a fourth benefit, tailor-made for your peers: a data warehouse enables an Executive Information System. An EIS is an application that delivers, in digest form, any information an executive needs for decision support. The philosophy behind such systems is that typical executives do not fall among the users described above: There ‘s no way they’re going to go to four or five sources to cobble together the data they need to do their jobs. They want whatever they can get, information-wise, by going to as little trouble as possible to get it. For such users, a data warehouse is a dream come true.
Who gets the bill?
If you can’t sell this, then they just aren’t buying. A data warehouse is going to ultimately be a bargain and a powerful strategic tool that will give your company a competitive edge. And while you can’t offer a very solid cost figure, you can safely say it’s going to be more than $100,000 and that it is unlikely to reach seven figures.
What are you buying? If you’re on an ERP platform (SAP R/3, Oracle, PeopleSoft), you can buy a development kit that will give you the tools you need to extract and load data, and to develop data mining and analytic applications. If you’re not, the principles remain the same. You’re buying storage (you already know about this), extract-transform-load (ETL) software for putting data into the warehouse, and software for data mining, and for analytics (Online Analytical Processing, or OLAP). Beyond this, it’s a people process: You’re going to pay for the on-the-job training of local users of this analytical function, and for the trial-and-error development of reports by many users who can make use of this new analytical vs. transactional mindset (these will all be discussed in more depth in future articles).
You’ll need to bottom-line it, and the bottom line is just what everyone wants to hear: with data warehousing, your user community will be able to do more with less.