Microstrategy Blog

Business Intelligence is complicated.

IT people think that they have all of the answers for all of the problems, and that answer is “data”. Just give people the ability to drill through whatever it is that they want, the ability to pick whatever they want out of the giant metric bin of values, and they’ll solve problems, right?

In practice, it’s never that easy. The IT department seems to forget at time that while they might deal with the data, they aren’t the end user, and while they understand the warehouse, they don’t use it to make directional decisions. The response of some is just to throw it all out there, and have your user base sort through it, gleaning the pearls of wisdom that may or may not be there…don’t worry about the fact that accountants, finance department, managers, strategic planners, and executives have never used the tools before, and might not be technically inclined; that’s on them. Sometime the myopia of the data managers leads to poorly implemented systems.

In a great white paper by Kevin Quinn, VP of Product Marketing for Information Builders, he addresses this issue in depth.

He states:

Even a simple data warehouse has hundreds of columns of data, and it’s not uncommon for more complex systems to have thousands of columns. When an end user is faced with a blank canvas, thousands of columns of data, and hundreds of accessible features, complexity is automatic. “Where do I begin?” is often the first question, shortly followed by “I don’t have time for this,” or “I give up.”

It’s important to remember that while we might be quick to trumpet the value of in depth drill maps, predictive algorithms, and complex data mining tools, it’s the end user that uses the data on a daily basis to make the strategic decisions that a company requires. As a data warehouse and business intelligence developer, it’s not just our job to build systems, it’s also our job to take the time to ask the right questions. What works for the technically advanced, might be useless for the 90% of people who are not, and have neither the time, nor the drive to learn systems that might be our life blood.

In the 60′s, Theodore Levitt wrote the industry altering article Marketing Myopia where he criticized the marketing professionals of the time for their narrow understanding of the industries that they were in. Levitt concludes, “…the organization must think of itself not as producing good or services, but as buying customers, as doing the things that will make people want to do business with it.” As BI professionals the end user is our customer, let’s make sure in addition to focusing on the warehouse life-cycle, and data freshness, we take the time to make sure that our user base wants to do business with us. We achieve that by starting the conversation:

What do you need?

I haven’t been working with Microstrategy as much as I would like to lately. I’m still doing some consulting where I work with it, but for the most part, right now I’m tinkering with the nuts and bots of data warehousing, and discovering the wonder of PERL (I know, I know). I’m catching up on my Stephen Few reading, and I can’t believe that I missed this post. The money quote:

This morning, I was faced with a fresh reminder of the current state of data impoverishment. A reader invited me to visit Microstrategy’s website to see the finalists of its customer dashboard competition. What I found was depressing. I couldn’t find a single example of a dashboard that could be used to monitor information effectively. At best they could be used to look up a few facts when what’s actually needed is a rich set of comparisons. The problems that I found are too many to delineate, but all the dashboards suffer from a common flaw: they say too little and what they do say they say poorly.

Click through to view the dashboards he’s talking about here. He’s right, most of them are hideous…hence the title. It makes me think that the next time that they decide to have one of these competitions I should talk my clients into letting me jumble their data, and then share the Dashboard. Maybe I’ve read too much Stephen Few and Tufte in the past, but when I make Dashboard, I keep in mind what those dashboards are trying to achieve, who the user base is, what questions they have, and how that dashboard will answer those questions concisely.

Here’s where I think I depart from Few’s analysis.

I don’t blame Microstrategy’s customers. They’re working within the constraints of the tool and emulating impoverished examples, which is probably all they’ve ever seen. I mostly blame the folks at Microstrategy, who should know better. This competition gave the folks at Microstrategy a perfect opportunity to critique the designs that were submitted and show their customers how much better these dashboards could work if designed more effectively, assuming their software makes this possible. Did they miss this opportunity because they don’t know any better themselves?

I tend to speak in analogies, so you’ll have to forgive me, but it seems that what Stephen Few is doing is saying that if someone buys a car, and then promptly drives it into a brick wall, that the car salesman bear some sort of responsibility…not everyone wants to hang around and pay for training, and I don’t believe Microstrategy has some greater responsibility to offer it at no charge. Granted, I don’t think that Microstrategy should have given the “winner” award to who they did, and I also don’t think they should have “showcased” those Dashboards that just didn’t cut the mustard at all. However, what is Microstrategy supposed to do here? As a vendor, they don’t necessarily have the ability to be the critic without chasing away a customer base. I think the solution would have been to pick a few less candidates to display on the web site…or just stick with the professionally made charts and avoid a contest all together.

So the real question is how do we solve the problem of impoverished data?

This is a real breakthrough!

MicroStrategy has finally made its awesome online training available for everyone, at http://www.microstrategy.com/Education/Online/.

According to MicroStrategy training:

“A measurable value, often numeric and typically aggregatable, stored in a data warehouse.

A schema object representing a column in a data warehouse table and containing basic or aggregated numbers-usually prices, or sales in dollars, or inventory quantities in counts.”

How…technical.

Let’s explain it in terms the terms of an “every day life” transaction. If you bought shoes at the shoe store, the transaction involved a certain amount of “facts” and “attributes”. The facts in this case are “dollars, and quantity”. You gave something measurable, to get something measurable. Your fact is the base level of the values in the transaction that we later turn into metrics. The attributes in the transaction are almost limitless: the Converse All Stars, the shoe size, the store name, the person who you bought the shoes from, the time, day, and month that you bought them in. Metric are then built around facts. The metrics would be total dollars spent, average sales price, or the number of sales.

Simple enough.

I deal a lot with Consolidations, as any reader of this site will know, however, it seems the the MicroStrategy universe is a bit adverse to them…

Looking for explanations as to why? Is there a better way to be doing things? Are Consoldations just generally a no-no that lead to bigger problems?

Comment people…I’m actually soliciting opinions on this one.