Making Smarter Decisions: Five Business Intelligence Myths

Flickr Photo: bredgarIf you're a frequent reader of this blog, you know that if I could do it all over again, I would have paid more attention in math class. Though my high school calculus class actually made me break out in hives -- it was the most stressful experience of my life -- I adore data. I want to eat statistics for dinner. I would marry a good data visualization. Mashups make me want to sing!

I love this stuff because, to me, data done right is empowerment. I look at it this way: a piece of data can tell you where you are, today. If you collect and analyze data over time, it can tell you where you're headed, tomorrow -- and that can help you make better business decisions. 

That's when data becomes intelligence. 

The field of Business Intelligence (BI) is growing by leaps and bounds as technology tools make getting, combining, and visualizing data easier than ever before. I'd like to stress that even though it has a fancy name and is used at large corporations, BI is something we all can do, no matter the size or scope of our organization.

Really, it's something we all MUST do, because it will help us serve our stakeholders better.

I hear from folks in the field that we don't do more with BI because it seems complicated and expensive. But if little ol' NTEN can pull off a BI strategy -- and we do our best over here -- so can you.

Let's bust a few BI myths.

Myth #1: BI is Super Complicated

BI is just a framework for agreeing on and delivering the data you need to make better decisions in your organization. 

It's not just a random assortment of data, it's the stuff that tells you if you're meeting your key goals and objectives. Importantly, BI is also multi-layered: A good BI strategy will not only tell you if you're meeting your goals, but how and why.

Here's a very simple example: You may have a goal of distributing 10,000 copies of a report via your web site. A basic BI strategy would certainly provide a way to tell you how many reports have been downloaded. A good BI strategy would also tell you what sources prompted those downloads (direct emails from you, advertising in online journals, etc.) so that you can make decisions about where to invest your recruitment resources.

It's all about mapping data to business needs to make better decisions. Our friends at NetSquared have a great podcast interview with Steve Williams of Business Objects that provides more good examples.

Myth #2: BI is Expensive

Like anything you undertake, actually implementing a BI strategy is going to cost you something. Even though you DON'T have to buy expensive tools if your needs are fairly simple, it will cost you in staff time. You'll need a good chunk of time to develop the strategy, and then ongoing time to implement the strategy.

BI is about helping you make better decisions, though, right? So if you're actually making better decisions, you should save a heck of a lot of time over the long haul by not implementing things that just don't work. Like everything in life -- and against all human nature, it seems -- we need to make upfront investments to see long term payoffs.

What you don't need, especially as you're just getting started, is really expensive software. It's out there, but you can do good, solid BI with a little fortitude and a lot of Excel.

Myth #3: Dashboards = BI 

Here at NTEN, we confused the two for a long time. We had a dashboard that told us things like how many members we had and how many webinar registrations we'd sold. That's data, not intelligence. 

Now our dashboard tells us where we are against our membership goals for the year, and where we projected to be at this date. It's easy for us to see if we need to take action in any particular area to bring numbers up.

More importantly, there are lots of worksheets behind our dashboard that let us know more about why we might not be meeting a goal. That's the stuff that helps us make better decisions about what to do next.

Myth #4: Our Staff Tracks What They Need. We're Fine.

You're not fine, I promise you, for two reasons.

First, I guarantee you not everyone in your organization tracks things in the same way. So, when you sit down in that senior management meeting, you're comparing kittens to Pop Tarts across all your departments. What one person counts as a "client interaction" may be completely different from another person's. Part of a BI strategy is agreeing on definitions for exactly what you're tracking, and how you'll track it. You need to come up with master data definitions and a rigorous vocabulary. Even if this is all you do, you've made huge strides. 

Here's the second problem: As Gartner put it in a webinar I attended, "managers want to dance with their data." In other words, managers love to come up with their own ways to view and analyze data related to their departments. Why? Well, a lot of times, they want to be able to make that data look as positive as possible. BI requires a level of internal transparency that many managers may find uncomfortable in a paradigm where she who controls the spreadsheet controls the perception.

Myth #5: BI is About Technology. If I Buy the Right Software, I'm Set

Preposterous, I say! I asked some folks on Twitter today if they had a BI strategy and what their biggest challenges were.

No one said the biggest challenge was the software. 

In fact, some said the software was the easiest part. Here are the responses I got:

@peterscampbell said:  Biggest BI challenge is that data needs to be input in order to be analyzed. Investment, accountability, quality control.

@jonstahl said:  garbage in, garbage out, like @peterscampbell says.

@johmmerritt said:  BI = very broad term encompassing assets: people, data, skills, tech. Challenges often reside in identification, classification

As with anything, the biggest challenges in doing BI right are going to be those pesky people you work with. You're going to have to convince a whole bunch of people of the importance of:

• Data quality. For BI to work, you need good data. Since 80% of all data is unstructured, and most of the rest is input by humans, it's up to the people you work with to do it right. 
• Definitions. You're going to have to get a variety of stakeholders to agree on what's important to track, and how to track it. They'll have to spend time figuring it out with you.  
• Ongoing support. Business Intelligence is, by definition, iterative. As the needs and goals of your organization change, so too will your BI strategy. This will require an ongoing investment from the people in your organization.
• Collaboration. You need to involve folks with a variety of skills and roles in the organization to make BI work. You need analysts to arrive at consensus on definitions and metrics in the organization. You need IT folks who understand how data is structured, what queries are possible, how data modeling works. You need leadership skills to drive adoption and participation across the organization. Close collaboration is always easier said than done.

What about you? Do you have a BI strategy at your organization?  What challenges do you face and how do you tackle them?