Don't Rush to "Enterprise"

Tom Davenport, co-author of the 2007 book Competing on Analytics, is a great evangelist for the work we do here at Aventine Partners.  He does a great job in his book explaining what analytics is, what it can do for a business, and how the competitive environment is increasingly such that analytics is a must-have, not just a nice-to-have. 

However, there is one important area where we part ways, and I spotted it emphasized in a recent Direct Marketing Magazine summary of a Davenport talk at the NCDM conference this week.

Davenport has distilled and packaged some of his ideas into the acronym DELTA, which stands for:

• Data
• Enterprise
• Leadership
• Targets
• Analysts

The article is a good read for this nice summary of priorities, and everything on that list is great, except the "Enterprise" part.  In our experience, when you hear "enterprise", expect to soon learn about an enterprise-wide something that will be long, expensive, painful, and risky. 

This is really the area where I think Davenport doesn't make much sense.  In his otherwise exceptional book, he describes two paths a business can follow to build an analytics capability (see pp. 113 - 117). 

The first he calls "Full Steam Ahead".  It's the path of 100% commitment, led from the top, with unshakable faith in the power of analytics and the certainty that it is the key to domination of your chosen competitive space.

The alternative is the "Prove-It Detour".  Note, not the Prove-It "Path", but the "Detour".  Here's how Davenport introduces the options:

After an organization has realistically appraised its analytical capabilities, it must now choose which path to pursue.  Organizations blessed with top management commitment and passion for analytics can move quickly through the "full steam ahead" path, while the rest are forced to take the slower "prove it" detour . . .

To those already convinced of the benefits of analytical competition, having to avoid the fast track feels like an unnecessary detour.  Indeed, this path is much slower and circuitous, and there is a real risk that an organization can remain stalled indefinitely.  We estimate that having to "prove it" will add one to three years to the time needed to become an analytical competitor.  But executives unwilling to make the leap should take a test-and-learn approach -- trying out analytics in a series of small steps.

Can you guess which road he wants you to take? 

The problem is this just doesn't make any sense.  While we're talking about the virtues of gathering, analyzing, and understanding data to make better business decisions, he's saying not to apply the same philosophy to the decision to invest in the capability of gathering, analyzing, and understanding data. 

The practical reality is that every business will use analytics and create value from it in different ways.  Every organization will be in a different place culturally and politically to adopt these practices and change their behavior.  Taking time to get data to make better decisions about exactly where and how much to invest in this capability is the only sensible thing to do.

Davenport seems in his gut to understand this:

Despite its drawbacks, there are also important advantages to taking the slower path.  Any true analytical competitor wants to have a series of experiments and evidence documenting the value of the approach, and the prove-it path helps the organization accumulate that empirical evidence.  As managers get more experience using smaller, localized applications, they can gain valuable insights that can be translated into business benefits.  Each incremental business insight builds momentum within the organization in favor of moving to higher stages of analytical competitiveness.

There are practical reasons for taking the prove-it road as well.  By starting small, functional managers can take advantage of analytics to improve the efficiency and effectiveness of their own departments without having to get buy-in from others.  This approach also requires a lower level of initial investment, since stand-alone analytical tools and data for a single business function cost less than any enterprise-wide program.

By building a string of successes and carefully collecting data on the results, managers can attract top management attetion and executive sponsorship for a broader application of analytics.

Yes. Yes! YES!  That's exactly the way to think about it.  But this should be the default path, not the backup.  Having spent some time doing this work at firms that are also in the technology implementation business, we've seen too many examples of companies being led down the primrose path of spending years and millions building an "enterprise-wide" customer data management solution, only to find at the end that much of the investment was wasted because they hadn't thought through exactly how it would enable them to create value, or leadership changed, or business conditions changed.  In some very sad examples all they got out of the investment was some slick reports that could have been produced with Excel and some custom plug-ins.

So for those businesses and managers that are just getting going with analytics and don't want to take unreasonable chances with shareholder dollars and their own careers, here's an alternative approach I'll call the Five-S Path:

1. Small and Simple to Start.  Consistent approaches and processes across an enterprise is the right goal -- eventually.  But when you are starting something new, a controlled environment and the ability to contain risk is needed.  Enterprise is definitely not the place to start.  Get over the fears of "silos" and find a modest-sized group with a clear problem, a capable and willing manager, and some data to get started putting analytics to work.
2. Six Months to Value.  Who wants to make big investments with payback beginning in years two or three?  Well, that's what you're facing when you start talking enterprise.  Start small, and start with immediacy and urgency.  If you can't describe how you'll be doing specific things that create value within six months, that's too long.  Better yet, shoot for four.
3. Success Stories are Key.  Internal politics is a reality, and odds are high someone's pet project, or at least frame of reference, will be challenged by the results of an analytics endeavor.  The best defense against the naysayers and obstructionists are easily and frequently communicated success stories that tie data and analytics to value creation expressed in specific dollar terms.  The more stories, the better.  Will it be easier and less risky to generate many modest successes or one or two big ones?  You decide.
4. Staged Investments.  Wherever possible, break investments in capabilities and technolgoes into smaller pieces or states.  Sure, this might increase total time and cost somewhat, but it preserves critical flexibility and enables you to take advantage of new knowledge and experience as you make decisions about subsequent investment decisions.  Unless you're sure you have the analytics game all figured out, make modest investments and don't bet the farm.
5. Skills Trump Tech.  If you can spend $1 to develop skills or $1 to acquire technology, get the skills first.  The right skills can work around technology obstacles and shorcomings, but technology without skills is just a dumb box.  Unfortunately, most companies get this wrong because it's much easier to open the wallet for the persistent technology salesman.

Studying great analytics successes can make one want to get there yesterday.  But working with companies on this road, we know the biggest enemy of progress is often trying to do too much, too soon.  Opt instead to be pragmatic and control risk, and with perseverance, you are more likely to get to your destination.