Page 129 - DCAP208_Management Support Systems

Management Support Systems

Notes trying to get just the right data set or they want to understand just one more thing about the
process before they jump in. Please go ahead and dive in. And as you analyze the results,
consider the Do’s and Don’ts list for refining the process.
Don’t second-guess your analytic results: This happens time and again. A company will
invest in analytics but not trust the results. This often occurs when organizations fail to get
executive buy-in prior to rolling out an extensive analytics initiative. But failing to stick to
what the analysis suggests renders your efforts moot. If all you do is say “When it matches
the hypothesis we will run with it. When it doesn’t we will override it,” then you are not
using analytics. And yes, sometimes it is hard to stick to your guns – particularly if the
recommendation is a little uncomfortable or different from what your organization
traditionally does. The key is this: When you can’t follow recommendations that go against
traditional thinking, analytics just becomes a layer that reinforces conventional wisdom –
not something that helps your organization grow.

Do respect the creative elements of analytics: An organization can go too far in assuming
that analytics is a pure science. It’s not. There is science involved in building a model, but
questions like “What is the right thing to predict?” and “What factors are needed to build
the model?” require the artistic and creative efforts of business users who think about
these problems daily. When we talk to companies about model building, we emphasize
the need to bring the scientists together with the non-scientific business people. Doing
that ensures that the analytics address the right problem in the best possible way.
Don’t be afraid of new data sources: We’ve leapt from having a household file with
demographics to factoring in transactions and other data points. Leading-edge companies
are adding Web interaction information like what the customers are looking at on the
Web, what reviews they are reading, what product pictures they are zooming in on and
what search terms they used to get to the site. This data is important to understand what is
going on inside a customer’s head before they make a purchase. In addition, sensors and
RFID data are critical new data sources, particularly relating to supply chains, transportation
and manufacturing. There are countless other data sources arising across all industries. In
fact, there are so many that the term “big data” has become popular these days as a catchall
term for the wealth of new, large data sources. The more you can take every one of these
pieces and stitch them together, the more you’ll know about your customers and processes.
Organizations that do this will be far more successful than those sitting back frightened
about incorporating new data sources.
Do stay on the cutting edge: Along with embracing new data sources, organizations need
to embrace new ways of looking at data. This might include looking at cost-effective ways
to speed model processing, pursuing additional modeling techniques or improving the
way analytic results are distributed to users. The last thing you want is to have your team
training a new person and starting the conversation with “Here’s how we do it here. This
is how you’ll do it too.”

Notes It is important to have standard procedures and approaches, but you also need to
regularly challenge them and ensure there isn’t room for improvement. After all, you
won’t be the leader if you are simply copying what everyone else is doing.
Don’t expect one person to lead the charge: We have seen companies – typically the industry
laggards in using analytics – decide that they are going to hire one person to handle all
their analytic needs. After a year or two, when this poor, beleaguered soul has not single-
handedly transformed their business, they decide that “predictive analytics doesn’t work.”

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