never been a time in history when the opportunities have been so great for a
small business to build a competitive advantage with information.

The convergence of several mega-trends such as
social media and consumerization of technology creates terrific opportunities
for small businesses who embrace big data in their competitive strategy. To be
clear, when used for competitive reasons, I define big data as: the massive amount of rapidly moving and
freely available data that potentially serves a valuable and unique need in the
marketplace, but is extremely expensive and difficult to mine by traditional means.
Big data can be incorporated into your strategy in three different ways: as a
core product or service (e.g., Greenplum), in support of a core product or service
(e.g., Progressive Insurance), or as a key capability that’s built within your
organization to support your strategy (e.g., analytic-driven organization).

Regardless of your approach, you need to take a couple of steps in preparation. First, build a
strong business case that clearly articulates the threshold at which an
investment in big data doesn’t make sense anymore. For instance, if your
strategy has an upside potential of $2 million, it doesn’t make sense to spend
$5 million on a huge data science team.

Second, make sure you have a good
execution framework in place that favors flexibility. Adopt a philosophy of
adaptability so you can move in lockstep with a sometimes erratic marketplace. It’s
also a good idea to publish a scorecard so everyone in the organization has
direct access to what you consider important and how your strategy is
progressing. You’ll probably need to bring in outside help, so make sure your
business case supports it, and make sure you select the right person (or people)
for the job.

you’ve managed to make it past step one with a strong business case, and step
two with a good execution framework, then welcome to the show. This is the fun
part, but it’s also the stretch that separates the men from the boys. That’s
okay, though. If you’ve gone through the diligence of taking the first two
steps properly, then I’m quite sure you have the early lead in this race. The
bulk of my work as a management consultant involves untangling the messes made
by leaders who don’t start properly.

For this
phase, you need to know where you’re going and how to get there. Successful
small business leaders that incorporate big data analytics in their strategy
bob and weave to an unknown port in the marketplace. It’s a place that spells
success but feels like chaos.

Destination unknown

can assure you, without an ounce of doubt, that your journey from step three to
your next destination will not go as anticipated. In fact, to succeed, you must
be extremely flexible on what your offerings and target markets look like. The
single most common failing of small business leaders is holding onto a dream
that’s not happening. That’s why it’s so important to build an adaptable
organization. To make matters worse, success doesn’t smell like you think it

a small business leader, your next port-of-call is chaos. The surprising truth
for most small businesses is this: if you’ve arrived at a place where the
complexity is so overwhelming that the business is starting to suffer, then
you’ve actually reached your rite of passage to the next stage of growth.

E. Grenier, professor of management and organization at USC’s Marshall School
of Business, calls this the crisis of leadership. If you can
successfully navigate this treacherous passage, then you’ve graduated to your
next stage of evolution, which has more of a management hue than one of
leadership. And for the purposes of launching a strategy that incorporates big
data, once you’ve survived this revolution, I don’t consider you a small
business anymore. The trick is getting through this crisis. For now,
your long-term goal should be to reach, and then traverse, the crisis of

the way, you should mark some shorter-term milestones of success. This is very
important, both financially and psychologically. The most significant
shorter-term goal for your strategy is the point at which it is paid for. When the profits from your strategy exceed your intended
investment, your strategy has paid for itself. This is a very significant point
in your strategy’s execution and it should be celebrated. Of course, if your
payback period is considered a milestone of your strategic execution, it
must happen at some point before your implementation is completed! Never launch
a three-year strategy with the intention of collecting your first dollar after
year three. In this case, your ideal payback period would be one
year, not three or four.

navigate unchartered waters to an unknown destination, you must take
organizational change management seriously. The anxiety level of you, your data
science team, and the rest of your organization will be very high at various
points throughout this journey. It’s vital to have the tools and skills in
place to deal with this anxiety and the uncomfortable feeling of moving in the
dark. If you don’t get this under control, it will paralyze your organization
and you won’t be able to move anywhere. One critical tool for keeping everyone
focused and motivated is a scorecard.

You can’t manage what you can’t measure

are magical devices, especially for leaders incorporating big data into their
corporate strategy. In the mid-1990s, Robert S. Kaplan and David P. Norton had
a thunderous impact on the management community with their introduction of the
balanced scorecard. Before Kaplan’s and Norton’s insights, most companies had a
myopic, financial view of measuring success. What the original balanced
scorecard did was extend the financial model to other areas of strategic
importance, including: customer perception, business processes, and
organizational learning and growth. Not only did it catalog and track
additional dimensions of strategy, but also the relationships that connect the
dimensions together. This had a powerful effect on how companies of the time
managed their businesses.

Since then, scorecards have retained their relevance and purpose in the
strategic landscape. Scorecards serve as a discipline for strategy formulation,
a governor for strategy execution, and a powerful communication tool for
understanding, commitment, and motivation. During strategy formulation (i.e.,
steps one and two), your scorecard helps you crystallize success into objective
metrics. And since this is a sweet spot for data scientists and other analytic-minded
people, it also serves as a great team-building tool.

During strategic
execution, your scorecard serves as your instrument panel. It notifies you and
everyone else in the organization where things are going great and where
adjustments must be made. However, the most powerful purpose a scorecard can
serve in your situation is as a change management tool. A public scorecard
instantly communicates what your intentions are, in clear and objective terms.

can take the traditional balanced scorecard and both tailor and supercharge it
for our purposes. To build an adaptable organization, tailor your scorecard to
track measures of flexibility. Identifying and reacting to leading indicators
is a key component of an adaptable organization, so this should be an important
area to focus on. This is where your data science team can really supercharge

For instance, sophisticated discovery techniques can help identify leading
indicators, and advanced machine learning can help with difficult causation
problems. Expedient experimentation is another hallmark of adaptable
organizations, so your scorecard could also track how long it takes your data
science team to transform an idea into a product or service offering. These are
just a couple examples to get you started. You’ll be surprised at what you and
your data science team can produce after a few creative sessions.

Balancing scope

any of these ideas to work, you must have the right management philosophy.
There are a few options to consider; however, none of them involve locking down
scope. In an earlier article, I referenced the post, lever, and balance method
(PLBM) as a framework for building your management philosophy. In short; out of
the management metaphysics of scope, time, and effort; you must decide what
will be locked (post), what will be intentionally moved (lever), and what will
be allowed to adjust (balance). To make it from step three to your next
destination, you must use scope as a balance.

approach respects the fact that you don’t know exactly where your next
destination is. The worst execution approaches I see use scope as a post. This
is the Software Development Lifecycle (SDLC) equivalent of a waterfall approach
and echoes the common failing referenced earlier, where a leader won’t let go
of an idea that’s not working. Instead, move scope (i.e., your offering /
market combination) around. Don’t worry; you can still
make money while you’re doing it. Read and react to your market signals with
fast and furious experimentation. This is the essence of adaptability. Your
strategy will benefit with accelerated payback and your customers will love you
for it, as it signifies that you’re listening to them.

back to PLBM, you can either post on time or post on effort. Posting on time is
very similar to the popular agile methodologies (e.g., Scrum); however, be
careful. A truly agile execution requires the data science team to self-organize
(otherwise, it’s just iterative). In my experience, data science teams don’t
self-organize well. Keep the coach, because she’s important; however, make sure
there’s a good analytic manager in place to keep the team moving forward. With
time as a post, and scope as a balance, effort becomes your lever. This
translates to fixed time-boxes (e.g., 30 days) and the occasional team
adjustment to make your scope balance out where you want it to. Also, make sure
your scorecard clearly communicates your philosophy and signals when
adjustments must be made. This will keep your execution on track.

other way to do it is posting on effort. Although this is very unconventional,
it’s my preferred philosophy and method of execution. Posting on effort means
you build your data science team, and then keep it that way until you’ve
completed your execution and reached your destination. You’ll still release
scope in iterations; however, the time periods aren’t fixed. You’ll manually
adjust the length of each iteration until you feel comfortable with what your
data science team can deliver. Even then, with this approach, it’s okay to
stretch the iteration a bit if the team feels close completing something
significant. Again, use your scorecard to communicate your philosophy and keep
things on track.


a small business by using big data in your strategy will take you and your
followers on a crazy journey to an unknown but marvelous destination. There’s
no way to know exactly where you’ll end up with your business, so don’t get too
hung up on one good idea. Instead, build an innovation funnel that you load up
with a lot of ideas, build the capability to quickly turn these ideas into
offerings, and then listen to your customers. Install a management philosophy
of adaptability and instrument your success with a scorecard. Then execute with
a purpose.

execution is the key. Too many people are just playing around with big data
without a solid idea of what they’re doing or where they’re going. This is a
waste of money and it will sink your business before you ever get out of port.
After step one and step two, your homework is done. Step three and beyond is
about action. You might not know exactly where you’re going,
but you’ve got a good guidance mechanism in place and a team that can turn on a
dime. That’s what it takes to succeed.

luck in your initial growth phase. It’s a tough one, but the most rewarding to
navigate. I’ve given you the mindset, the techniques, and the tools to survive
your first growth spurt. The rest is up to you. Surround yourself with amazing
people and settle for nothing less than excellence.  And when it’s time to let go,
put a good management team in place, and celebrate big. You’ve just struck gold
in the big data rush.