If
you have a suspicion that big data analytics could play in integral role in
your small business — you’d be absolutely correct. We’re in a unique period of
time where information prowess can build a decisive competitive advantage for those
who embrace it. 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.

Progressive
Insurance is a perfect example of a traditional company using the power of
information to build a competitive advantage in the marketplace, by monitoring
driving behavior in real-time and analyzing this data to adjust premiums. This
is an approach where big data supports a company’s core products and/or
services; there are two other approaches.  You can lead with big data analytics, building
a core product or service around it (e.g., Cloudera), or you can use big data
analytics as a key capability within your organization to support your
strategy.

Regardless of your approach,
step one is formulating a strategy with a strong business case to support your
use of big data. Without a strong business case, there’s a great probability
that you’ll make a poor investment decision. Although soft benefits are good
(e.g., a leading-edge image), your business case must be grounded in hard,
economic benefits. Like building a profit and loss statement from top to
bottom, start with revenue projections and work your way down to big data’s
contribution to the success of your strategy—without any consideration of costs. This will give you a good gauge
for how much you should spend on big data.

Strategy
is 20 percent formulation and 80 percent implementation. There have been
numerous occasions in my consulting career where I’ve had to clean up the mess
created by a big strategy firm who formulated a fantastic strategy that didn’t
vet out in the real world. In many cases, it’s not the strategy firm’s
fault — it’s the client’s interpretation of what should happen next. This is
especially true of small businesses trying to leverage big data for business
success. Creating a strong business case and assembling the right people is a
great first step; however, even the best strategy or plan is still— at best — just a good idea. For a fighting chance at success, it’s the second step that
counts.

Getting your head straight

Before
you take your second step, you must get your head straight. A tightrope walker
will tell you that taking the first step is relatively easy compared to taking
the second step. Of course, embarking on a business strategy that incorporates
big data is not life-threatening; however, it is very risky. You can mitigate
some of this risk in step one: building a solid business case and assembling
the right team. But there’s an important philosophical gap between taking the
first step and taking the second step. As you make the shift from formulation
to execution, it’s important that you install the right mindset for yourself
and the right culture for your organization.

The
paradigm of execution that you should inculcate is one of adaptability. The
strategy of no strategy is an idea that’s gaining popularity among the strategy
pundits of today. That is, instead of building a three or five year strategy,
some leaders are opting for no real strategy at all and focusing more on
staying flexible. Notwithstanding its faddish and somewhat hyperbolic
emergence, the idea has merit considering the conditions of the current
marketplace. In the old days markets were more stable and competition was not
as fierce. Nowadays, one false move, and you’re out of business.

Getting
clear on your philosophy to management is important before you take the second
step of your strategy. There are three interrelated metaphysics that pertain to
the philosophy of execution— scope, time, and effort. This is important because
most execution failures result from not understanding or clearly communicating
these relationships. To form your management philosophy, choose which
metaphysic will stand firm (post), which one you will adjust (lever), and which
one you will allow to be adjusted (balance). This is want I call the post,
lever, and balance method (PLBM) of management. To build a paradigm of
adaptability, you should use time as a post, scope as a lever, and effort as a
balance.

What
this translates to is a set of drop dead dates where something of marketing
value is delivered. Exactly what will be delivered must stay flexible (scope is
a lever, not a post); however, it must be something of value to your target
market. I suggest you condition your data science team to deliver something
once every four weeks — even if it’s small. It’s okay to have an initial ramp
period, but it must be short — three months max. On the marketing side, you must
identify signals that will tell you how your target markets are shifting, so
you can prioritize the functions your data science team develops. Consistently
releasing value to your target markets will significantly reduce your risk of
execution and accelerate your payback period. Make sure you’re clear on this philosophy
and that your organization is clear on how you plan to operate.

Taking the second step

A
data science team is one of the few groups that can instrument their own
success; the astute leader should take advantage of this. A popular joke about
the psychic who just went out of business is, “I wonder why she didn’t see
that coming?” If you’re going to invest in analytic competence, you might
as well divert some of this competence into running your strategy. Data science
can take some anxiety out of the second step.

Although
the philosophy advocates getting quick results, your second step should be
taken very carefully. Imagine that our tightrope walker, after taking the
second step, feels uneasy. Don’t you think they’d like the option of taking that
step back? When building the execution framework for your strategy, you need a
way to undo the last step. This will give you the courage to experiment without
taking the risk of losing ground. Your data scientists should already be
familiar with the concept; this is just good version control. You need to
extend the version control concept to all the elements of your strategy.

Testing
for success is another area that’s critical to taking the second step. How do
you know your offering is right if you don’t have good feedback? One of the
first exercises you should do with your data science team is building a
scorecard. Make it as sophisticated as possible — you have the talent, why not?
Your scorecard should give you an indication of whether or not your last
release was successful. It would be nice if you could simply monitor revenues;
however, it’s not that simple — any marketer will tell you that consumers are
not that turnkey. You can however, develop leading indicators to help you
understand if you’re doing the right things. For instance, you could monitor
customer sentiment through the social media channels to see if your ideas are
catching on. How auspicious to have a data science team in place that can pull
that off!

So,
the idea is to take a small second step, test for success, and then adjust
scope as necessary for step three. Rinse and repeat. If that third step ends up
being a bad move, that’s okay–just back up using your strategic version
control. In fact, you might go back and forth several times until you find the
right lane that propels you forward. That’s the nature of adaptable execution.
I’ll admit, the uncertainty is uncomfortable for most. That’s why it’s
important to get your head straight, read your market, experiment, and move in
short bursts of success.

Expert advice on experts

Outside
experts can be a blessing or a nightmare; the outcome has more to do with you
than anything else. Sure, there will always be charlatans out there trying to
rip you off; however, most services available provide some value to some
markets. It’s your job to determine if a particular service offering is
appropriate for your strategy.

Building
a solid business case and installing your execution framework makes it easy to
bring in outside help. Your business case will prevent you from making a gross
financial error; your execution framework will help you determine if you’re
making the right choices and mitigate the impact of bad decisions. Whether
you’re hiring a strategy consultant or managed services firm, knowing exactly
what you need and whether or not it makes business sense are the keys for a
successful engagement.

Outside
expertise comes in multiple flavors; however, for strategic purposes consider
three general classes of consultants: strategy, execution, and implementation.
Strategy consultants are used when you don’t know which direction to go. Let’s
say you’re six months into your strategy, you’ve made three releases into the
market, and nothing has really worked out. This indicates a gross misalignment
between your target market and your offering. This situation requires a strategy
consultant to untangle everything and set you on the right course.

Execution
consultants, like me, specialize in bringing a strategy to life. Execution
consultants are used when you’re comfortable with your strategy, but need a
blueprint for how to make it happen. Executing a strategy that involves big
data and a team of data scientists is not easy. You need the right mix of
leadership, management, and talent. In many cases you’ll be dealing with fickle
markets and intelligent but sensitive personalities. An execution consultant
can help you build and maintain that flexible structure that’s required to not
only survive, but succeed in such a tricky environment.

Implementation
consultants are the ones actually getting their hands dirty, once you have a
strategy and a plan in place. In most cases they will be data scientists for
hire; however, you could also go outside for business analysts, business
experts (e.g., marketing gurus), or even managers. I wouldn’t recommend hiring
contract data scientists if you’re using big data for your core strategy,
unless you plan to hire them once your strategy is successful. If big data is
playing more of a supporting role in your business strategy, contract data
scientists can be very beneficial. For instance, you might hire a few data
scientists to develop a sophisticated analytic service that complements an
already successful product. Once the analytics are developed, the difficult
part is over, and you’re free to release them to their next engagement.

Conclusion

Revving
up your engine is fun, but success happens when the rubber meets the road. The
scariest part of employing big data on your small business’s strategy isn’t the
first step–it’s the second step. With this second step comes your first
opportunity of knowing whether your great idea — is really a great idea. In the
true spirit of big data, there are opportunities that are moving around you at
high volumes, high velocities, and high varieties. Develop an attitude of
flexibility and an organizational culture of adaptability, and use experts
wisely so that you don’t get knocked off by the first gust of wind. You have
good vision and drive and you’ve taken a good first step. So be smart, surround
yourself with the right people, and take one more step — then don’t look back.