are big decisions to make with big data. Consider these two statistics: 70
percent of all change efforts fail; and projects with excellent organizational
change management (OCM) average 143 percent return on expected value. The first
statistic surfaced in the 1980s when the Standish Group started tracking
success rates of IT projects.
Since then, you would hope things would have
improved; however, that’s not the case. A 2008 IBM study confirmed that the
majority of change projects still fail. The second statistic comes from a
McKinsey study that was published in 2002. Other top firms like Boston
Consulting Group and Bain have similar findings. For a large company, a failed
project is unfortunate, but life goes on. For a small business, a failed
venture is devastating. That’s why most small businesses don’t make it to their
trying to freak you out, or sell you on the idea of organizational change
management. My point for juxtaposing the two radically different outcomes is to
demonstrate that there are methods and strategies successful companies embrace
that unsuccessful companies don’t. Over my 20 years as a consultant, I’ve had
the privilege of working with large companies and small, and some patterns have
emerged. Once you have an idea of how big data will be used in your corporate
strategy (core, supporting, or operational), you must clearly understand
whether it makes business sense to proceed with your approach. Successful small
businesses not only have great visions but they also make great decisions about
if and how big data will grow their business.
a case for big data
absolutely, unequivocally, undeniably, must build a business case for big data
from the top down: Benefits to cost. I’ve seen far too many companies spend
money on big data without a clear idea of how they’re going to use it. There’s
so much hype around big data and misguided advice, it’s no wonder people are
confused. This confusion translates to irrational behavior that doesn’t make
any business sense. I implore you to do the opposite: build a rational business
case first, and then spend money on big data if it makes sense.
business case is not a panacea, but it will mitigate egregious strategic
errors. When I consulted for Visa, part of my responsibility was to help build
solid business cases for the 150 projects that were moving through its
enterprise data strategy. Although all 150 projects were on the radar, the
executives would not authorize any project that didn’t have a strong financial
case. This is a good practice that you should follow as well. A strong business
case for big data will quantify your investment decision.
business case stems from your strategy. You’ll need to determine the upside
potential of your corporate strategy and big data’s contribution to this
upside. To determine your upside potential, compare what your profits would be
if you launched your strategy against what they would be if you did nothing.
The difference represents your strategy’s potential
value. For instance, let’s say your profits this year are $10 million. If
you do nothing, in the next three years, your profits are projected to be $11
million, $12 million and then, $13 million. However, if you launch your
strategy, you profits are projected to be $11 million, $15 million, and $20
million. The potential value of
your strategy then, is $10 million ($3 million difference in year two, plus $7
million difference in year three).
decide the chances of your strategy’s success. This tempers the potential value
to something more realistic. Not all strategies work out, especially if they
involve big data. I suggest a very conservative estimate of perhaps 50 percent
or less. It may come as a shock to think that your strategy only has a 50
percent chance of success; however, this is the reality. Expect the best but
plan for the worst. In our example, if we adjust our potential value of $10
million with a 50 percent chance of success, we arrive at an expected value
of $5 million.
last step is to determine big data’s contribution to your strategy. Up until
this point, big data has been implied; however not explicitly involved in any
of the calculations. This is the point where big data becomes explicit. If
you’re using big data as your core strategy, big data’s contribution is 100
percent. However, if you’re using big data to support your core strategy, or to
fortify the operational capabilities that power your strategy, big data’s
contribution will be something less.
point, don’t think about cost; think about contribution to success. For
instance, Progressive Insurance is in the business of selling insurance;
however, they’re using big data quite effectively to adjust premiums based on
real-time driving data collected from sensors placed in their customers’ cars.
So, big data isn’t their whole strategy, but it may contribute 65 percent to
its success. Using this rationale, if the company in our previous example came
up with the same percentage, the expected value of big data’s contribution
to its corporate strategy would be $3.25 million (65 percent of $5 million
expected value). This gives us a rational place to start when considering an
investment in big data. For instance, if you spent $1 million on big data,
based on these numbers, you’re looking at better than 3 to 1 return. That’s not
a bad decision if you ask me.
Strengthening your case
that you have a general idea of how your business case for big data should be
built, let’s explore some ways to make it stronger. You shouldn’t spend too
much time in thinking and analysis without taking action; however, you
shouldn’t spend too little either. On the rare occasion that I actually see a
business case built for big data, it’s not tenable. Instead, it’s laced with
wild assumptions that have weak or non-existent support. Large companies
typically have strong governance to protect against weak business cases;
however, as a small business leader, it’s your responsibility to reinforce your
business case with a little armor.
risk you run is in step one: projecting the profits from your strategy. Since
profits are revenues less cost, everything hinges on your revenue projections.
This is where small business owners tend to get careless. It’s good to be
optimistic about your ideas; however, it’s dangerous to base financial
decisions on hopes and dreams. I’ve even seen people reverse-engineer revenues
from how much they want to spend on big data! Sorry, it doesn’t work that way.
of your strategy, understand your market. This is the best advice I can offer
to a small business owner. I’ve seen a lot of brilliant independent consultants
forced into a salaried job because they didn’t know how to market their
talents. A small business is no different. It’s great to have a big data
insight; however, it’s a completely different thing to have a big data insight
that’s vetted against your target market. Make sure your revenue projections
are supported by some form of marketing analysis. This is easiest for those
using big data to support a market-driven strategy–by definition they should
already understand their market well. However, all companies must know who their target market is, and be able to
make an educated guess as to what their customers will buy.
area of the business case to attack is step two: your chance of success. I
recommend an extremely conservative chance of success because big data
initiatives are inherently risky. That said, there are ways to improve your
chances. If you take an agile approach and deliver tangible results from your
strategy as soon as possible, not only will you increase your chances of
success by clarifying unknowns in the marketplace, but you’ll also accelerate
your payback period, which is the amount of time it takes to recoup your
investment. Practically, this is easier said than done; however, it’s worth the
other key things that increase your chance of success are scorecards and risk
management. It’s best to follow a data-driven approach, instrumenting your
success with a scorecard. A scorecard first defines your success criteria, and then
monitors your success during strategy execution. It also monitors key risks and
assumptions. Big risks shouldn’t come as a surprise. Your scorecard should give
you adequate warning to make adjustments.
you have a good idea of how much you can spend on big data, it’s time to make a
shopping list. Don’t be disappointed or upset if everything on your shopping
list adds up to more than you should spend. This is actually a blessing in
disguise; your diligence in building your business case has just saved you from
making a bad decision.
you shouldn’t spend more than the expected value of big data’s contribution to
your strategy; however, this is not your spending threshold. In our earlier
example, we had a $3.25 million expected value of big data’s contribution. If
we then spent $3.25 million on big data, we would have no return on our
investment. That’s not too smart. In fact, your spending should only be a
fraction of your expected contribution value–one tenth is ideal, but one third
isn’t bad. You really shouldn’t exceed half of your expected contribution
value. So in our example, if your big data shopping list came up to $2 million,
I’d recommend you pass, even though you’re staring at a vetted $10 million
biggest ticket on your shopping list will be talent, so I would concentrate my
efforts there. Data scientists are not cheap; however, there are some
cost-effective ways to find similar talent if necessary. The challenge with
data scientists is that they have both analytic and programming skills. This is
a little hard to find now; however, we should start seeing more talent enter
the marketplace in the near future. In the meantime, I suggest looking for
people that either have advanced degrees in some form of mathematics or
physics. It’s more likely that they’ll have some programming experience, than
finding a programmer that has advanced math skills. Another avenue you might try
is out-of-work Six Sigma Black Belts. If they have a proven track record, they
should have good analytical skills. And if they’ve also done some programming,
you’re in luck.
you can’t stop with just one data scientist. It may take a team of data
scientists, and at a minimum, you’ll also need analytic leadership, analytic
management, and business experts (especially marketing). Ideally, you’ll also
add change leaders (remember the opening statistic?), team builders, governors,
and quality specialists. Even if you have resources currently in place
(including yourself) to fill these roles, do they really have the time to
devote to building out a new strategy, while simultaneously keeping the
business running? Probably not. A consultant(s) could be used; however, they’ll
cost more than a new employee. That said, a consultant might be more efficient,
as their expertise is deeper and wider than a typical employee. You’ll have to
run the numbers and do what’s best for your situation.
final caution: don’t cut costs just to cut costs. Balance costs to build a
reasonable business case, but don’t overdo it. You get what you pay for, and
you don’t want to skimp on talent if you can afford an expert. Like anything
else, you take huge risks when you center your approach on reducing cost. Low
quality talent is unpredictable and the last thing you need on a big data
initiative is more uncertainty.
a strategy that involves big data can be exciting, but it can also be fatal.
The success rate of companies taking this journey swings wildly from dismal
failure to wild success. It’s not all random. It certainly takes some luck, but
it also takes the ability to make smart decisions, especially if you’re dealing
with the limited resources of a small business. Before launching a strategy
that includes big data, be sure your investment makes economic sense.
business case provides a rational explanation for your big data investment. By
quantifying big data’s contribution to your strategy, you extricate its real
benefit in financial terms. This is the information required to make a wise
investment decision: the bulk of which will be on big data talent. Of course,
you get what you pay for, so make your decisions based on value, not cost.
walked you through the mechanics of building a strong business case for big
data, so take some time today to put some rough numbers in place. It won’t take
very long and it will give you a good sense of whether to consider a business
relationship with big data. If the numbers work out, then great, go for it. If
the numbers don’t work out, go back to the drawing board. Either way, pat
yourself on the back for making a good decision. Continue making decisions like
this and you’re well on your way to success.
Read more about big data and the path to success with TechProResearch’s article The big data rush: How small businesses can strike gold.