Critical steps for successful SMB big data strategy

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Big data analytics can play an integral role in a small business. Formulate a strategy and use it to build a competitive advantage in the marketplace.

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.

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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.


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