Big data is constantly transforming the way companies do business as it unlocks new insights into opportunities and efficiencies each day. However, it’s important to be organizationally prepared before getting too deep into a big data project. With that in mind, here are six big data questions that will help you determine how to reap the maximum value from your big data initiatives.
Big data is a relative concept that is constantly in transition, and it can mean different things to different businesses. For this reason, it’s helpful to create a working definition of what big data means to you. Many companies talk about big data in terms of volume, but it’s also useful to conceive of it in terms of velocity and variety. You may want to consider, for example, how important it is to have real-time data—that is, big data operating at a certain velocity—as well as what types of data will be most valuable for achieving your goals. The answer to this question often drives a lot of the attitudes surrounding big data initiatives and shapes the vision for what the company can achieve with big data.
Thanks to technological breakthroughs, businesses can leverage data insights to achieve almost any goal they set. Traditional IT barriers are no longer in the way of such progress, so there are no excuses for not pursuing the strategy you have in mind. If you want to understand customer sentiment on social media, for example, you can do that by collecting relevant data and harnessing its insights. But you need to know this: How serious are you in wanting to go after those kinds of insights? How serious is leadership in seeking the answers? And how wide ranging is your imagination regarding the kinds of big data questions you can ask? These are the only factors that will limit your big data success.
If you find the great insights you were looking for in your big data, how will you operationalize them? For example, you might have discovered a fascinating customer insight about purchasing preferences in your company’s western region. How will you make sure you can rapidly deploy it? This need to operationalize your insights probably won’t be limited to a one-off scenario. You’ll need to incorporate your insights into your organizational DNA, propagating them throughout your operations. You have to decide what actions you will take based on those insights, how you will operationalize those steps, and how doing so will change the behavior of your company. That’s when transformative change takes place.
You will also need to define the rules and requirements for globally managing, securing, and governing your big data. Ideally, it’s better to design them beforehand, from the ground up, rather than having to tack them on as an afterthought. But you don’t want your project to be unduly constrained by governance and security. Those factors should enable the business rather than inhibit it. You’ll need to find a way to forge ahead without creating risk for your organization, by balancing governance and security with your business objectives. It’s also worth considering the risk—what it will cost your organization if you wait too long without doing anything. After all, we are at a data tipping point and no company wants to get left behind.
When you pursue a big data project, it’s important to construct your team with the right skills and perspective. Contrary to what business leaders might assume, that doesn’t necessarily mean bringing in a techie to crunch the numbers. If you want to achieve transformational results in an area like customer experience, for example, you’re going to need other skills and viewpoints as well. There’s a softer, more emotional side that often comes into play, and it will rarely be directly reflected in the data itself unless you intentionally shape it that way and search for those kinds of attributes. In such cases, the style of analytics is not just purely statistical—it also has to do with people’s behavior, motivations, and attitudes. Accordingly, you’ll want to ask yourself how you can bring those kinds of diverse, nuanced insights into your analytical team.
Some of the projects you pursue may fail, and that’s OK. The key is to fail fast. You don’t want to spend two years on an initiative and then fail, generating a questionable return on your investment. It’s better to adopt an approach of nimble experimentation—moving on quickly if a project is not successful so that you can try something else that might work better. Along the way, consider your organizational attitude toward failure. Are you going to be punishing people if they fail? To ensure long-term success, you need to make it clear to your employees that failure is a necessary step on the path toward innovation. People need to be comfortable with failing and learning from their mistakes in order to make the great discoveries that move the business forward.
As we’ve seen, big data projects often prove to be more complex than we might assume at first glance. But by asking yourself these questions before getting too deep into a big data project, you can go a long way toward achieving lasting, transformative results with any big data endeavor you pursue.
To learn more about how to ensure that your big data journey is a successful one, download this white paper.