If you care about big data, care more about analytics

By Daniel Newman, Co-CEO V3 Broadsuite and president of Broadsuite Media Group, Broadsuite

Today, big data is one of the hottest topics in the business world. We are seeing companies exploit this information to outshine their competitors. Some companies consider it a guiding star, while others refer to it as a crystal ball that can provide new insights for the future of enterprises. So how do you leverage this information gold mine? Before you can do that, there are some misconceptions about big data that need to be cleared up.

The biggest problem with big data: Non-application

First of all, big data is not just any data. I’ve seen many companies drain their resources on big data strategies and analysis when they are not even remotely dealing with it. What sets big data apart from traditional data sets are the three V’s—volume, velocity, and variety. It’s the rapid acceleration of these three factors that makes big data complex enough to produce correlations that were previously unpredictable. However, the major problem with big data lies elsewhere.

Tons of information is generated every minute—precisely, 2.5 quintillion bytes of data every day. However, most companies inundated with all this information are still merely collecting and contextualizing mountains of data, doing very little with it after they have stored it in their repositories. Now, the info itself doesn’t really mean anything, and a huge pile of raw data is no better than an old stack of accounting reports or financial documents, unless it’s put to work.  

What should brands do? Analytics, of course

Instead of simply hoarding it, brands and companies need to parse through the vast heaps of data to find the useful information that helps them make better decisions. What they really need to be thinking about today is analytics. I’m sure you’ve heard of analytics since it’s one of the terms thrown around a lot in big data discussions. But what does it really mean?

In terms of a definition of analytics, one of the best I’ve come across is on theAptera Blog. They define analytics as “all the ways you can break down the data, assess trends over time, and compare one sector or measurement to another. It can also include the various ways data is visualized to make the trends and relationships intuitive at a glance […]the data crunching, question-answering phase leading up to the decision-making phase in the overall Business Intelligence process.”

What this really means is that analytics determines how much meaningful and actionable insights you can glean from big data sources. And, ultimately that’s what matters. The thing is, data never just hands you insights unless you take the right measures to decipher what it is trying to show.

To understand analytics better and to derive the most value from it, you must first understand its types and the differences between them.

Types of analytics

Descriptive analytics. These talk about the past—from one minute ago to even 10 years back. Customer info, social data, sales reports, search engine results are some examples of descriptive analytics. These are the basic building blocks from which we can derive useful information.

Predictive analytics. This is the next step in revealing what might happen in the future when a variety of techniques are applied, such as data mining, machine learning, modeling, and game theory. It’s important to note here as Dr. Michael Wu, data scientist at Lithium Technologies, wrote in the company blog: “The purpose of predictive analytics is NOT to tell you what will happen in the future. It cannot do that. In fact, no analytics can do that. Predictive analytics can only forecast what might happen in the future, because all predictive analytics are probabilistic in nature.”

Prescriptive analytics. This type of analytics provides actionable advice based on the estimations generated in the previous step. It is this last step that really lays out the best courses of action while also giving feedback to show the potential outcome of those actions.  

So it boils down to this: Analytics—not big data—is what leads companies to business intelligence and improved performance. It’s time to stop worrying about just collecting all the data that’s out there and start thinking about analyzing data-driven information that guides business improvement and offers a competitive edge. 

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