Customer Experience, How to Break Free From Big Data Analysis ParalysisBig data is getting a lot of deserved attention -- it helps you know more about your customers and your market. But the trick is extracting these insights and making them actionable.

Here are some of the steps you can take to turn insight into action as well as one very practical way to make big data work for you, as a marketer.

Moving From Insight Into Action

Big data gives you insights, but data by itself doesn't give you actions. You and your team need to be able to take the insights and turn them into concrete, measurable activities.

Another challenge with big data isn't just translating insight into action, but comes from just how much data you collect -- big data is big after all. When you’re looking at the mounds of information you have access to, it can be easy to miss important facts or potential opportunities.

Here are four things you can do to turn your data into actionable steps without making a crucial omission:

1. Visualize It

Part of the challenge associated with big data is simply knowing where to start. There is so much and it can become so overwhelming that you really need help identifying which data is important and which is significant.

Sinan Aral, the Scholar-In-Residence at the New York Times, writes, “Handling the streams, archiving the sessions and storing and manipulating the information are in themselves herculean tasks.”

He continues, “But the even bigger challenge is transforming beautiful, big data into actionable, meaningful, decision-relevant knowledge.”

Aral says that data visualizations are tremendously effective to spot trends, but also to help isolate areas for further research and analysis. This might sound like busy work, but often the raw data from big data isn’t actionable in and of itself -- it needs more work, more massaging and more digging to be made valuable.

For example, a conversation on Twitter about a blog post might show high levels of activity and conversation, and you find certain Twitter users more effective at stimulating conversation and debate than others, but you still need to understand the networks of those influencers and how they behave.

2. You Need People Who Can Handle Big Data

Avinash Kaushik has what he calls his 10/90 rule. With this rule, he argues that you should invest most of your analytics budget ensuring you have people who can handle and interpret data. He felt this way because when he created the rule, most analytics programs only spit out mounds of data and you’d need the brainpower to put that data to work.

You still need brain power, but his 10/90 was first proposed in 2006 and analytics tools have come a long way since then. Get smart people, get good data people, but find what balance works best for your company. It might be 10/90 or it might be 50/50.

There is no set rule for every company -- you just need to experiment and learn what works.

3. Cut Through The Noise

When you work with big data -- you get so much information you can become paralyzed. Instead, you need to be able to use technology and brainpower to cut through the heaps of data -- the noise -- and focus in on what is most important -- finding correlations.

The best way to cut through the noise is to search out pieces of data that appear to be correlated with one another -- when one moves the other appears to move. Once you have this, and before you jump to conclusions, you next have to search out causation -- which variable causes other variables to change.

Causation is where the real value of big data is found -- it lets you spot which behaviors are likely to lead to other behaviors.

4. Ask Smart Questions

Thomas C. Redman and Bill Sweeney, writing for the Harvard Business Review Blog, propose seven questions you need to ask your data team. Here are some of the most important questions:

  1. What are we trying to solve? If you don’t ask this question, you’ll never be able to cut through the noise and focus on what’s important.
  2. Is this data trustworthy? You need to make sure the data was collected in a way the protected its integrity and ensures it’s as representative of the group or market you’re researching.
  3. What assumptions are you making? In many instances, the best case scenario doesn't involve eliminating assumptions, but, instead, identifying them and putting them out in the open.

Real Time Response To Big Data

Now that you've analyzed the data, uncovered insights, and translated those insights into actions -- you’re finished, right? No, you’re not.

There’s another challenge associated with big data -- more information is always coming in, new data is being monitored and new insights are found. It’s a never ending, always evolving process.

Because it is continually changing, it’s very hard to stay on top of it -- this is where many marketing programs built on big data breakdown. The team becomes so paralyzed by the continual inflow of new information, that they are unable to manually respond to all of the insights that comes from their data.

The businesses getting the most value out of big data are those that create rule-based automated systems that are designed to act on data in real-time.

Let’s look at the banking industry -- one quarter of all big data use comes from this sector. Each firm handles 3.8 petabytes of data and the industry collects and combines data from credit card and banking account activity with publicly available economic data to analyze customer sentiment and attempt to predict customer behavior.

Online, such automation can be handled by web personalization platforms that are driven by behavior-based analytics. Such platforms give online businesses the ability to use external (demographics, location, age, gender, etc.) and internal data (click stream analytics data, business metrics, engagement trends, etc.) to automatically personalize the user or customer’s web experience. This kind of personalization increases website conversions, user engagement, and the level of customer happiness.

A great example of web personalization in action is from the online retailer Gardeners Supply Company. They used web personalization to craft a targeted offer to its visitors coming to its site from Pinterest.

They launched it because they were having trouble converting visitors from the social network, but they saw conversions increase six fold after they started responding to incoming data in real-time, rather than providing a static experience to every website visitor.

Web personalization enables businesses to create automated workflows that take action on your data in real-time in time to provide value back to the customer -- and in turn improve business outcomes while you sleep.

How have you leveraged big data for your marketing team? Leave a comment and let us know.

Title image courtesy of gualtiero boffi (Shutterstock)

Editor's Note: Catch up on more of this month's focus on how marketers are using big data analytics here.