Let’s face it, big data – or data in any size, format or shape – is nothing more than just a bunch of digital bits that occupy space on a disk somewhere. To be useful to the business, end-users need to be able to access it, and pull out and assemble the nuggets of information they need. Data needs to be brought to life.
That’s the theme of a webcast I recently had the opportunity to co-present with Tableau Software, titled “Making Big Data User-Centric.” In fact, there’s a lot more to it than making data user-centric – big data should be a catalyst that fires peoples’ imaginations, enabling them to explore new avenues that were never opened up before.
Many organizations are beginning their journey into the new big data analytics space, and are starting to discover all the possibilities it offers. But, in an era where data is now scaling into the petabyte range, it’s more than technology. It’s a disruptive force, and with disruption comes new opportunities for growth.
Here are nine ways to make this innovative disruption possible:
1. Remember that “data” is not “information.” Too many people think that data itself is a valuable commodity. However, that is like taking oil right out of the ground and trying to sell it at gas stations – it’s not usable. It needs to be processed, refined, and packaged for delivery. It needs to be unified for eventual delivery and presentation. And, finally, to give information its value, it needs to tell a story.
2. Make data sharable across the enterprise. Big data – like all types of data – tend to naturally drift into silos within departments across enterprises. For years, people have struggled to break down these silos and provide a single view of all relevant data. Now there’s a away to do it – through a unified service layer. Think of all the enterprisey things coming to the forefront in recent years – service oriented architecture, data virtualization, search technologies. No matter how you do it, the key is to provide a way for data to be made available across enterprise walls.
3. Use analytics to push the innovation envelope. Big data analytics enables end-users to ask questions and consider options that weren’t possible within standard, relational data environments.
4. Encourage critical thinking among data users. Business users have powerful tools at their disposal, and access to data they’ve never had before. It’s more important than ever to consider where the information came from, its context, and other potential sources that are not in the enterprise’s data stream.
5. Develop analytical skills across the board. Surveys I have conducted in partnership with Unisphere Research finds barely 10% of organizations offer self-service BI on a widespread basis. This needs to change. Everybody is working with information and data, everyone needs to understand the implications of the information and data with which they are working.
6. Promote self-service. Analytic capabilities should be delivered on a self-service basis. End-users are accustomed to information being delivered to them a Google speeds, making the processes they deal with at work – requesting reports from their IT departments, setting up queries – seem downright antiquated, as well as frustrating.
7. Make it visual. Yes, graphical displays of data have been around for more than a couple of decades now. But now, there is an emerging class of front-end visualization tools that convert data points into visual displays – often stunning – that enable users to spot anomalies or trends within seconds.
8. Make it mobile. Just about everyone now carries mobile devices from which they can access data from any place. It’s now possible to offering analytics ranging from key performance indicator marketing, drill-down navigation, data selection, data filtering, and alerts.
9. Make it social. There are two ways to look at big data analytics and social media. First, there’s the social media data itself. BI and analytics efforts would be missing a big piece of the picture if it did not address the wealth of social media data flowing through organizations. This includes sentiment analysis and other applications to monitor interactions on external social media sites, to determine reactions to new products or predict customer needs. But there’s also the collaboration aspect, the ability to share insights and discoveries with peers and partners. Either way, it takes many minds working together to effectively pull information from all that data.