Beyond the hype: When data collaboration really matters

The rate of data creation today is already simply mind-boggling. And it’s speeding up more every day. IDC’s latest Digital Universe study predicts about 1.7 megabytes of new information will be created every second for every human being on the planet by 2020. Consumers create the vast majority of new data in the world today — through social channels, retail POS purchases, online behavior and preference tracking, digital output, cell-phone locations, and the like. Yet, IDC found that only 0.5 percent of that data’s ever analyzed.

Andreas Weigend, who directs Stanford University’s Social Data Lab, is a longtime proponent of the societal benefits that come with the socialization of data. If the last century was marked by the ability to observe the interactions of physical matter—think of technologies like x-ray and radar — this century, he says, is going to be defined by the ability to observe people through the data they create, share and consume.

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From both a social and business perspective, of course, the vast volume of data that consumers create every minute, blended with business intelligence insights and external data sources — like, U.S. Census or customer sentiment data, for instance — is tremendously valuable to businesses of all kinds. Blending and harmonizing that data in a fast cycle helps business leaders succeed: to understand customers, increase response times, add to the bottom line, give people more of what they like and less of what they don’t, pivot faster to beat competitors, improve hiring processes — and for new innovations like live monitoring of patient data to improve health outcomes.

A new business data analysis collaboration model emerges

On one side, businesses now require smart machines to blend vast amounts of data together for analysis. Two- thirds of organizations are already trying to blend together five to 15 sources of data for analysis, according to a recent Harvard Business Review study (PDF), and the majority who use manual analysis with Excel docs realize it’s not a viable solution anymore.

On the flip side, once that complex data blending has been done, there are significant benefits to the wisdom of the crowd, aka data analysis collaboration. Various experts in different disciplines can review and weigh in, external partners can collaborate on shared data insights, and new answers can be asked in a groupthink manner with iterations within the data analysis.

Many big vendors such as Amazon, Google, IBM and Microsoft are now starting to talk about the benefits of sharing and collaboration in data analysis. But the majority of business people are confused by how valuable it is and what it truly means.

Like the social business collaboration trend that followed the consumer social media revolution, business data analysis collaboration has arrived. Or has it really yet? Does data collaboration in modern data analysis really matter — and if so, for whom and what?

3 places it matters — the rest are all hype for now

1. Globally distributed teams and in-context collaboration

When teams are globally distributed, they need the ability to see insights and work off the same palette of visualizations and views. Why? Key stakeholders may be in the Pacific time zone observing a new insight on the business, while other team members may be located in a different time zone and review it many hours later. Success for globally distributed teams hinges on everyone being able to see what was observed, even though data sources being viewed later across the globe may have updated and been refreshed.

This scenario is essentially the concept of in-context collaboration. If it’s not in context, and different people see it and read about at different times, and they’re trying to answer the same question, you might as well go back to the old ways: like sending dashboards by email and letting the threads go on and on, which can be an immense waste of time — or take so long that businesses can’t take effective action to make meaningful, data-driven decisions.

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2. Tracking workflow across data stewards for business collaboration

When data teams have access to different data sources that need to be blended into holistic insights, each owner needs to make a connection to each source, determine what’s needed from that source, and then pass on the data to the next player in the group who knows which source needs to be blended in next to answer business questions.

Think of this as collaboration amongst data stewards for the purpose of a trackable, high-quality, and auditable workflow, where collaboration acts as hand-off points in the data pipeline — and eventually, blending the right data sets to garner the most valuable results.

3. Power of crowds for a 360-degree point of view and best outcomes

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When it comes to data insights, the best and most meaningful are those where key stakeholders that really know the business — how it works and what they care about — jointly collaborate on data analysis for fast insights on what matters most. The power of domain expertise makes it possible to know the right questions to ask get near real-time insights on the business.

Together, they know what data is the newest information, what’s irrelevant or an outlier, and what actions must be taken based on the data insights revealed so they’re most meaningful and material to the business. No good business decision in any area is made alone. It’s always a team opinion and decision. A 360-degree point of view through business data collaboration leads to the best, most successful business outcomes.

Sharmila Mulligan is founder and chief executive of ClearStory Data, a big data analytics startup based in Menlo Park, California.