A splash of data used to be all you needed to get attention. Today, data-smart audiences want much more: detail, what-if scenarios, and deep, multi-sourced data for any questions they may have. They also want to see your work, from initial questions to final insights.
What they want is what people have always wanted: stories. For data, that means data stories. People live and breathe stories, whether they’re aware of it or not. So it’s inevitable, now that business people have tuned in to the “data-driven” ethos, that they would want “data storytelling.”
As the Big Data and analytics parade marches on, I often find that the people we're talking to in large enterprises carry the title chief data officer or CDO. Industry analysts back this up. A 2015 report by PwC found there were 100 CDOs in large enterprises in 2013, more than double the number in 2012. Gartner's most recent tally pegs the number at 950. And it predicts that by 2017, 50 percent of all companies in regulated industries will have a CDO. The CDO role is still evolving, but Debra Logan of Gartner describes the CDO as the "glue between data strategy and metrics."
Business analysts and line-of-business (LOB) data users have plenty of robust, self-service BI tools at their disposal. What they often lack is a way to get all the most relevant data into those tools. In a TDWI Checklist Report, Dave Stodder, Director of TDWI Research for Business Intelligence, lists seven best practices for executing a successful data science strategy. Number five: Give Data Science Teams Access to All the Data.
There’s no mistaking that you need to leverage your data to gain a competitive advantage. We hear it from the analysts, experts and those who have been there and know what the power data can do.
Finding the Right Data
But it’s not as simple as pulling up all your data sources, connecting them together and then doing the analysis. What happens instead is you spend days, weeks, even months trying to find the right information across all the silos of applications and data repositories inside your organization, many of which are hidden from view.
Some Attivio folks flew to the annual Gartner BI event last week to take the pulse of Business Intelligence, data discovery, and data democratization. We wanted to hear the latest from Gartner thought leaders and the several thousand data practitioners. In the opening keynote, there were 5 key takeaways. I’d like to zero in on numbers 2 and 3.
Number 2: Accept that the world will get more distributed. No surprise here. The shift began when new ways of consuming data emerged and spread like wildfire.
In a recent blog post about data exploration, Forrester's Boris Evelson discusses Tableau's recent acquisition of HyPer. He notes that HyPer addresses Tableau's previous lack of in-memory data exploration capability.
Though Evelson's blog mentions Tableau and other BI providers, his broader points center around the importance of removing barriers to data discovery, especially when analyzing Big Data stores.
Two or three years ago, when Big Data had started to gain serious traction in large enterprises, there was a rush to hire data scientists. Of course, disagreement reigned about what credentials made a true data scientist. Wonky math geeks were a good place to start. The rush to hire data scientists echoed the trend some decades earlier when investment firms hired quants right out of college and put them in the basement to create trading algorithms.
One thing was clear though. Data scientists were scarce and expensive.
Most chief executives have dreamed of leadership that permeates their organizations at every level. Yet to have that, every decision maker from the C-suite down would need data. And they fear that such data democratization may invite anarchy.
Despite that fear, their dream persists, and for good reason. Organizations with democratized data tend to have more buy-in, people at all levels take more responsibility, and their ideas are more relevant, with a greater chance of real impact. They feel stronger loyalty. Overall, there’s just plain smarter decision making.
The evidence comes from a variety of contexts. Military battles have been won because low level officers were free to think for themselves. Organizations have thrived on products that spawned when a mid-level manager pursued an idea. Workforces have toughed out hard times when data let them believe in fairness and a bright future.
Despite Gartner’s observation that 41% of organizations are unsure if their Big Data ROI will be positive or negative, they remain keenly interested in investing in Big Data technology so as to take advantage of data-driven use cases in an effort to improve predictions and forecasts, exploit IoT opportunities, identify new products and services, and improve operational efficiency. Other tactical business drivers include real-time decisions and insights. It’s tempting for organizations to climb on the Big Data bandwagon while overlooking the unique set of corresponding challenges attributable to the:
Another fiscal year has come to a close, and we at Attivio have never been more excited about what’s to come. Our entry into the Big Data market is filling a void that enterprises struggle with every day.
We understand that data is a strategic asset that, when made accessible to everyone in the organization, can lead to smarter, faster decisions, pushing past the competition in ways that didn’t seem possible.
We’ve built a platform that accelerates data discovery and the resulting analysis, and in the process, have helped numerous enterprises do more with their data than ever before.