Chief Data Officers have a lot of things on their plates. And one of those things is giving users freer access to the data they need. This is what we call "data democracy." Most CDOs like the idea of data democracy in theory. But in practice, the CDOs we talk to find that efforts to create a data democracy face at least four common barriers:
There's a lot of talk these days about how to streamline the data supply chain. And the discussions often boil down to how to control an organization's data and how difficult and time consuming it is for business users to access it. As I wrote recently for DataInformed, highly structured systems for managing data like master data management (MDM) and enterprise data warehouses (EDWs) put a kink in the data supply chain.
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.
For all the talk about competing on analytics, little is said about what that takes. Strong visualization? Speed? Easy to use tools? It takes all that, of course, but one thing comes first: ready access to the data — the right data, for the people who need it, when they need it. As I said in my 5 predictions for BI and Big Data in 2016 post, without access to all your data, competing with analytics is just talk.
We’ve been talking a lot about data democratization lately—what it means and how to achieve it. In practical terms, a data democracy provides unfettered access to all an organization’s data stores for anyone one that needs it. That means someone or something has to have a firm grasp of where all the data lives and understands what it contains.
As Nate Silver points out often—and humorously—in his book The Signal and the Noise, the world is full of noisy data. Inside the untapped potential of Big Data and Business Intelligence is the signal. When the power of data is fully harnessed, it enables executives to transform productivity and act with certainty. My predictions for 2016 are about the trends that move businesses through the noise to truly leverage information as a strategic asset.
“The only thing better than certain insight is certain insight you don’t have to work hard to find.”
Our view of any business situation is enhanced when every relevant insight is available for consideration. In the early days of search, our queries determined precisely what we found. We were limited by the data that was indexed and the terms we searched.
The short answer: “To get them out of their hair.” But seriously…
At Attivio, we work with decision-makers at various companies who own and administer the BI infrastructure. We call them “BI tech owners.” A BI tech owner’s team governs data and delivers it to business users for analysis.
BI tech owners are not in an enviable position at the moment. The proliferation of self-service analytical tools for Big Data and BI have generated orders of magnitude increases in requests for data. And those requests all come with an ASAP attached.
The single, most-important determinant of the lifetime value of a modern, enterprise-wide application is its dexterity in adding or changing the data sources that drive the application. ‘Rock stars’ deliver solutions that provide value and benefits for years, adapting to new or evolving data sources quickly and easily. And, now, there’s no excuse for not being a ‘rock star’.
How Data Virtualization Drives Secure, Agile, Enterprise BI
If you believe that the convergence of Big Data and Big Analysis is a ‘half full’ opportunity, chances are you’re familiar with and engaged in the implementation of data virtualization to accelerate the move to data democracy in your organization. New tools extend the franchise for secure, well-governed data provisioning – offering the ease of self-service and/or dramatic gains in IT productivity. By eliminating many of the costs and risks associated with traditional data integration methods, virtual data marts b