Data Dexterity is the Last Mile in Agile BI

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.

That widespread access to data across the enterprise — what we call data democratization — starts agile BI down that last mile. That access engages the organization’s total intelligence, not just the few with the technical know-how to find data on their own. Data democratization accelerates decision making, informs intelligent reaction to competitive threats, speeds innovation, and identifies opportunities. 

What are the key capabilities of what we call “Data Dexterity”?

The right people get their hands on the right data quickly

The primary inhibitor of data democratization is the systematic lack of insight into what information is available and relevant. Analysts who lack instant access to enterprise data go with what they’ve got — which is usually only about 10% of all that exists, according to Gartner.

That helps explain Forrester’s finding that satisfaction with analytics actually fell by 20% from 2014 to 2015.

Picture it: You have bigger data than any competitor. You have the easiest to use analysis tools. You have the best visualizations. You even have the most skilled analysts and the wisest executives. But where did we put the data? Which Hadoop cluster? Whose silo? How deep in the data lake?

Today more than half the time spent on data analysis is in profiling and understanding data sources — before any data analysis can take place. With such bottlenecks, how can business users keep pace with today’s business? A self-semantic data catalog is the only real answer.

Break down silos of information

I’ve never talked to a company that admits to having data silos. Silos go under other names: data warehouses they inherited with an acquisition, or isolated departmental repositories, or just a barrier between the data warehouse and the data lake.

Sometimes it’s a “known unknown” problem – the data is locked in silos, and it’s just too time-consuming to get it out. Whatever the cause or combination of causes, the effect is siloed data — inaccessible to others who need it.

On top of that, the relationships between siloed data sets are very hard, if not impossible, to model. If customer data resides in three different systems that don’t speak to each other, how can you truly address your customers’ needs?

To make your data work for you and align with your business goals, you need to unify disparate sources across silos.

Data becomes a strategic asset

Top performing companies recognize data as a strategic asset. From the CDO down, they work to ensure that business users can tap that data no matter what silo, Hadoop cluster, or data lake it may hide in. The data’s been profiled, identified, unified, and provisioned. Users have immediate visibility into the right data for analysis, and they extract maximum value from it.

Data as a strategic asset gets a big boost with an advocate for data in the C-suite. CDOs prioritize data in the enterprise, launch analytics initiatives, and monitor effectiveness. (See my post on CDOs.)

This is Data Dexterity. An agile data supply chain surfaces the best information and takes analytics over the last mile. While some organizations go on talking about data, the company that harnesses all its onboard intelligence wins the race.