The Chief Data Officer has never been a more necessary role in the organization than it is today. Organizations capture and store more data than ever before, and it’s growing exponentially every year.
Not only is business data growing, but we are seeing new types of data continually entering the mix. Data is structured, unstructured and semi-structured. It’s stored in big data lakes, in business applications, in file shares, and other places across the organization. There’s so much data that even the CDO isn’t completely aware of what’s out there.
And then there’s the external data. CDOs are becoming aware of the need to bring in external data sources that provide relevant, and sometimes essential, information to support decision making.
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.
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.
For all the talk about the importance of making decisions based on solid data, most companies still struggle to understand what data they have and how to get at it to actually use it.
Your company has taken the time to ensure you are capturing as much data as possible whether it’s structured, unstructured or semi-structured. You also purchased the best BI tools to help you analyze your data for insights. But there’s a problem. There are serious gaps in the data supply chain.
