The Times They Are A Changin’, by the great Bob Dylan, was the anthem of social and musical change in the 1960s. This song signaled the start of a social revolution whose effect we still feel today. If you do not already realize it, we are in the midst of change within the IT and Analytics world that has the potential for upheaval and transformation like those in Dylan’s day.
In today’s established, traditional world, IT is the holder, gatekeeper, and provider of data within organizations. When business users identify a need for analytics, IT asks “What questions are you trying to answer?” This is not a bad thing; it is just the approach the industry created to deliver analytic solutions.
I refer to this approach as Question First. Question First evolved out of a time where:
• Data extraction was difficult and time consuming
• Querying and reporting data required a developer
• Analytics were static and backward looking
• Ad-hoc reporting was technically challenging and extremely costly
• Most organizations consumed only structured data
In my next few posts, I will examine how technologies in today’s “big data” world are making the above points invalid. Before digging into the details, it is critical to understand that most business changing ideas come from the people running and performing the business every day. Empowering these folks to consume and react to data quickly and easily is critical in fostering the growth of business innovation. This empowerment requires a new approach to analytics.
The above diagram, by Cloudera, illustrates a more Agile approach that I refer to as Data First. Today, businesses are generating and consuming more data than ever. There are so many sources of data that it is becoming challenging to sift through the data and meet everyone’s unique needs in a timely manner. The Data First approach shifts the determination of questions from IT to the business users. IT focuses on collecting, creating, integrating, and managing data from the many different sources and providing secure access to the data. Business users then use one of the hundreds of tools on the market today to discover important data elements and iteratively develop complex analytics to answer an unlimited number of questions. Once the business deems a question critical, IT migrates the analytics to a production level environment.
To make this approach work, the IT culture has to incorporate the following beliefs:
• Today’s business users are technically sophisticated enough for data discovery and analytic tools
• Business users want to perform discovery and analytics against native source data
• Extensive data quality activities go before operationalization not discovery
Join me in my next blog as I explore data extraction in the “big data” world.