There is a trend in business analytics these days that often pits two groups against each other: one demands speed, the other requires IT, oversight, or governance.
In the drive for a deeper understanding of data from disparate sources, customers tell us they need their analytics products to be fast and easy to use, but methodical enough for things like business modeling, compliance, and security. Oracle is calling this an “Axis of Tension”.
Think it doesn't happen in your organization? Consider that strain you feel between those who demand self-service analytics and those who require stricter IT involvement. The trend started years ago when control and power of desktop analytics tools allowed the average business manager to gain insight without the standard spreadsheet.
Some 46 percent of respondents to a TDWI survey on visual analytics best practices said they’re dependent or extremely dependent on IT for analytics, but only 37 percent thought they would be just as dependent on IT in two years.
The rapid adoption of desktop analytics has now plotted a future where more than $187 billion in big data and analytics software revenues will be spent by 2019, up from $122 billion back in 2015, according to researchers with Gartner.
This brings me to a recent group discussion on Twitter on the theme of Business Intelligence. Howard Dresner, a business intelligence guru in his own right, posed the question of how to enable data diversity while managing the inconsistencies or lack of governance.
"In many cases end users are leveraging/analyzing these data sources & making decisions without governance," Dresner said. "Are these two goals contradictory?"
The crowd was mixed on how to balance the push and pull of these two factions, but longtime business intelligence and data warehouse expert, Sara Warner, suggested organizations avoid the so-called data chaos by loading owned data into a large repository (i.e. data lake). The organization should then validate, verify, and qualify the data. At that point, the IT department should be in charge of integrating and regulating data sources. Finally, there should be a good amount of housekeeping to allow for consistency.
"[Avoid data chaos] by managing expectations qualifying data sources and giving an indication of appropriate usage," Warner said.
Ultimately, the path to take for businesses hungry for data analytics clarity is for jointly owned initiatives by IT, Finance, and Operations to be effective. On approach these businesses need to consider is adopting a complete solution that allows for flexibility of usage with the ability to shift between data needs, whether it is hosted in the cloud or managed on premise.
Luckily, Oracle offers numerous products that help no matter how small and nimble or how big and detailed your BI work needs to be.
Photo courtesy of Miemo Penttinen