Big data analytics have helped companies do some pretty amazing things, from predicting consumer behavior to driving additional sales. But behind the scenes, big data can be a big problem.
So how are big data analytics being misused in business?
Pitfall #1: Complacency
The first common pitfall to avoid is thinking that big data analytics means you don’t need a bigger data warehouse.
After organizations spend significant time and money in building out their foundational enterprise data warehouse, then perhaps another year getting a visualization tool working consistently on top of it, it’s difficult to think, “Let’s re-architect!”
But the downfall of many current investments in big data analytics is that they’re rooted in more ETL, more hardware, and more horsepower. The realities are that the organization should be continually investing in and seriously re-examining the overall data strategy every three years.
The last few years have been marked by massive cloud platform innovations, reliance on unstructured data and less on traditional relational databases.
Pitfall #2: Blind faith
The second common pitfall in big data analytics strategies is the lure that the “tools are magical.”
Household names like Microsoft’s Power BI, Tableau and Qlik deservingly continue to lead Gartner’s Magic Quadrant, but are far from wizardry. It’s no wonder why leadership teams are mesmerized; someone recently shared a list of the Top 50 Open Source BI Tools.
Really… 50?! How can you choose?
Our team strongly prefers Power BI because of its deep integration to Microsoft’s Azure Platform, ability to implement into applications and mobile readiness. A real ownership in common data definitions across different entities (i.e. business units, regions, etc.) can require expert advice. Most companies stop short of taking their “just data reports” to advanced analytics that can drive business decisions, if not automate them.
If applying your big data analytics strategy toward machine learning, artificial intelligence (AI) or data science is your destination, then you must get the data engineering right first.
Avoid these pitfalls and learn best practices from the experts at Innovative Architects.