03/07/2017 Steve Remington

Business Analytics Tools are not Pokemon – you don’t have to “catch ’em all”!

Pokemon logo over Pokemon card collection

In early June, I attended the Gartner Data and Analytics Summit in Mumbai, India. I attended the summit as a representative of one of Minerra’s key technology partners, Yellowfin. With their Indian partner Aptus Data Labs, we engaged over 100 attendees with some great conversations about business intelligence, business analytics tools and the challenges of both.

In our conversations, one theme quickly emerged. Many people visiting the Yellowfin booth had a similar conundrum: they had many different analytics tools (usually three or more) in their organisation and wanted to consolidate down to just one tool.

Over the two days, the number of people coming up to us with this issue was so numerous that we began calling it the “Pokemon Problem” – it appeared that many organisations treat analytics tools like Pokemon and feel that they have to “catch ‘em all”!

Why so many analytics tools?

The reasons why organisations “suddenly” had so many different analytics tools varied but over the course of the summit, some similar explanations emerged:

  • Different tools were acquired as a result of mergers and acquisitions but nothing had been done to standardise one analytics tool across the merged organisation.
  • Organisations had a loose or non-existent policy about the purchase of analytics tools within different departments, so each department purchased the tool they wanted despite different tools already existing within the organisation.
    • This is also a consequence of poorly governed self-service business intelligence.
  • Organisations had an existing analytics tool, but the existing tool did not have the required functionality. As a consequence of this, it decided to buy another analytics tool and use both tools in parallel rather than creating a plan to migrate from the old system to the new.
  • One organisation even said they purchased a new analytics tool because a newly employed data scientist did not like the existing analytics tools.

Why is this a problem?

The problems cause by collecting many different analytics tools were:

  • Confusion and inefficiencies among consumers of analytics content. There was a huge burden on staff to remember how to use many different tools to access the information they need to monitor performance and make decisions. This also led many users to use the analytics tools just to download raw data so they could later analyse the data using Excel – the one tool they know well. This also increased the risks associated with ungoverned analytics content being used in the organisation.
  • Low productivity for analytics developers, either because additional developers are needed to ensure that the organisation has expert skills for all the analytics tools, or the existing developers have to know all tools, which leads to them having only a moderate level of skill with all tools rather than being expert in one tool.
  • Increased licence and maintenance costs for the organisation because they have small licence holdings with many software vendors rather a large licence holding with one vendor, which may lead to overall lower licence costs.
  • Increased operational costs because IT departments have to provide and maintain multiple sets of infrastructure for each analytics tool, particularly if the tool requires a server to distribute the content.

In my next post on this topic, I will provide the responses we gave to attendees – how you can solve the issue, and where you can start.

Steve Remington – Principal Consultant and Founder, Minerra

Is your organisation using multiple analytics tools? Are your employees making the most of these tools? Are some of these tools perhaps redundant, or have too many overlapping features? Minerra can help assess your needs and weight them against what you have to provide you with a plan to streamline your analytics tools. Contact us for a casual chat to see how we can help.

Image Credit: Jarek Tuszyński via Wikimedia Commons

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