Choosing the right analytics tool doesn’t always mean choosing the best tool

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Recently, my father decided he needed a new laptop. He doesn’t know an awful lot about laptops, so he went to the electronics store to get some help choosing the right one to purchase. He came home extremely pleased with himself, having bought a brand new Apple MacBook. He told me that it had an […]

Recently, my father decided he needed a new laptop. He doesn’t know an awful lot about laptops, so he went to the electronics store to get some help choosing the right one to purchase. He came home extremely pleased with himself, having bought a brand new Apple MacBook.

He told me that it had an incredibly fast processor, despite being one of the thinnest on the market. He told me it had incredible graphics for gaming, that it could sync up to all his other Apple products seamlessly. He also told me about how he could produce his own music on his MacBook, as well as a hatful of other features he had impressively remembered from what the sales assistant had told him.

I asked him what it was he actually needed to use the laptop for. He told me it was for browsing the internet and writing his new book. He doesn’t play computer games, he doesn’t have any other Apple products and he certainly isn’t a music producer.

So he had set out to buy a tool to fulfil his needs and has ended up with a something which cost twice as much as he needed to spend, with a whole host of additional features that he didn’t need in the first place.

Whilst it amused my family and we all had a laugh at my father’s expense, the lessons that he learnt should be applied to our clients. This is particularly relevant when they are building up their analytics technology stack.

The race for analytics

Our clients are experiencing an exponential growth in the amount of data available to them. They are under pressure to extract the value from their data, and exploit it to gain a competitive advantage.

Whether their ambition is to move from static reporting to more dynamic self-service BI, or exploring the art of the possible with machine learning, pretty much all our clients are trying to grow their analytical capability.

Building new analytical capabilities is no easy feat. It can involve organisational change, a new operating model and embedding new people, processes and technologies into your organisation. A key part of this is the need to upgrade your technology stack, and investing in new analytics software.

The paradox of choice

In his book ‘The Paradox of Choice – Why More Is Less’, American psychologist Barry Schwartz argued that eliminating consumer choices can greatly reduce anxiety for shoppers. Unfortunately, our clients will face the same anxiety when they delve into the options available to them.

The market for new analytical tools is incredibly saturated. There are a whole host of software options to suit every analytical need. The image below merely scratches the surface.

Figure 1: The Big Data Landscape 2016

Choosing the right tools to make up your analytics technology stack amongst the wealth of options is a real challenge. What are the ‘right tools’ for your organisation?

If we ask the CFO, they will tell you that the right tools are cost efficient when it comes to licensing, scalability and training their staff. They are after a return on their investment, and they don’t want to be ripped off by slick presentations.

However, when we ask the CIO, they are more concerned about choosing analytics tools from reliable vendors that can easily integrate with their organisations’ current data architecture, that are future proof and fit in with their overall IT strategy.

Let’s not forget about the people who will be actually using the tools. They want to modern and advanced analytics tools that are appropriate for their skillset and will help them make the most of the data they have.

These needs translate to a fine balancing act between a number of properties of different analytical software.

Figure 2: The web of selection criteria for analytical software

Be diligent

With a convoluted landscape of analytical software available and a web of selection criteria to negotiate, it can be quite easy to have a look at Gartner’s Magic Quadrant and plump for the ‘best’ software on the market.

This approach will rarely give you the solution you need. Instead:

  1. Prioritise your needs. Prove the Minimum Viable Product (MVP) before you explore the art of the possible. Define your needs and convince yourself that the software will meet these needs.
  2. Explore the art of the possible. New innovations in analytical tools and techniques are appearing at an exciting pace, so don’t limit yourself to your MVP. Just make sure that these innovations can add value to your organisation, and aren’t just a whizzy gimmick.
  3. Try before you buy. When identifying new software to build into your analytics technology stack, get your analysts hands on with the prospective tools. Try them out, develop proof-of-concepts and build real use cases to truly understand the strengths and weaknesses of each option. It’s like buying a mattress – you wouldn’t take the word of the salesman, you’d lie on it yourself!
  4. Involve everyone from the offset. Ensure that all stakeholders from finance, IT, the data and analytics community and internal business customers are part of the selection process from inception through to rollout.
  5. Be tool agnostic. Remove any preconceptions of different software you have. New analytical software solutions are appearing every week and what was relevant last year might not still be true.

If only my father stuck to these principles. He would have ended up with the right tool for his needs. Instead, he has ended up looking like a right tool himself.

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