Data is an ingredient. But you need the recipe as well!

It’s not just about the data – it’s what you do with it and how easy it is to gain insight.

As I sit writing this blog post I’m in my favorite restaurant in London, Bentley’s Oyster Bar and Grill and the owner and TV chef Richard Corrigan is sitting at the other end of the bar. His books and dishes are amazing, but while he values and is passionate about his ingredients and where they come from, he also talks about what you need to do in order to turn great ingredients into a great dish.

This got me thinking: imagine a chef that only talked about his ingredients, not how to use them. Not much use. Yet that is exactly what many do when it comes to analytics when they just talk about data. To me, data is just a source, an ingredient and therefore it’s worthless without the recipe.

This casts my mind back to a blog post I wrote last year titled “Big Data will Save the Planet.” It was designed to challenge folks to stop coming up with new tabloidesque headlines about the fact that we have ‘big data,’ and instead focus on the great use cases for it. I was reminded of this at our employee summit in Mexico two weeks ago, where not only did we preview (sorry for being a tease) some of the really cool things we have on our roadmap but we were also able to hear about some great analytics use cases from some amazing customers, partners and our global consulting colleagues.

In every example I saw, none only talked about just data. They all started with a use case, a business challenge they wanted to address or sometimes an opportunity they could not afford to miss. They went on to talk about how they used analytics to address that problem. Sure, data is part of this and of course you have to have it but as I suggest it’s frankly just a source. The real conversation is how you use it and for what purpose. Let’s take another example: in December we published the story of retailer and food manufacturer COOK and the applications they developed to track demand over the holidays. Throughout the story the focus is on the use case, not the data – it’s also an apt story for my ingredients plus recipe analogy.

My point of view is that the conversation has to focus much more on the “how”. Analytics and big data solutions need to be designed for every use case that an agile business user might need or see. They must take advantage of an opportunity to address needs while providing the governance framework that IT and the wider business relies upon. These solutions need to be easy to use and incorporate visual analytics that are deployable at the point of decision at every level in the business. They should also be integrated alongside every business process and support enterprise applications: taking advantage of all deployment scenarios (cloud, on-premise or embedded). As a key design principle, rather than being mobile ready, they need to be built for any device with touch. This is precisely the approach that will allow the collective intelligence of humans to be leveraged to solve business problems and find new opportunities.

Data is just a source, an ingredient and therefore it’s worthless without the recipe.

Talking about the “how” is therefore far more interesting than talking about just the data they use. Imagine an analytics vendor that puts just data at its heart, essentially ignoring the people, the real humans whose job it is to derive the insight from the data and solve business problems. They are ignoring how and where their customers use their solutions and how they themselves go about designing analytics solutions that really get the best from the people that use them. Essentially they are just talking about ingredients, without any of the recipe.

As I said earlier, I don’t think that’s much good but it also raises a question for a future blog. The best chefs develop their dishes and taste as they go. They are agile and they innovate and I think this too is true of analytics. The ability to sample the end result (taste your food) and adjust along the way is essential to creating a quality result. Sometimes this process means you need to bring in additional data and analysis (new ingredients) that you didn’t expect originally as a result of what you discover (tasting).

Let me know your thoughts on this…


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