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image image 17th December 2015

Riding the Unicorn – Aligning Technology, Data & Analytics Strategies

Now that I’ve got the customary ‘unicorn’ reference out of the way in the title, down to the serious matter of discussing why, given that it’s so gosh-darned important, we seem to repeat the same mistakes when it comes to Technology, Data & Analytics.

First of all, let me say that there are many organisations out there, some big, some small, that are doing great things with technology, data & analytics. However, it would be all too easy to simply wrap things up and conclude “Just do what they do!”…most of them write about it. If it were that easy, why aren’t so many of us doing it already? The simple reason, it’s not what they’re doing that, I believe, is the most important aspect, it’s how they are doing it.

The wealth of data at our (potential) disposal is greater than it has ever been and will only continue to grow. As such, we need bigger, faster and more sophisticated ways to interact & use that data, right? So, where do we start…we need an IT project! Right there is problem 1.

IT can be and is a great strand of any business, but part of the reason we struggle to deliver excellent analytics is because at the outset, we view these as needing just another IT project. Analytics is neither a product, nor a system or a platform. It cannot be ‘architected’ in the conventional way. Analytics is the search for value in data and as such, needs to evolve as the business evolves to the decisions that previous insights have helped drive. So…bigger, faster, more sophisticated? Nope. Just flexible.

Every business is on a different part of its analytics journey. Some are in the phase of a proliferation of Excel workbooks. Some are having that long wait for access to a data source granted by their DBAs. Some are using that all-singing, all-dancing cube that was set-up 10 years ago to answer every question we had. Some have embraced the latest trend and employed a Data Scientist to solve all their problems.

So, when it comes to the CTO making decisions, we often hear words like “scale” & “enterprise”. When we hear the COO talk, we hear “fast”, “flexible” & “simple”. The CFO combines these 4 words and concludes “expensive” and starts to panic, but is assured by the CDO that if we don’t use data we’ll sink, so he signs the cheque. That merry-go-round seems an almost annual topic of discussion in recent years and yet little progress ever seems to be made. That’s where problem 2 comes in.

When we make technology decisions, who is making them? And to what end? We seem to make quite significant advances in operational, transactional technology quite regularly, yet analytics is still floundering. Why is that? Businesses need to be highly engaged in both aspects of their platforms. All too often, we focus on the transactional needs of business systems and usually forget to consider how we want to analyse the new data & patterns that these new systems generate until after the event. All of this can be resolved by something as simple as thinking & talking to the front-line of the business at the outset. An analytics strategy cannot be separated from the data strategy, nor the technology strategy.

However, it can all get a little overwhelming. If we’re going to align & co-ordinate all these strategies, we need Governance Committees, rules, integration, training…the list goes on and on until ultimately, in order to ensure we deliver something, we spend big and choose the path of least resistance. How did that work out for you? It didn’t? You’re not alone.

Whether it’s Big Data or just regular, everyday small data at the heart of your analytics, data & technology strategy, there are a few key aspects that, in my experience, will help ensure that you don’t suffer the same mistakes again.

Don’t overstretch yourself at the start

Solutions don’t need to be big to be advanced. Start with the data you currently collect. Start with the current set of business problems. Ask yourselves what the burning issues are. What kind of solution(s) could solve these problems? But be ready to support scale out, as once you ignite the fire of insight, it will spread rapidly. Keep things small & modular. Have you seen what they can build out of LEGO® bricks? Doesn’t that sound infinitely more scalable & manageable than trying to sculpt that masterpiece from one big lump of rock?

Keep your governance & road map simple

Foster a culture within the business that promotes communication at all levels, not just managerial…we are, after all, all working for the same company aren’t we? If you haven’t explored the value in your data, it’s a little hard to start stipulating the right governance & security procedures if you don’t fully understand how that will impact users, but we need to ask them and get them involved to even begin.

Make the business accountable for the solution

Massive IT budgets tend to hide massive failures. If we put the business at the centre of the project, we have more than a vested interest in making sure it succeeds and will, hopefully, remain engaged throughout.

Remember, this is not a one-off

There is a reason we talk about road-maps & journeys. There will be several stop-offs along the way, perhaps lasting months. The business will seek to implement decisions generated by the initial insight your new analytics, technology & data improvements have helped enable, but pretty soon, they’ll be seeking new & ever more valuable insight. So, be ready to keep developing & innovating extensions & advancements in those solutions.

But don’t be tempted to tie yourself into long supplier contracts & licences. It may seem like economies of scale, but no one will thank you when you’re sat on £1m worth of licences and/or hardware and can’t change anything for 3 years. It’s not easy, but in the long-run, flexibility will allow you to create value that will more than offset the savings you would have made through those tie-ins.

Don’t forget training

If your analysts & end-users all currently use a particular tool, try to understand why they use it. Is it the best, the easiest, the most flexible? Is it just what you gave them 10 years ago? Where do those toolkits sit in your new world, if at all? Introducing new toolkits to a large number of people all at once will typically result in mass resentment, mass hysteria or mass resignation. Whilst it is true we may need to bring new people in, if we engage existing users at the outset and understand their requirements, we can make sure that key players get exposure to the new toolkits and start to extol its virtues within their teams and put the right training plans in place.

And finally…

Don’t expect perfection

You’re going to run into problems. You’re going to change your mind. You’re even going to fail. Embrace and accept it. If you foster that collaborative culture, make flexibility your focus, those failures will be small and few and far between. Your analytics should enable you to make smaller, more frequent value decisions and you’ll learn more from every one – win or lose.

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