One size does not fit all on the analytics roadmap, but don’t worry, help is at hand.
Funny thing, slow punctures. You know when you've got one, and you also know that they never magically self-heal as you'd like them to.
Even so, rather than repairing or replacing the tyre, you’ll choose to work up a daily sweat with the foot pump and live with the subconscious fear of a potentially much more expensive total blowout.
Which is a bit mad. You know you’re going to have to sort it out at some point, so why not do it when the problem first arises? That way, your car can function at peak efficiency for longer – and for all you know, they might even have brought out a new tyre with magical self-healing properties. Intelligence is king.
Being aware of what’s going on and where you are on your business journey is an easy and very useful kind of intelligence, and the physical means to corral and expedite that intelligence has been around for longer than you might think. Iconic tea shop chain J Lyons & Co took delivery of its first computer in 1951. One of its very first tasks was to decide on the number of cakes and sandwiches to make for the next day. Lyons called it “meeting business needs through actionable information”.
The ability to leverage information remains at the heart of every business. Today, it’s called analytics. Gartner recently reported that of all the marketing capabilities required to support strategy delivery in the next 18 months, marketing and customer analytics was considered to be the most vital by CMOs. The only difference between 2019 and 1951 is that there’s an lot more actionable information around these days. If your grip on business intelligence is looser than that of your rivals, you won’t have to waste much intelligence on working out the direction of the line on your profits graph.
With watercooler chat now turning to scary-sounding concepts like artificial intelligence (AI) technology and ‘deep learning’, there’s an understandable temptation to stick with your existing analytics foot pump, but in a business environment running hard on compressed air, that’s only one step above opting out.
Reassuringly, cranking up the pressure isn’t a risky or painful experience. The grand analytics roadmap has routes for everyone, and there’s honestly nothing new in the techniques that are deployed on the journey. And thanks to the growth of affordable cloud computing and scalable computing power it’s all become a lot more accessible. This is reflected in the wider availability of qualified and experienced resource too.
Stage one of the analytics roadmap is the elementary business intelligence (BI) that your trusty Excel spreadsheet can provide. It’s handy for board reports, management reporting, KPI tracking and other operational measures. With a little help from your data engineers you can put more building blocks in place and move swiftly onto stage 2.
Getting your data in one place will give you a richer data source and a deeper well to drill down into. Excel is still useful at this level, but you might also want to look at software packages like Tableau, PowerBI or QlikView which come with snazzy dashboards (for executives), reports (for managers) and pivot tables (for analysts). Recent developments like self-service analytics also aim to make it easier for end users to produce outcomes without having pick up the phone to more technical IT bods.
Changing business needs and priorities mean that there will always be a need to provide ad hoc insights. The ability to ‘deep dive’ into the data, pinpoint trends and provide non-regular insights marks your arrival at stage three of the analytics journey. Supported by your robust and reliable new data warehouse, your BI technology stack can help untangle some thorny business questions, especially when it’s supplemented by skilled resource and free open source programming languages like ‘R’ or ‘Python’ which help to open up flexible new data-interrogation channels.
Stage four on your journey is the core analytics capability. Providing predictive and propensity modelling and segmentation, this more advanced technique is supported by a statistical toolkit. Excel, Tableau and other dashboard reporting products are still likely to be required, with additional capability deliverable through investment in products like SAS, SPSS and the open-source programming languages already mentioned.
Now we’ve reached the elephant in the room – AI (artificial intelligence). AI is based on the concept that systems can learn from data, identify patterns, make decisions and carry out specific tasks with minimal human intervention. Often supported by enterprise scale solutions like IBM Watson, Pega and SAS, it’s becoming the go-to solution for complex problems involving a large amount of data and lots of variables, such as (for example) online recommendation offers from Amazon, Netflix and others.
Peter Drucker famously said, ‘if you can’t measure it, you can’t manage it’. Measurement must be customised; it must reflect the dynamics of your industry and the requirements of your business, both strategic and operational. That’s a focused view, but the analytics roadmap has a pleasing and reassuring generality and openness about it. There’s something there for everyone.
Big changes always start with small steps, and companies like Optima are there not only to help you to walk before you run, but also to provide you with the right trainers – and to show you a long-overdue and powerful alternative to the foot pump.
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