Analytics and Big Data – the terms are out there and run off the tongues of techno-geeks and many consultants very easily, but what are they and should the average small business be worrying about them?
Big Data is pretty well what it says on the tin – data and a lot of it. Increasingly there are things around the house and office that generate big data – energy meters are a particularly good example. However every time you access the internet and use something like Google or try booking a hotel, order a book off Amazon or download one onto your Kindle, you are creating data that is captured in one or more of the Big Data warehouses out there. Your own website will also capture data, but I suspect that for most of us, the traffic will not be enough to call it Big.
Analytics is the term used to describe how that Big Data is used and interpreted, and has grown out of a number of disciplines including statistics, computer science, operational research, maths and heuristic development. People might try to sell you analytics, but in many cases you are just getting some data presentation and a bit of statistical analysis, not the full fat Analytics that are helping corporations and Government organisations to sell you things. You need a lot of traffic and a good dollop of cookies on your website to allow the sophisticated analysis and action that the big sites use.
For a small business, particularly one working with or selling to the public, you will generate data, and depending on your reach and your customer numbers, you may start to think of it as big. For the most part, you can analyse that data using Excel yourself, because Big Data is also real time and really big.
To put this in context, I used to work a lot with the annual data set of all qualifications being studied at FE Colleges in England. At a leading edge Analytics conference, I discussed it with a big data/analytics person, and she said that it was too old, too static (so not real time) and too small at a mere 15 million entries to really be considered alongside the work presented at that conference. The analysis I did could not influence or change the behaviour of the data items (students) either. That day they had the energy companies talking about smart meters and instant consumption data, and Hotels.com and booking data, and how these could affect power generation or inclinations to take a short break. I felt very small and very passé.
I have kept up with the debate of course (despite continuing to feel small and a bit passé). The next big thing in analytics is going to be text interpretation so that analytics results can predict the future and not just project the past. It is not something I will ever use in my work however I will be looking out for evidence of it in my interactions with the big e-commerce sites and Google – and looking for a way to prove them wrong too!
Jane Holland is an Associate of MRE, a community interest company (CIC) offering ethical business support, advice, development and practical services in starting, running and building their business.
Jane is our guest blogger for November
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Lots of small and medium sized businesses make really great products and offer first rate services. In many cases they are better than the established big brands, and yet in their desire to challenge the market leaders, most run in to the same obstacle:
The prospect or potential customer makes a poor decision, sticking with the established supplier even though the product or service isn’t as good.
Many of the challenger brands are striving to become the new leader, the ‘default’ choice in their market.
So why do defaults persist, even in the face of ‘better’ options? Here are four factors that help explain why customers stick with their established choices and don’t make ‘rational’ decisions:
Inertia – sticking with what we know usually requires the least effort on our part as customers. When we are in a hurry, it’s an easy solution.
Lack of expertise – in a situation where we feel unsure of ourselves and our ability to select the right option, the ‘default’ can be seen as representing advice from more knowledgeable customers.
Risk of change – while implicitly accepting that ‘the usual’ may not be the best choice, we know it well and what we might miss if we switch to an alternative. Sometimes the known ‘loss’ can outweigh the potential ‘gain’.
Too difficult – our initial enthusiasm to find a better alternative can evaporate if we discover that comparisons are complex and drive us back to our regular option.
So, armed with this understanding, what can we do?
As a challenger, consider each of these potential barriers and find ways to reduce them or even remove them completely. Can we make switching easier, can we give prospects the ‘expertise’ to make a better decision, can we mitigate the risks?
If we are already the ‘default’, we need to emphasise what our customers get from us, to reduce the risk of complacency, and highlight the risks of switching. Oh, and keep an eye on the challengers too!
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