Analysing the analytics

By Cliff Brereton, Co-Founder of from Intense Computing.




Many people do not realise the returns from data analytics projects. They lose money – that’s the truth. They lose money and CIOs are under the cosh.





Joint research by Capgemini and Informatica found that less than a third of big data analytics projects are considered profitable. The research suggested that the other two thirds of big data projects in the research are either losing money or only breaking even - 12 per cent of those cannot yet determine whether their big data projects are profitable.





The reality here of course, like any new hype technology, is that businesses have rushed out and invested in big data technology and systems with little planning or fore-thought. Their competitive pressures are so high that the chance to grab immediate insights and a quick fix has driven them to make risky investments. The vendors didn’t say stop, and why would they?  





Where businesses have gone wrong is a failure to analyse their operations before analysing their data. They’re running before they can walk. They’re using gut feel to decide what to measure with their new Business Intelligence (BI) system or analytics software.





This doesn’t need to be a perpetual task, but a small amount of pre-data analytics work could save businesses a lot of wasted money. I recommend customers consider six small steps before making an analytics technology investment.   



Clarify the business strategy and its measures. What are the priorities for the business? What are the Key Performance Indicators for each business area - R&D, supply chain, manufacturing, marketing and sales, HR, and finance, etc.? These need to be clarified, understood and agreed.    

Align data. Do you have the data in your business to support your key performance indicators in each business unit? Where is that data? There are potentially a dozen or more categories of data – big, small, internal, external, simple, complex, cheap, expensive, etc.. Have you identified all of these?

Apply SMART. Identifying data and KPIs is only going to get you so far. Businesses also need to ensure those KPIs are Specific, Measurable, Achievable, Realistic and Timely [SMART], otherwise no amount of technology nor data will help you to get any valuable insights. Once this is done, customers should start to see the fog clearing.

Calculate your potential ROI. What’s the likely return from your analytics projects vs the cost of new systems and data collection? How hard will it be to build an infrastructure to support your project and find the data? Would the return on investment be 10x, 5x or 2x the investment cost? What should the priorities be, e.g. focus on a massive project with a 10x potential return or do lots of smaller projects with only a 2x potential return? A good supplier should be able to provide you with the necessary business models to help calculate potential return.

Gauge the competition. No business is an island. It’s essential to analyse the public domain to see what competitors look at in terms of analytics. Is the competition more or less advanced than you? Do you need to go for a massive, costly technology investment right now if the competition isn’t even close to doing that itself?

Be an organisation that’s ready. This is a really important step - taking a cold decision as to whether your business is ready to make an investment. Is every department ready or structured to work to KPIs, do you have all of the data necessary; is there a culture of sharing data between departments,  is the business in a position to act on any insights from the analytics project?



The vast majority of customers we work with are operating at only steps 1-3, which is an indication of how few firms are really ready to make a big data technology investment. When we help customers through these steps its often the case that organisations are simply too siloed internally to understand their KPIs, collate data and act upon it. Sometimes they don’t have the leadership to act upon the findings from an analytics project. Occasionally, when we apply our own business models to the data at step 4, the potential ROI isn’t great enough – although I’d argue even a 2x return and the chance to double your money is probably worth the investment needed.





For me, its absolutely essential that businesses apply these six steps before making an investment in technology to ensure a big data project will have a much higher ROI and chance of success versus the rest of the market.      




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