22 Jun 2017
Big data is big business and in an ever competitive and volatile market, it can mean the difference between pennies or pounds when it comes to profit. If utilised properly, big data can give an organisation an edge over its competitors, reducing costs and increasing margins. Although it’s the current business buzzword, data is nothing new – organisations have collected information for decades, merely skimming the surface when it comes to tapping into its business improvement potential. The difference is that now, we have the technological capabilities to do something exponentially useful with it, and its these new abilities that are set to transform the way organisations operate.
Organisations have more access to data than ever before, drawing from information pools created from the collection of operational and point-of-sale statistics and can be applied to almost every industry. You just need to know how to use it, and therein lies the challenge.
The reality is that many organisations are failing to truly realise its potential. Big data is advanced analytics and the clue is in the name – with an emphasis on the advanced! A huge proportion of companies aren’t even at what Oliver Wight would call, a ‘capable’ level. They haven’t reached the maturity to competently handle data, meaning the complexities and logistics of big data aren’t being fully understood. In an attempt to tap into the trend, immature companies create dozens of data functions to examine reams of statistics, which they then don’t ultimately utilise. But, unless they dedicate time beforehand to understand what information they want, what purpose it’s going to serve and how they’re going to manage it, it’s an exercise in futility. Insights derived from data are only useful if they’re relevant.
Data quality is a major bugbear. A lot of high-level big data is built up from very small, everyday transactional data, and companies tend to not pay as much attention to its quality as they need to. At Oliver Wight, one of our improvement techniques involves asking companies to validate their data to evaluate its true accuracy and more often than not, they find their small data accuracy is just 5% to 15%. Subsequently, either organisations ‘fill in the gaps’ using intuition instead of statistics, or resources are wasted scrubbing the information before it can be aggregated and analysed. Or, in a worst-case scenario, the data is used in its erroneous state with dire consequences. Advanced analytics are about transferring data to true knowledge, and you can only do that if you can trust it.? ?
Even once data accuracy has improved, it needs to be translated from numbers into strategic business plans. The interpreters? Advanced analytics and data analysts. Advanced analytics is a mathematical tool which is applied to business data for the purpose of assessing processes, with the view of improving practices and ultimately, minimising costs and maximising profits. Pivotal to this translation, is the data analyst. Highly talented individuals with a very particular skillset, data analysts take aggregated data and analyse it to reveal insights regarding how organisations operate.
For a lot of businesses, the shift from experience-intuition modes of working to data-driven models may mean structural reorganisation and how well companies evolve to adapt will impact their success long-term. There needs to be a cultural shift to embrace the era of big data and a willingness to invest in the talent and technology that will make big data worthwhile.
In Part II, I’ll explain why you should be doing everything to make big data work for your business.