FOCUS 11
KPMG’s research has identifi ed three popular types of analytics, to all of which big data could make a crucial difference:
1 Core analytics
Amazon has relied on analytics that support the organisation’s core purpose and help achieve its growth targets. Successful companies in this area recognise that analytics is about both driving profi ts and monitoring trends internally and externally, which helps them stay ahead of customers, suppliers and competitors. For example, if you’re doing sentiment analysis on social media, you might identify a trend six months before your rivals, enabling you to steal a march on them.
2 Ancillary analytics
Some activities are not core to the business, but still require monitoring and improvement. Here the focus is on having adequate capability rather than being the best. Even so, data can still yield dividends. By sharpening its understanding of ancillary analytics, Heineken has been able to reduce the number of vendors it uses by a factor of 10. Better insight into its vendors has also translated into major improvements in its contractual arrangements with them.
3 Remedial analytics
Big data isn’t always driven by the need to seize an opportunity. When the worst happens, companies must quickly and accurately understand what went wrong and how to rectify it. Our research indicates that organisations taking a disciplined approach to the key areas of analytics are more likely to bounce back rapidly from a disaster. These three kinds of analytics are critical to long- term success yet will affect each organisation differently. The questions may be similar: Where would big data be a game-changer for the business? What supportive process, policies and structures will be needed? What expertise will add the most value when aligned to our business objectives? But the answers will most certainly differ from business to business.
For some, the immediate value of big data may reside in reducing churn in the workforce. For others, it may be about analysing why the ratio of fl oor traffi c to sales varies so much between stores. One upscale US retailer has used big data to analyse key data points (sell-through rates, out-of- stocks, price promotions) at the product or stock-keeping unit level, and at a particular time and location. This allowed the retailer to develop thousands of scenarios in order to assess the probability of selling a product, so as to optimise assortments by location, time and probability.
Some analysts have argued that big data’s algorithms can replace managers as decision-makers, but I disagree. Human insight remains vital. Mindless data sets can often miss the relationships that a human being might recognise instantly. Let’s say that you’re a supermarket branching out into insurance. If you found a data set that told you an applicant regularly fi lled their car at a petrol station in a rough neighbourhood at 3 a.m., this insight would affect how you handle that application. The key here is to understand how data sets can relate to each other.
© 2014 KPMG LLP, a UK limited liability partnership, is a subsidiary of KPMG Europe LLP and a member fi rm of the KPMG network of independent member fi rms affi liated with KPMG International Cooperative, a Swiss entity. All rights reserved.
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