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The leading knowledge platform for the financial technology industry

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Accenture’s Five Steps to Data Analytics Heaven

Given the regulatory attention being directed at the industry’s dealings with its data fundamentals (see the host of recent reference data related fines and proposals for the establishment of data utilities for proof), it is no surprise that consultants have been quick to highlight other industries from which the financial services industry can learn a few tricks. A case in point is Accenture’s recent think piece on turning data into a strategic asset via its simple five step model that purports to place analytics at the centre of the decision making process.

Accenture indicates that financial services firms are currently failing to recognise the full value of the data they hold and hence unable to realise the analytics potential that has been a characteristic of the retail world for some time. The “data gold mines” buried in firms’ operations can be mined to add value to firms’ business, contends author of the paper Jeanne Harris.

The idea of learning a few tricks in terms of better managing and using data is not new. Panellists at a Thomson Reuters event earlier this year discussed the potential to learn data management strategies from the pharmaceuticals industry, for example. Since the rise of internet giants Amazon and iTunes, the industry has also been pondering how to adapt its models to better reflect the digital age and track its customers’ spending propensities. So what, if anything, new is Accenture proposing?

The consultancy firm is touting its own five step model, dubbed DELTA (the basic tool of the management consultant is an acronym and this one stands for ‘data, enterprise, leadership, targets and analysts’), to put analytics at the core of a business.

In terms of data management, the article suggests that data, rather than a by-product of business, should be seen as a means to getting an “analytical edge”. This has certainly long been the mantra of the enterprise data management (EDM) vendors out there, but with so many other things going on in the market, is this really the driving force behind EDM management buy in? Risk avoidance and staying out of jail is probably a better bet.

Much the same as EDM, Accenture also stresses the importance of an enterprise-wide approach to data standards and sharing, before analytics can be added on top. However, Harris accepts that silos remain within firms and these must be taken into account: “While integration is critical to the success of analytics, it has to be done for the right reasons, and with a healthy respect for its limitations.” All sensible advice, but still nothing new.

The ‘leadership’ component comes in the form of an “analytical leader”; similar to the role of a data champion it would appear. Data managers have long appreciated the importance of a senior level crusader to drive forward a significant data project (see discussions at last year’s FIMA for example). Accenture’s version however, is an individual that leads by example in the analytics field and teaches the rest of the firm the benefits of using analytics in decision making.

‘Targets’ refers to the need for clear objectives and benchmarks to be set in these projects. Rather than a sprawling and potentially lengthy endeavour, Harris indicates that firms should instead identify areas that can receive the biggest payoff. Again, tactics that are often deployed at the start of a data management project; the low hanging fruit if you will (see Julia Sutton at RBC’s approach to this on the data management front here).

The last component of Accenture’s model is ‘analysts’, or the people involved at the grass roots level. Harris identifies four general types of analytical talent and how to spot them within your organisation: champions, professionals, semi-professionals and amateurs. It’s easy to see which category the largest proportion of industry participants would fall under from the data management point of view…

You can view the full article here.

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