An intelligent approach to data

Exploiting the data stored in finance, sales and marketing makes a company a stronger proposition. Nick Britton discovers how to turn your data dump into a strategic gold mine

For an increasing number of companies, managing data isn’t just a necessary chore, it’s an integral part of their business model. Scott Kiehl, head of business intelligence (BI) at gaming machine operator Inspired Gaming, says that the way in which his company exploits its data is the foundation of its success.

‘Previously, a collector would pick up money from the machines weekly,’ explains Kiehl. ‘We would wait to see when revenues for a particular location dropped off, then send over a van with a new machine.

‘Now our networked machines run on a common platform. We can pull data back from them on an almost transactional basis then send an invoice [to the venue hosting the machine] based on that information. This is also processed, allowing us to determine whether content is or isn’t working.’

In total control
If a machine rejects a certain number of coins per hour, for example, a message is sent to the nearest service centre so that maintenance staff can be dispatched if necessary. Similarly, if a game isn’t proving popular, a new one can be uploaded.

It’s hard to put a figure on what the advent of networked gaming machines has meant for Inspired Gaming, says Kiehl. In the seven years since the system was introduced, the company has expanded into seven countries other than the UK, and it listed on
AIM last year with a market capitalisation of £115 million (now over £200 million). The international growth would, Kiehl suggests, have been ‘difficult, if not impossible’ without networked machines.

Inspired Gaming shows that BI can have a dramatic impact on a company’s bottom line and its scope for expansion. It’s a pity, then, that growing companies don’t always make the necessary investment, says David Hobbs-Mallyon, BI product manager at Microsoft.

‘Some organisations see BI as more of a luxury than a necessity,’ he admits. ‘But with organisations looking to expand in a systematic manner, having the right information at their fingertips is exactly what will differentiate organisations from the competition.’

Laying the foundations
Hobbs-Mallyon adds that before embarking on a BI project, companies need to get their data in order. That might mean customer data, sales data, or data from the supply chain. Most likely, it means all of it.

‘You have to take a strategic decision somewhere along the line that BI is something of importance,’ he says. ‘When you make that decision, that’s when master data management gets on the agenda.’

As much a management philosophy as a set of technologies, master data management (MDM) promises a seamless integration of data across the divisions or departments of a company.

That may sound more like a vision of IT nirvana than an achievable goal. Tony Jaskeran is the man charged with making it a reality in his role as head of BI at Allied Bakeries, owner of the Allinson, Kingsmill and Burgen brands. ‘We see information as the primary leverage in helping us achieve our business goals,’ he says.

Over the past century or so, Allied Bakeries has accumulated a vast trove of data. The problem is the treasure and the trash is spread across a large number of business systems: 131, to be precise. Of these, the majority were developed in-house.

‘Over the past decades, if you needed a particular process or application, you designed it yourself,’ Jaskeran explains. ‘So you develop more systems without really introducing classical processes.

‘Another problem arises when companies merge. The target company often has its own processes and applications, and integrating these with existing systems can be difficult.’

Smaller enterprises may not face issues of this scale or complexity, but problems of inconsistent duplicate data systems, and the difficulty of getting an overview of the information you have, will strike a chord. Even MDM vendors agree that, for most companies, harmonising the various strands of information is a long way off.

‘I would estimate that fewer than five per cent of businesses have implemented any holistic data strategy,’ says An-Chan Phung, chief technology officer of system integration specialist VisionWare. ‘MDM is still fairly new, and a lot of people don’t actually know this sort of technology is available.’

Time to catch up
Tony Fisher, chief executive officer of software supplier DataFlux, goes further. ‘No company has actually finished an MDM implementation,’ he says, adding that only ten to 15 per cent of businesses are even ready to start. DataFlux has constructed a data maturity model that shows the journey a business takes when implementing MDM Click for to view table

‘Once companies make the hard decision to change, we move in a very methodical way to help people bring in elements of the system one at a time,’ Fisher notes.

A question of quality
Fortunately, there is widespread agreement about the first step in this process. An investment in data quality is essential for MDM to be worthwhile, according to Phung.

‘The standardisation of information is an absolutely key component in any MDM strategy,’ he says. ‘Information has to be fit for purpose – and that’s a controversial point, because people collect information that is fit for their purpose, not fit for the purpose of the organisation.

‘Employees have to understand that when they put in bad information, it affects the whole organisation. It needs a complete change of mindset.’
Phung states that it takes nine seconds to fix data quality issues at the point of entry, but 19 minutes if the problems are left until later.

Inevitably, getting your data in order is likely to require a serious investment. DataFlux’s data quality initiatives start at around £50,000 to implement, depending of course on the size of the company and what it wants to do. If successful, though, the process should improve your understanding of your business and ultimately bring financial returns.

The Number, which runs directory service 118-118, took the decision to improve its data accuracy two years ago, focusing in particular on duplicate records.

‘Because we take hundreds of millions of calls, even the tiniest errors were unacceptable,’ says Andrew Larter, data and development director of 118-118. ‘So our data processing volume and error correction operates on a very large scale.’

Larter claims that by using software that provides a framework for data quality and targets duplicate records quickly, The Number reduced the bad data in 118-118’s directory from five per cent to virtually none in six months. ‘We were able to totally eliminate the risk of bad data,’ he states.

Once quality is improved across a business’ various systems, it’s time to bring the systems together. But this doesn’t necessarily equate to centralisation, according to Jaskeran.

‘MDM is not about consolidating all your data into a single set,’ he explains. ‘In supply chains where you’re looking after products, there may not be an impact on the financial strategy, so there’d be no need to integrate those two areas.’

Points of agreement
Where there are ‘touchpoints’ between areas of the business, as Fisher calls them, getting agreement about frames of reference is vital.

‘A classic example is the question: what is a customer?’ says Fisher. ‘Finance, sales and marketing will all have different ideas about what a customer is. Is it someone we have sent a product to? If we have sent them marketing brochures, are they a customer?’ Implementing a master data strategy means that departments have to work together more closely, Fisher adds. It’s as much about people as it is about technology.

‘Cultural change is the biggest issue with any of this,’ he says. ‘People aren’t used to and don’t necessarily like working with other people. A manager might be very successful operating within the confines of what he does. Now he is being told: “You have to work with these other people here.” And that’s very hard to do.’

It’s an irony not lost on Fisher that many of the problems advocates of MDM are now trying to solve, were thrown up by the solutions his industry provided to previous problems.

‘Thirty years ago, business was all about handshakes,’ he recalls. ‘But over the past few decades, mom-and-pop shops have almost completely died out, and the intimate understanding they had of their business and their customers has been lost. So we created customer-marketing systems, which were fantastic – in isolation.

‘We created data warehouses to help us analyse data. But one big fallacy of data warehouses is that they do not drive analysis back into operational decisions.’

Fisher concludes: ‘Enterprise resource planning and customer relationship management both worked fine – but the two systems weren’t integrated with the data warehouses. So, in a sense, all this effort has been made just to get back to where we were 30 years ago.’