Guinness' next round of software

Guinness UDV has rationalised the use of online analytical processing tools developed piecemeal around the business to build a...

Guinness UDV has rationalised the use of online analytical processing tools developed piecemeal around the business to build a common data model.

Stories of companies embracing the e-business model are getting old hat now and many can point to a long, if somewhat chequered, e-history. But the story does not end at the point where the company embraces the Internet as a viable business medium and develops an e-strategy. There is still work to do before it can live happily ever after.

Grappling with the effects of e-business and adapting to the changes it imposes is an ongoing process. One major task is managing the quantity of data that is generated by Web transactions. For alcoholic beverage firm Guinness UDV this led to the implementation of a common data model based on business intelligence software.

Guinness UDV was formed last year when parent company Diageo merged its two alcoholic drink interests, Guinness and United Distillers & Vintners. Both had been using business intelligence for some time but, according to Guinness UDV's data technologies director Steven Sharpe, before 2000, "It tended to be tackled on a fairly market-specific way, with each market doing its own thing."

Compounding this problem was the fact that, following a series of mergers, Guinness UDV found that it was using a variety of business intelligence offerings, including 17 different online analytical processing and reporting tools.

Sharpe says the company realised it needed to set up a "more standardised, common approach" across all of its operations. "What we really needed was a data management infrastructure of which the reporting component was very important but alone would not succeed," he says.

At the beginning of 2000, the company began work to develop a common data model, taking an approach that Sharpe says "you might call datawarehousing". Its initial aims were to allow the integration of data from multiple sources and to provide an aggregated view of that data. For this, the group turned to business intelligence software from Cognos.

As Sharpe explains, Guinness UDV already had a large number of Cognos licences deployed globally. Cognos' pricing and support models were appropriate and it had a good approach to managing a relationship.

"It was the right answer for us at the time and it continues to be the right answer," says Sharpe. "Cognos supports business change."

Once Guinness UDV had consensus on this approach, it implemented four pilot projects covering the key areas of its business. Datawarehouse models were set up for sales, supply chain, manufacturing and finance - each using Cognos business intelligence software at the front-end. These pilots were run in parallel and lasted an average of nine months.

Based on the results of the pilots the company came up with a single standardised answer. Sharpe says that solution is now being deployed in all the company's large and medium markets and the group is considering whether it is appropriate for its smaller markets.

Sharpe claims there were no major teething problems but admits there were issues that needed to be resolved - the key one being data quality. He says the success of business intelligence solutions often relies on the quality of the data so any issues need to be addressed fully and methodically.

"You can have the best technical implementation of business intelligence, but unless you have good quality data you won't have a successful solution," he says.

"Ideally, you need to change the structure and culture of an organisation so that data quality is managed at source," says Sharpe.

But cultures don't change overnight. And although Sharpe says that using data cleansing tools is not the answer, as they only offer temporary solutions, he admits that in practice they are needed to complement this approach. No matter how good your business processes are, there will always be some failures, he says, and "data quality is a process failure issue".

In other words, companies should manage data quality at source but they should also expect problems to occur. These can be resolved using data quality tools. Reporting tools can then be used to identify which processes are failing and effectively "close the loop".

Firms need to realise that "data ownership is not an IT but a business issue", says Sharpe. However, getting the business side of the company to take ownership of data is not easy, not least because "data is not sexy - it is pretty boring stuff, quite honestly".

Sharpe believes a key reason for the success of Guinness UDV's content data model and its use of business intelligence software is the work it did at the beginning to make sure end-users would buy in to the system. "At the end of the day, you have to sell business intelligence solutions to your organisation," he says. "Buy-in at the start is so important."

Guinness did this in a number of ways. It ran a series of global workshops and identified interested parties - typically business intelligence and datawarehouse managers - and got them involved in shaping the model for the business. A 15-strong "global application team" was established at the beginning of 2001 to oversee the project. "It maintained a standardised approach to maximise return on investment; minimise time to deployment and minimise cost."

But this standardised approach is not set in stone. Sharpe says that flexibility has been built into the model to allow for country-specific differences, such as pricing models. The company also plans to make enhancements to the underlying common data model to allow it to adopt a more flexible approach to hierarchy management and incorporate hash key algorithms - which are used for creating digital signatures.

Although it was originally developed as a logical data model, the common data model has become part of the global IT strategy and is now the standard Guinness UDV information management solution. Sharpe says that demand among its users is "far greater than we'd anticipated, so we must be doing something right" - a point he illustrates by quoting an Australian user who likened the transition to a move "from shithouse to penthouse".

Software in the public house

Guinness UDV found it was using many different business intelligence and reporting tools to cope with all the data created by e-business initiatives and wanted to create a standardised approach to data management

The company created a common data model, incorporating software from business intelligence firm Cognos.

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