My clients often see a lot of value in enterprise data management efforts (whether MDM, CDI, data quality efforts) as a purely back end, technical effort. However, in order to add even more value to the effort then we often (read always…) need to look at the underlying business processes rather than looking at data and technology in isolation. As always, there needs to be a good understanding of the processes before we can work out what can be automated. What often happens in data-oriented projects is that the first choice for automation is reporting. This can be a trap which we need to be clear with the sponsor/client about the direction the project will take.
As a provider of enterprise data management we have to be clear about what we’re there to do. Typically, this is to improve the quality of some aspect of the organisation’s data landscape. We can’t do this unless we’ve included in the scope of the project room for the other classic drivers of people and process along with the more upfront data and technology.
If changes to process and training/support for people are not in scope, the data might be good for, oh, about a minute, then the processes (and the people..) that populate the data stores are off again. When managing the scope we can be careful to steer away from ‘more reports’ and include a chunk of process optimisation to align with the goal of the project, again, improving that set of data. Because process and people work and software delivery work are very different beasts, the timescales involved mean that if the programme can be managed on differing timescales then they should be.
The work around process and people is so important in a data management programme because it involves improving the quality of data at source. Technology can only do so much and its job becomes a lot easier when the processes and people are working in the right direction.
The superior doctor prevents sickness; The mediocre doctor attends to impending sickness; The inferior doctor treats actual sickness. Chinese Proverb
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Intro.
Hi and welcome to my blog. I want to use this to communicate the up and coming trends in biz intelligence, enterprise data management, including master data, enterprise information integration, operational intelligence, and historical insight including predictives. The overarching aim though is to focus on the areas of data integration which actually deliver increases in business performance. A lot of sales fluff goes into making up new acronyms and jargon. I’m not a big fan of all this.
To start, let’s go for a ‘state of the nation’ around this wide area. What will BI, data integration actually consist of in the next year or two. Here’s a load of words. I’ll follow up on these in the future
Master data management(including customer, product, organisation, and the other big dimensions of a business)
Enterprise metadata
Operational analytics. (is this an oxymoron?)
Predictive algorithms (the future – David Bodanis when he addressed the firm last week)
Historical integration (traditional data warehousing? )
The big question is, as always, why? We’ll follow these up over the coming weeks.
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health & safety
One of my colleagues and I wrote this after the Buncefield explosion in the UK in late 2005. I’d done some work with a major energy firm on the data integration and reporting around their health & safety. Hopefully more energy firms will look into using their historical data to try to predict future events.
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information & decisions 