Gustav Piater, sales and marketing director at AIGS (Yellowfin South Africa), explains why it’s important to have one version of the truth.
It’s a familiar scene: The monthly sales management meeting began well enough, only to descend into claims and counter-claims about whose “data is better” — sales or finance?
It’s one of many possible results of poor data governance, and it should never have come to this.
Traditional BI governance fails
After all, you’ve dumped your old approach to BI, in which you pulled data from the source application into a data warehouse with business rules, and from there into spreadsheets and mailed out to business users.
In that scenario, spreadsheets were the clear culprit. They sprouted like weeds in every department, each with its own way of calculating the sales figures, each with its own formula, and more than a few with their own hidden calculation and transcription bugs. Clearly, the rules of data governance — defined as the overall management of the availability, usability, integrity and security of the enterprise’s data — could not be observed.
New BI governance fails
So why did the new BI tool fail (statistically only 13% BI adoption worldwide)? How did the same old spreadsheet-induced governance problems resurface? Chances are your organisation has fallen prey to a common problem with many of the new tools.
Due to a growing need for real-time data, modern BI tools tend to pull data straight from source applications without passing the data warehouse layer, and then commit the old cardinal sin of allowing users to export it into Excel reports — without defined business rules.
Like the traditional way, this is an outright and immediate breach of data governance, opening the organisation up to many ‘versions of the truth’.
So how can it be fixed?
Only a 100% Web-based platform deployed on a centralised information architecture can provide in-system governance with models governing user-, group-, content-, function- and data-level security.
In this scenario, BI is done within a closed, centralised security environment in which data is extracted and collaborated on, but only by users with rule-based access and sharing rights (agreed upon between IT and the business).
The difference between these three basic approaches is architectural: The principles of data governance must not only be observed, they must be implemented centrally in a single integrated environment, thereby applying to everyone equally and ensuring a single version of the truth.
A single-platform approach offers several clear data governance advantages:
* Complete data lineage: Business users and data experts alike may modify existing data and create new data, because a single system reveals how it was calculated, by whom and when.
* Complete activity lineage: A single system also exposes actions that don’t necessarily alter data, such as collaborations, messages and implementation.
* Standardised and complete: To attain one version of the truth subscribed to by the business it must have a comprehensive store of business and technical metadata; shared, reused business logic; and standardised definitions — which are best supported by a single system.
* The consistency, lineage and tracking needs of data governance can only be managed with a single logical security environment, as described above. A single BI platform offers a simpler and more direct way to enable and drive this.
The best of both worlds
An integrated BI platform also supports a finer understanding of BI, one that recognises the coexistence of two fundamentally different approaches to BI-supported decision-making — self-service and a highly-governed, centralised setup.
These approaches occur in every organisation:
* Individual users tend to undertake highly innovative, ad-hoc exploration of data to address immediate questions. Often performed in spreadsheets with minimal governance, it has long operated under the radar of effective BI governance.
* The second type is the formal or regular reporting and analysis based on pre-approved data and using agreed tools to ensure repeatability and quality of results, usually associated with a data warehouse and centralised BI implementation.
The secret to BI success is to combine and enhance both approaches in a centralised process, thereby both encouraging creative use of data and analysis, and — as appropriate — to bring the results of such creativity into a more formal and governed environment.