Data Governance Success: Exercising Positive Control Over Your Data
Colin Rickard, 23/11/2009, posted in "Analysis"
Colin Rickard is Managing Director, West & North Europe, DataFlux. Recruited as MD in January 2006 to create DataFlux UK Ltd, Colin brought over 15 years experience in the ...more info
Colin Rickard is Managing Director, West & North Europe, DataFlux. Recruited as MD in January 2006 to create DataFlux UK Ltd, Colin brought over 15 years experience in the construction and implementation of Data Warehouses, CRM systems and Managed Services in both the UK and U.S. markets. Since then Colin has overseen the establishment of DataFlux as a major European data quality brand, growing the company from a relatively new and unrecognised European market entrant to its current position as the major contender in the European enterprise data quality software field. ...less info
Companies are facing increasing challenges when it comes to managing data. The need to reduce operational costs and the demand to meet increasingly sophisticated customer needs are factors that put pressure on data managers and IT departments to deliver a consistently reliable data infrastructure.
Boardrooms, in turn, are increasingly realising that data is an asset―like other tangible assets in the company―that requires investment to realise a competitive edge and the important operational efficiencies that are critical in today’s challenging times.
Many consider regulatory compliance as the primary driver of data governance today, bringing with it the risk of significant punitive measures for failing to comply within a set time frame. In addition, these regulations require particular data structures or reports in place.
For example, in the banking sector, the Basel II Capital Adequacy Directive requires that all banks either subscribe to the model for calculating capital reserves laid out in the Accord or implement their own risk assessment models, something the majority of banks are keen to develop.
However, in order to prove to regulators that these independent models are calculated based upon accurate data, many banks have had to increase investment in documented processes. This has primarily been to clearly show how data is managed across the entire organisation so that the robustness of the model used and its reporting accuracy can be quickly and precisely analysed.
In the retail sector, there is constant pressure to find ways to improve costs and operational efficiencies within the supply chain. A recent report from GS1, a global group dedicated to the development and implementation of global data standards and solutions for the supply chain, found that poor quality data will cost UK grocery retailers and suppliers in excess of £700 million over the next five years.
Vast amounts of data are shared between retailers and suppliers to organise the ordering and movement of stock, which lead to serious problems if the data isn’t correct. The inescapable truth is that unreliable supply chain data leads to losses in efficiency and erodes company profits.
These challenges can be overcome by is the establishing data quality firewalls that check information loaded into stock management and enterprise resource planning (ERP) systems at the point of entry and prior to migration from legacy systems.
Problems can also occur in automatic data exchange where data quality checks must play a role. In short, the whole industry is reliant on clean, accurate and reliable data that is accessible anywhere, at any time.
Organisations are now embracing data governance as a way to bring order to the chaos of their IT infrastructure. Data governance combines the people, processes and technology to create a consistent enterprise view of an organisation’s data. By concentrating on the health of the data, organisations can address the lifeblood of their enterprise, helping create better data to support any business initiative.
Whilst not new, there is growing interest in data governance as a concept. Many organisations are finding success as they establish data governance policies and teams. A recent research survey commissioned by my company amongst a senior IT audience in the financial services sector was designed to flesh out a number of important issues around data governance while benchmarking the sector’s progress. The results were encouraging but highlight that there is still more work to be done.
Top-level findings include:
- 86% of responding organisations view data as an extremely important strategic asset
- A third of organisations have already begun to implement a data governance program and a further third are either in the process of implementing or are considering a project
- The majority of organisations are equipped to process customer data against criminal lists, however a significant minority (11%) report that no such process is in place or they are unaware of any such capability in their organisation (7%)
- 16% of organisations claim ‘no-one has specific responsibility’ for data quality or data governance
Through an established Data Governance Maturity Model, organisations can identify and quantify precisely where they are―and where they can go―to create an environment that delivers and sustains high-quality information. The technology associated with data governance typically encompasses the following capabilities:
- Data profiling: Inspect data for errors, inconsistencies, redundancies and incomplete information
- Data quality: Correct, standardise and verify data
- Data integration: Match, merge or link data from a variety of disparate sources
- Data enrichment: Enhance data using information from internal and external data sources
- Data monitoring: Check and control data integrity over time
With these applications in place, companies have the ability to improve data throughout its lifecycle and monitor key data metrics to ensure the data asset is maintained continuously. We recently worked on a project with a leading UK financial organisation. The project has enabled the bank to assure the quality of data it uses to produce its reports to the FSA.
Data quality processes are applied in real time to guarantee the ongoing maintenance of the data according to policies determined by the data governance steering committee. The project enabled assurance around the Basel II Capital Adequacy Directive and a host of operational benefits.
Another area of risk mitigation the project addressed was improved visibility of multi-party house loans, where a trusted, single view into over 50,000 loan customers was established. This allowed the bank to identify interconnections between loans and homeowners and make more intelligent lending decisions.
In addition, data profiling technology was utilised prior to migrations enabling the bank, thus helping to identify potential issues with legacy data before it was moved into a new system. This improvement saves the bank over £50,000 per migration.
The path to data governance is not easy, but it is achievable. More importantly, it is a necessary response to the numerous business challenges that require high-quality and trusted data throughout the enterprise. By understanding data governance, organisations can progress towards the ultimate goal―a single, unified view of the enterprise.
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