Most data management specialists, myself included, would suggest that data management should be lead and owned by business.
Yet in many organisations, IT are the champions of data governance, data quality, master data management and similar data management initiatives.
After all, it is IT who are blamed when complex, data centric IT projects such as ERP or CRM implementation fail to deliver results. With poor data quality a significant contributor to these failures, it is not uncommon for IT specialists to realise the importance of data management long before business does.
We all understand the principles of maturity models. Ideally, data management principles should be ingrained in the corporate culture. An enterprise data governance organisation or data management centre of excellence should ensure that the enterprise maximises the productive use of information, eliminating duplication of effort and ensuring reuse. Enterprise data quality platforms will ensure data compliance to required standards – both by providing validation and correction at data entry points, and by measuring information’s compliance to company objectives.
Very few organisations have achieved this.
Ultimately, any data management team will combine both IT and business members. The “best practise” view, that these teams must be business-lead, is based on the reality that data management is about content in context – i.e. a value may be valid due to its unique business circumstances – and may require a specialist business knowledge to separate good data from bad.
If IT are the champions of data quality in your organisation then, quite frankly, I would encourage you to drive data management forward until business reaches the same level of maturity. However, do not make the mistake of assuming that this simply means making a technology investment.
Instead, I would suggest incorporating data management principles (such as data profiling) into tactical projects while building towards a reusable framework. Try to measure the impact that these practises have on your ability to deliver projects on time and on budget. For example, projects that have no data management focus may hit hurdles and delays in testing caused by the project team’s inability to accurately assess and manage data-related risks. In your projects, data issues should be identified early and planned for long before test phases are reached.
Also try to keep the end goal – an enterprise data management capability – in mind. By defining and implementing data standards, you will begin to see incremental benefits as each new project can leverage the work already done. Clearly, technology will add value, but try to ensure that technology investments are able to support the enterprise vision.
In this way, you can begin to measure the contribution that data management principles bring to the organisation, without overstepping your responsibilities.
The last step would be to communicate the success you are achieving to the broader community, including business. In this way, your tactical success will begin to get enterprise attention and, ultimately, may become the launching point for the enterprise initiative.