DG 4 FSI — Why Data Governance Matters

The Financial Services Industry Case

Awareness

Prioritization

Data Governance Framework

  • Leadership buy-in and commitment — Data governance is a process that needs buy-in from every level of an organization, and it starts with strong executive sponsorship but also from every other stakeholder in the organization, which need to be aligned and committed to the program.
  • Alignment with business goals and benefits — Data exists to serve the business — This means that any data governance process must be supported on a strong business case, their objectives need to be anchored on business objectives, otherwise it will be viewed as another siloed IT project with no perceived value from the business side.
  • Focus on strategic data — At an operational level, most of the organizations rely on dozens of different systems, which handle massive volumes of data of every kind of typology daily. Approaching data governance in a global perspective will inevitably lead to a lack of focus, resulting on a misalignment with the business objectives and incapability to deliver value. Again, being supported on a strong business case that identifies and prioritizes business critical and strategical data is paramount for success.
  • Cross organization involvement — As mentioned above, data governance is a process that needs buy-in from every level and area of an organization and failing to clearly transmit the objectives and benefits of data governance, while inevitably lead to a lack of commitment and involvement.
  • Business approach — Technology itself will not govern data, technology is but one of the components that supports a data governance program, and often proves useless if seen as an end in itself or not properly leveraged on the remaining components of this transformation process.
  • Time to deliver results — By nature, a data governance program implementation is expensive, time and resource consuming and span through long time frames, take time to deliver ROI. All these characteristics must be addressed when planning the operationalization.

Success Drivers

  • Data strategy is business strategy — Data’s purpose is to create value, so any data strategy must be oriented towards the organization’s strategic priorities and key business objectives.
  • Use Cases — From here it is possible to identify how data may be used to deliver those priorities and objectives. These will be the use cases for the data strategy. In an early stage, for effectiveness purposes, there should not be more than five use cases, all with clear, achievable objectives and stakeholders that are aware of the importance and impact of data.
  • Start small, think big — Always aligned with the data strategy start with a small, targeted initiative, where the impact and value of data can be clearly identified and working with a business stakeholder that can passionately and effectively articulate the impacts of data in their business processes and that will be eager to defend the project.
  • Measure and communicate — Setting up a set of metrics that can be linked to data governance and communicating them across the organization, a success story, that even at a small scale will create the awareness and act as a motor to leverage the replication of that story in other business units.
  • Business on the driver seat — All the program and initiatives must be driven and oriented by the business units. Data governance is not an IT function, it is a business function, it is the business who better knows what their problems and objectives are. The role of IT in this process is to find the right technology and support the business units in this journey.
  • Agile mindset — Apply an agile development mindset to all this process, start with a minimum viable solution and iterate, allow that visible results are presented in short time lapses.
  • Integrate — Data governance is only part of the process of managing the organization’s data assets, it must be integrated with other initiatives, as Master Data Management (MDM), data quality, data stewardship workflows, data catalog, business glossary and metadata management.

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Jose Almeida

Jose Almeida

Data Consulting and Advisory MEA - Driving better insights through better data (www.josealmeidadc.com)