DG 4 FSI — Why Data Governance Matters
DG 4 FSI — Although clear for some, will most likely look cryptical and incomprehensible for other. To understand what it means we need context, knowledge and, in this case, a degree of fluence in the English language.
The same can also occur in an organization, where a same piece of information can be interpreted in multiple ways, or not at all, where distinct business areas like risk, operations, trading or compliance view the same information differently, leading to substantial disputes about data quality, definitions, information storage, and control — This emphasises the importance of data governance — A way to assure that data in the organization is clear, reliable, and available wherever and whenever is needed.
The Financial Services Industry Case
The Financial Services Industry is a good example of the need for a robust data governance framework, taking responsibility to establish standards of conformity, integrity and reliability thereby increasing efficiency and throughput.
In the last decade regulatory requirements and industry standards in financial services increased significantly, posing overwhelming challenges on regulatory compliance and risk, customer relationship, profitability, and performance — All of them highly dependent on data, and demanding a global, organization level approach.
To comply with such comprehensive regulatory changes, governance will play a major role. In this context quality data provided through effective data governance and data quality processes is essential to achieve effective compliance reporting, ensuring accurate reporting and improving business decisions dependent on quality data.
As in other industries, the financial services are not immune to data quality, from false mortgage applications to incorrect credit ratings and balance sheets the list of data related problems is vast, adding to this bad data impairs the capability to make and execute decisions.
No decision is better than the data it relies on.
Looking at this scenario it is unquestionable that bad data directly increases costs and reduces revenue, and unless this is addressed proactively this is impacting your organization this very moment.
There are, however, some straight actions that can be taken immediately to push your organization to move in the right direction.
The awareness of data as the most critical asset for an organization keeps growing but the results will not follow and despite large investments to manage this crescent entry of data, most organizations are still unable to retrieve the meaningful insights that will enable them to take advantage of the potential created by all this data.
Create an environment where the importance of data is recognized across all the organization and where the existence of data quality problems is accepted, and handling data as an asset is a priority.
Identify business drivers to give a boost to data initiatives. It is essential that the connection between data and its impacts in business are always clear.
Bad data will impact business in many ways, either affecting the management confidence in the organizations data, resulting in missed opportunities by losing the capability to derive insights that can lead to competitive advantage, leading to lost revenue in many ways, resulting in reputational costs or undermining efforts to improve customer experience.
An especially important business driver for data initiatives in the financial services sector is regulatory compliance.
With a regulatory framework that keeps growing, it customary for financial services companies to demand longer timeframes to prepare for each new directive, seeming incredible how data-driven organizations struggle to supply accurate data.
This is true for many financial institutions of every size, trying to manage internal and external requirements for data, maintaining a silo-based infrastructure.
Often regarded as a necessary evil, data initiatives related with compliance are approached as a series of isolated initiatives, a tactical perspective, to satisfy the minimum requirements to comply to a specific directive.
Compliance should be an opportunity to establish a data governance framework that will allow the organization to comply and accelerate the deliverables for new compliance directives.
Data Governance Framework
The definition and implementation of a data governance framework with a clear roadmap of initiatives is a critical step, allowing the organization to move from a tactical to a strategic approach to data governance.
An effective data governance program must start bottom-up, its success depends on finding the efficient combination of people, process, and technology.
There are seven critical points that need to be addressed and that are essential for success:
- 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.
· Empowerment for the data governance team — Data governance will affect every area of the organization, often it will affect balances of power within those areas, added to the introduction of a new element, a data governance team.
- 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.
Data governance, as any process that is introduced into an organization it will create some disruption of the status quo, it will generate resistance to any change, a success approach to data must be able to overcome these and the challenges mentioned above.
- 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.