February 20, 2020

Defining Success as a key to Data Governance platform implementation

Shane Lakhani

Gartner defines information governance as the specification of decision rights and an accountability framework to ensure appropriate behaviour in the valuation, creation, storage, use, archiving and deletion of information. It includes the processes, roles and policies, standards and metrics that ensure the effective and efficient use of information in enabling an organisation to achieve its goals. It is, therefore, too broad a subject to be siloed into any one solution because it covers a multitude of business areas that, as financial services practitioners, we must all be aware of. 

And while there are tools to help us navigate the Data Governance ecosystem there is no ‘one size fits all’ blueprint for implementing a Data Catalog & Governance Tool such as Collibra, Informatica, or InfoSphere. There are however critical considerations, prerequisites and lessons identified that, if leveraged correctly, would mitigate risks and pave the way to a successful implementation which delivers lasting value to your organisation.

 

Business use case & value definition

In order to maximise value from a Data Catalog implementation, it is important to define the use cases and business value goals specific to your organisation. A strategy that clearly identifies and disseminates this will significantly increase the likelihood of adoption.

This validation process is an essential and necessary first step to ensuring compliance and process are smoothly intertwined. This first step ensures that strategy is aligned to goal and Governance is not done just ‘for compliance’ purposes but is a clear value driver for the business. 

 

Technical considerations

Technical decisions can also impact adoption. These span the metamodel and relationships, ingestion patterns, enterprise-wide integration, security and access, navigation and UI, data quality control and reporting. For example, integrating with your active directory and Single-Sign-On (SSO) will eliminate additional manual overhead to manage roles and permissions. Given the tool is for the business to use, simplification of the metamodel and user interface will increase business usage and minimise the risk of post-implementation rework. If the metamodel design is not fit for purpose to start with, it will be extremely painful to overcome in the future.

 

Pitfalls and successes

The road to successful Data Governance implementation can be a long one, albeit with many wins along the route to success. These tangible success stories are key in winning out the Data Governance argument within your organisation and using this as a platform to drive data-driven decision making in your organisation. 

There are pitfalls and successes to watch out for as you implement your Data Governance platform and widen the scope of Data Governance within the business.

 

What does success look like? 

 

Focus on the operating model 

Your business operating model should be the foundation for key design decisions. It is important to define a governance structure and data role holders that aligns to your operating model. Transparent roles and responsibilities linked to lines of business will enable business and technical stewards to perform their job.

 

Best practice training & education from day 1

To achieve a data-driven culture that maximises your investment, this activity must go further than just technical training. Data management policies and standards must be delivered to, and understood by, your organisation; ensuring both the business case and best practice guidance is understood, accepted by and applicable to all business areas and data role holders.

 

Always prioritise data quality management

Data quality can be a starting point for good things in a financial services business function or it can be the source of many problems. By prioritising data quality management wins, the wider business can clearly see value in what you are trying to implement across your Data Governance roll out.

Organisations should execute data quality assessments and improvement prior to ingesting data into your metadata repository and subsequently educate business users with best practice guidance and technical training before providing write access. These technical considerations will ensure data quality issues are kept at a minimum.

So too does implementing data entry validation rules, controls and governance to prevent or mitigate poor data quality while simultaneously embedding quality assurance processes with defined quality criteria to detect, correct and report on data quality issues. Data Quality should be a constant consideration to ensure a smooth running of your chosen Data Governance platform. It is also wise to integrate with other data management platforms such as data quality engines and visualisation tools to ensure data consistency and leverage capability.

 

Pitfalls to watch out for

 

Overlooking the original value driver and end-users

User experience is a key driver for technology adoption and sustained usage. The business are the end-users of the solution and design decisions should be based on how the business will be interacting with the user interface. We recommend keeping functionality such as workflow navigation, governance roles and language as simple and intuitive as possible.

 

Not maximising out of the box functionality

While some customisation for business processes will be required, out of the box functionality is highly configurable and business user-friendly. Specialising technology too early can result in having to reverse years of effort and cost.

 

Starting too large and not learning through a POC 

Demonstrating value through building a minimum viable product not only enables lessons to be identified and factored into implementation but also starts to create a governance culture to support embedding.

 

Data Governance is wide-ranging 

While these are clearly some of the pitfalls and successes of Data Governance platform implementation there are others and success or not, can only be attributed to the individual business. 

It must be remembered that Data Governance is wide-ranging and requirements and complexity will differ. Such work that we have delivered includes regulation-specific Data Governance delivery (BCBS 239 Compliance for a Tier 1 bank) as well as a wider Data Governance Policy and Framework for an Asset Management fund including Records and Management policy, stakeholder training, standards and procedures implementation and more.

Success can only be attributed to the requirements of the business in question and so it is best to approach the subject with clear objectives but never underestimating the scope of delivery that may be involved. 

 

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