Jul 2015 | Data Quality | Data Management
By Posted by Janani Dumbleton

Data ownership is now an increasingly hot topic among those in charge of data quality within the business community. There is a growing awareness of the need for a centralised strategy which sits alongside individual accountability and this is driving structural changes, with many businesses warming to the concept of Chief Data Officers (CDO) as board-level guardians of data within an organisation.

However, “ownership” can be a confusing and misleading concept if poorly understood. Many businesses mistake the allocation of responsibility with handing over complete control, which can hinder the open sharing and access to data upon which many companies rely if they are to reap the strategic benefits that it brings.

Clearly, this is not the right approach, as ultimately data touches most people within an organisation and impacts their ability to do their job successfully, whether directly or indirectly. This is why responsibility for data quality should be a shared, collaborative process, even in cases where overall ownership is delegated.

As such, the right approach to a data quality strategy needs to involve the careful allocation of responsibilities, with everyone in the organisation understanding the ways in which their roles overlap with those of others as part of a wider strategy.

How do different data quality roles interact?

Within a typical organisational structure, there are multiple business processes, people and technologies that interact with data, meaning it is used and processed in a number of ways at various stages.

Call centres, for example, may initially capture and create the data, which is then consumed and updated by the fulfilment and management teams. Marketing departments also consume the data, but are also likely to be involved in enriching it, whereas the IT department is responsible for moving and archiving it.

All of these processes differ in their approach and implications, resulting in a complex data journey. A failure to understand this leads to a simplistic approach to data ownership that creates inefficiencies, delays and mistakes.

How to define responsibilities and ownership

Once the various interactions business users have with data have been accurately mapped, businesses should seek to identify the responsibilities individuals and departments have towards these interactions in order to identify the right people to manage the data strategy for the organisation.

Using a tried and tested industry methodology such as a responsibility assignment matrix (RACI matrix), these roles and responsibilities can be clarified in a cross-functional / departmental data landscape.

Some can be classified as ‘responsible’ for data, meaning they are in charge of capturing the data element, whereas others are ‘accountable’, meaning they will take the final decision on its usage and may assume overall ownership.

Other categories include ‘consulted’ – the individuals that need to be asked before a decision or action is taken around creation of the data element – and ‘informed’, which means they need to be notified once the action is complete.

Applying this principle to all of the company’s data will allow the business to view trends in responsibility and accountability, allowing potential owners and responsible parties to be recognised organically and without conflict.

It is also important that there is an understanding of the data value chain, the journey and transformation data takes as it moves through the organisation. This will help clarify the various touch points with data consumers and producers that feed into such a matrix of roles and responsibilities.

Defining the organisational structure

Taking the above steps will make it much easier for a business to define a coherent organisational structure that ensures strategies are executed successfully. This will often involve the creation of a data governance council, consisting of business and technical stakeholders selected through the analysis of data responsibility trends.

At the top level, an executive layer would offer overall leadership and approval, with input from the CDO or the relevant board level member responsible for data, often the CIO. They would own the strategy but not the data itself, whereas a Data Governance Manager operating below would own the processes and day to day management. Meanwhile, the business users and stewards on the governance council would own the data in their individual business units and departments and take responsibility for their team’s successful execution.

With data becoming a strategic priority for organisations it’s no surprise that CDOs are growing in numbers as it becomes clear that acting as the guardian of data at board level is a role in its own right. With a sole focus, CDOs are able to ‘make it happen’ by creating a strategy which includes defining ownership and responsibilities and putting the necessary processes and technology in place to meet the demands of business users.

Being clear on roles and responsibilities to data means companies can ensure they reduce conflict around ownership, gain organisation-wide buy-in for data governance and ensure the most appropriate people are involved in decisions around data. In an age where data drives most businesses in some way or other, having a true understanding of your own direct relationship with it and how it fits into the wider picture is an integral part of establishing a data driven culture which delivers value.