Why Data Governance Matters

There is no doubt that most organisations, large and small, have realized the power of using affordable data analytics tools in an environment where most business activity is recorded digitally. One characteristic that is often overlooked or not discovered soon enough is the reliability or otherwise of the data. Many realise that good data governance is a prerequisite for exploiting the opportunities in data; whether that is more insightful management information, driving operational decision making or capitalising on your relationships with suppliers and customers. It all boils down to having reliable data.

Why is there a difference between the sales in our management accounts and the sales report by region and customer type? Can you explain why the total number of active suppliers in our suppliers’ directory and creditors listing is different? How did we end up with wrong contact details for over 20% of our clients? Why is our data warehouse not up to date?

Any of these questions sound familiar on a Monday morning? Given the recent explosion of data and the use and reliance of such data, organisations need to have the right setup to ensure the correct data quality. You are not alone in this journey. One of the conclusions of a recent PwC survey around Data governance in European banks was that banks are failing to address Data Governance in a structured way, with a lack of an overall Data Governance Framework across surveyed banks. This leads to redundant costs and inefficiencies. The survey also concluded that banks generally invest a lot of effort in the reconciliation of data, perform a lot of manual data collection and data granularity is often not appropriate. Although most of the banks surveyed have implemented a central Data Warehouse they are suffering from poor data quality. These findings reflect the reality in most industries. Many organisations struggle to build the necessary data governance capability that will provide the necessary level of trust in the data.

Data is often collected for a specific purpose and when organisations look at reusing data, they tend to focus and place responsibilities to address the security aspects of data. The question as to who is responsible for the quality of the data is often not answered. Should it be the finance team; marketing; operations; IT? This article will not solve your data related problems and issues but it should point you in the right direction to start taking the first steps in your journey to improve data quality in your organization.

A good data governance approach needs to recognise the context in which it operates. The way the business is run, its chains of command, what data it has, its commercial and regulatory priorities, are all critical to the design and to building trust. Many organisations struggle to find the right place to start or only begin thinking about data governance when a critical issue occurs. The key here is to set priorities for data in terms of what the organisation, across the various functions and units, wants to achieve, start with those and build out from there. Priorities change from time to time and your data priorities should reflect this. For example, locally, a number of organisations recently undertook a number data governance related initiatives focusing on the impact of GDPR. It is important to establish clear objectives that will drive the design and operation of your data governance capability.

Everybody in the organisation has a responsibility for data. However, good data governance has some essential features:

  1. Clear sponsorship from the leadership and with this, in larger organisations, we are seeing an emergence of the role of the chief data officer;
  2. Ownership and accountability for data which reflects the responsibilities for process;
  3. Integration of activity with related disciplines of security and
  4. Records management and information privacy.

Successful data management involves taking a holistic approach to risk, controls and assurance. You cannot effectively manage every data point, so a risk-based approach will help you focus on the data that matters most to your business. This approach, when combined with a ‘lines of defence’ assurance framework will help you manage and sustain the quality of your data.

Assurance, encompassing self-assurance (1st Line), management compliance review (2nd Line) and internal audit (3rd Line), is needed to give you confidence that your data controls are appropriately designed, embedded and effectively operating.

Common and well understood principles are at the heart of a successful data governance framework. These should be well articulated and aligned to the values and needs of the business. A robust governance structure will also consider all lines of defence:

  • The business as Information Owners and Stewards;
  • The Data Governance Centre of Excellence managing policy
  • Internal or external audit, assessing overall effectiveness

Wherever possible, data governance responsibilities should be embedded within existing structures rather than building new ones.

The Data Directory is one of the cornerstones of effective Data Governance. It describes the following in clear business terms:

  • The uses of data – in this case the uses of the data in the data book
  • The quality requirement of the data based on those uses – this is typically defined in terms of completeness, accuracy and appropriateness
  • The sources of data and how these are mapped to the uses – this allows the generation of the data residency matrix and of data flows
  • The controls and metrics that are in place that support the achievement of the required data quality
  • The monitoring that is required of these controls and metrics to inform the Data Deficiency process

The scope and “rules” for populating the Data Directory form part of the Policy for Data Quality. A Data Deficiency Log can be used to manage potential issues with data quality or controls that are identified through the monitoring process.

Cultural and behavioural changes are also crucial to improving both the underlying quality of data and pro-actively managing data issues. Where required, these expectations should be agreed, formalised and monitored as part of a formal individual performance management process. Additional skills and resources will be required to design, operate and monitor data governance which can be developed through training or recruitment.

The success of any data quality project depends on the adoption of a data governance model across the organisation. The implementation of such model typically follows a multi-year, multi-phase approach; therefore, it is essential to factor time and resources upfront for establishing a governance model. That model should to be scalable, flexible, and adaptable to the different needs of the organisation. The objective is to change how the organisation manages information, so it can be effectively used to help to achieve business goals such as, driving down business costs, improving competitive position, or meeting risk and compliance objectives. The approach to achieve data quality is not different to achieving quality in other parts of an organization. Defining clear targets, monitoring, measuring and providing a feedback mechanism for continuous improvement.

If you want your organisation to excel further in the digital age, data governance matters.

References:

https://www.pwc.fr/fr/assets/files/pdf/2016/05/pwc_a4_data_governance_results.pdf

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