Data Governance and Quality: Why a ‘Big Bang’ Approach Will Not Work

 

Data governance and data quality are at the forefront of the Chief Data Officer’s mind. Poor data quality is one of those perpetual issues that can cause tremendous business pain. The symptoms are common: complaining of bad decisions made based on missing information, costs of duplicate information, penalties for not meeting compliance obligations, lost supplier volume discounts costing the company millions. Without effective Data Governance, you can kiss goodbye to any impactful insights you may look to generate.

We caught up with Ram Kumar, Director, Enterprise Information Management and Privacy, Office of the Chief Analytics Officer – IAG to discuss what he views as the most important elements involved in establishing effective data governance and why a ‘big bang’ approach will not work.

 

CDO Forum: What do you consider to be the key building blocks to establishing an effective data governance framework?

Ram Kumar: A Data Governance Framework should be viewed as a business tool for managing the end-to-end lifecycle of an organisation’s lifeblood and its key strategic asset – data. The key building blocks of the framework are: culture and awareness (the most challenging of all), data quality, master data, reference data, metadata, data privacy and ethics, data security, data architecture, data modelling, data accountability – roles and responsibilities, data value metrics, data classification, data principles, standards and policies, data retention and destruction, data risk management, data exploitation and monetisation – internal and external, data analytics and intelligence, and supporting systems, tools and processes.

 

CDO Forum: Data Governance is accepted as necessary within organisations but sometimes, it is seen as ‘boring’. How do you keep it relevant for your peers and ensure that people remain engaged and understanding of data governance?

Ram Kumar: The word ‘governance’ is often seen as painful, bureaucratic and boring. The purpose of data governance is to help manage the strategic asset and exploit it in a manner that generates appreciable value to the organisation and to its customers, partners, employees and shareholders. This is where it is critical to have metrics in place to measure and demonstrate the value of data (through data governance) to the business. This is a cultural challenge and it can neither be addressed overnight nor can a ‘big bang’ approach work.

Quick wins through agile approaches (flexibility and adaptability in governance process) to demonstrate direct benefit and value to the business in a timely manner is critical to keeping people engaged with data governance. If data governance has to succeed, it should not be seen as a barrier to business initiatives, rather, an enabler by helping make informed decisions to manage key risks in a timely manner. The efficiency of the governance process is critical to this.

 

“If data governance has to succeed, it should not be seen as a barrier to business initiatives, rather, an enabler.”

 

CDO Forum: What do you consider to be the main obstacle to improving Data Quality within an organisation, and how have you set about overcoming that?

Ram Kumar: Data Quality is still seen as an IT problem rather than a business problem in most organisations, which is fundamentally a cultural issue – lack of appreciation that data is a strategic asset. Organisations continue to build processes and systems without thinking about data quality in the requirements/design stage which I call “Data Quality by Design” culture.  There is no point spending huge amount of effort and money fixing data quality half way through a business process or at the end of a business process as the originality of data is lost and importantly, the source of dirty data continues to pump dirt. Businesses should understand that no matter how smart their business processes are, how skilful and efficient their people are and how state-of-the-art their technology is, if the data that touches people, process and technology is poor in terms of its quality and integrity, the outcome will be poor. Data quality cannot be a project or a program, it should be thought of as an ongoing investment into creating value from the strategic asset.  Monitoring and measuring the quality of data and providing a financial metrics to it that is understandable by the business is critical to drive data quality culture.

 

CDO Forum: What do you believe to be the most common form of ‘bad data’ and what effect can that have in an organization?

Ram Kumar: Data that is misleading by not being accurate/reliable/trustworthy is the most common form of ‘bad data’. The impact to the organisation due to bad data could be significant materially. Impacts could take many forms, including poor customer engagement and experience resulting in customer loss, legal and regulatory compliance issues, reputational damage, poor decisions driven by poor analytical and predictive models, inaccurate/poor product pricing (e.g. underwriting risk) and high operational costs.

 

CDO Forum: How important are people as an obstacle and/or enabler for data governance/quality projects?

Ram Kumar: When I think about information management challenges, the number one cause is people. Technology, processes and tools are the easy parts as there are plenty of options available and they just do what people tell them to do as people design them. It is therefore important to drive the data/information management culture in order for an organisation to generate appreciable value with its data. This culture should be driven from the top by taking accountability (rather than just sponsorship) of the outcome. A bottom up approach to drive cultural change will not guarantee success. Cultural change requires both a top down and bottom up approach. Data governance and data quality should not be seen as projects. These programs have no end dates as they manage the “strategic asset (Data)” of an organisation like any other asset. People should be rewarded and recognised for managing data efficiently and effectively if an organisation truly wants to drive data driven culture.

 

“Data governance and data quality should not be seen as projects. These programs have no end dates as they manage the “strategic asset (Data) of an organisation.”

 

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Ram Kumar is on the judging panel awarding IAIDQ’s IQ Excellence Award. The Information Quality Excellence Award aims to recognise teams and organisations that have undertaken information quality projects or programs with demonstrable successful outcomes. The winner will be expected to make a presentation based on their award submission at the CDO Forum in Sydney Australia, 9-10 February 2016. Award details at: http://iaidq.org/iqexcellence  

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