Although data governance often does not make it onto the board’s primary agenda, it should do.
Data related mistakes, missed opportunities, break-downs and leakages cause significant, recurring and – often – hidden costs. These costs are not only financial, they also include erosion of reputation, customer dissatisfaction and wasted effort.
Key reasons why data governance fails:
- Lack of clarity of purpose – when it is not clear why data are being collected and what their definition and purpose is, it is almost impossible to design aligned systems, reporting, processes and governance around them, especially in a global organisation. This causes misunderstandings, delays, duplication, mushrooming reports and omissions.
- Small picture thinking – the bigger picture – the organisation wide, cross-functional connection between data – is often not taken into account, which causes errors and breakdowns. Often this is due to a lack of end-to-end process expertise and awareness with regard to data. Roles of data stewards are also not assigned cross-functionally.
- Culture not taken into account – people make or break data governance. If people do not understand or follow the rules or support the organisation’s desired values around data, no data governance arrangements will be successful and deliver the desired results. Any data governance operating model must be designed such that it is supported and enhanced by the organisation’s culture or culture change has to go hand in hand with the data governance implementation.
- Absence of consistent focus – data fires are fought, and then the focus shifts to something more ‘exciting’. However, the trick to data governance to make it consistent and habitual. When it’s not embedded and automated where it has the biggest impact, data governance is usually an uphill battle.
- Inflexible and cumbersome governance arrangements – data governance arrangements are overly bureaucratic, unrealistic, or too far away from the real business and value creating processes. Governance processes be they to agree or to follow data rules can be manual, out of date and time-consuming
Key elements of successful data governance are:
- When data governance works, it not only removes these hidden costs from bottom line, it also contributes to increased productivity, less organisational fire-fighting, growing intellectual capital and innovation.
- Absolute clarity of purpose for crucial data objects and information clusters
- Cross-functional data governance mandate, roles and processes
- Embedded in the organisation – in processes, systems, culture and value system
- Consistent and automated focus, which is measured and reported on periodically
- Data governance proactively and transparently contributes to the organisations vision and value creation processes
Data Governance Maturity Health Check
Our data governance maturity health check provides you with a snap shot of where you are right now, and what is working and not working for you. It identifies key strengths, weaknesses and risks, and provides you with practical recommendations to significantly improve your data governance and raise your maturity levels.
Data Governance Design & Delivery
We help you develop your data & information vision, your data strategy and a sound benefits case. After a quick current state analysis, we assist you in designing a practical data governance operating model including organisation design, roles, competencies, capabilities, processes, and performance measures. We support the implementation and cultural change required to ensure your data vision delivers for you, and costly data related issues are sustainably resolved.
Data Management & Transformation
Aronagh collaborates with Quorum Consulting, a Digital Transformation & Business Systems Consultancy, for Data Management Services in Europe, and is therefore able to offer the full breadth of data management services.
To discuss your requirements or find out more, contact us here.
Data Governance Case Study: A global enzymes manufacturer
Novozymes recognised the need for global data governance to improve data quality and reduce data conflicts across functional silos. After Quorum’s initial review, Novozymes decided to build the foundations of sustainable., global data quality efficiency. Quorum helped Novozymes to set up the MDM project, carry out a deep-dive as-is analysis, design scalable data governance, and develop a Novozymes data management methodology underpinned by the end-to-end governance implementation of the Vendor data object as a pilot. [Read more]