Overcoming the challenges of data governanceBy Stuart Chambers, Data Practice Lead on
Successful data governance is about linking data to business objectives in a way that is not a burden or a cost to the organisation. But there are significant challenges that need to be met for this to happen.
The second of two blogs looking at data governance based on a webinar presented recently by Stuart Chambers of Oxygen and Jonathan Adams of DATUM.
Everyone is doing data governance differently
Research tells us that currently one in three business executives do not trust the data upon which they base their decisions, and 63% of companies lack central control of data quality and governance. Over 50% of major organisations have individual departments adopting their own data quality and governance procedures. In short, everyone is doing data governance differently.
One issue that trips up many enterprises is neglecting data governance when a new IT system is implemented. There is often a significant dip in data trust once a new system is installed, which causes a period of lost ROI. It is at this stage that data governance suddenly becomes important, and when ownership and stewardship tools are typically assigned and implemented.
Data stewardship tools for better data trust
What our Information Value Management (IVM) solution by DATUM aims to do is prepare organisations earlier in the implementation cycle, minimising the period of risk post implementation, and accelerating information trustworthiness and value creation.
Consistent data is the key to enterprise information. Vital to achieving it is understanding what different types of data look like, how they interlink and how they can be organised into a logical hierarchy based on perceived value. The real problems in data governance exist at the bottom of the ladder – in the area of structural and reference data. That is where inconsistency is rife, but it is also difficult and costly to fix.
Building a data governance framework
A working definition of data governance is fundamental to building a framework that the whole organisation can embrace. IVM breaks data governance into three aspects:
- A body of business execution knowledge (policy/metadata) to rationalise business understanding of prioritised data
- Processes for data coordination
- Organisation for accountability
Once this structure is in place, the enterprise then needs to assess its value drivers: what are the things that it values most?
Most organisations’ value drivers fall into three categories:
- Analysis and Insights: Top line insights that might change the business model and significantly impact revenue or types of customers serviced
- Operational Excellence: Improvement at the margins. Answering questions about how an organisation can become better by optimising data flows around the organisation and improving existing processes
- Reporting and Compliance: Audit survivability. Ensuring an organisation can easily show a regulator what it is doing and why it is doing it, thereby reducing any compliance risk. This is a challenging process, particularly as the breadth of data inputs enterprises make use of are expanding rapidly
Driving business value with data governance
What is valuable will differ by company but understanding what factors are driving the value and being able to articulate what they are – such as strategic sourcing, product traceability, enhanced customer satisfaction – are important for the organisation to define.
It is also important to understand exactly where an organisation sits in terms of its data governance maturity prior to starting its journey. Is it still in the manual firefighting phase, using spreadsheets and manual processes – what IVM calls the ad hoc data activism level? Or, is it more sophisticated with data quality monitoring and data cleansing processes that enable it to be more proactive and execute data governance in a way that feeds into the business process to drive value.
Where does your organisation sit on the data maturity scale?
IVM has a proprietary matrix that helps organisations discover exactly where they sit on the maturity scale by comparing the three different components of governance (business execution knowledge, process and organisation) with three key governance objectives – order, efficiency and control. Establishing exactly where the organisation sits is critical to the next stage of developing your data governance strategy.
Getting started with data governance
To find out more about how Oxygen and DATUM approach solving these data governance issues and to learn more about its Information Value Management solution, you can access our on demand 'Getting started with data governance' webinar.
If you have further questions, please don’t hesitate to email me at firstname.lastname@example.org for more information.