Move over Data Science it's now time for Data Governance to have the spotlight.
While Data Science is no doubt important and offers a lot of value, you need to ensure that your data is built on a solid foundation or it will be “garbage in, garbage out”
Over the last year, there has been a massive increase in the demand for Data Governance specialists in both the Financial Services and Consulting space. It's great to see this somewhat niche space grow and get the respect it deserves however, there still seems to be confusion as to what data governance means?
A few years ago, I was fortunate enough to interview Ram Kumar who was previously the Director of Enterprise Information Management at IAG regarding Data Governance and its impact. His words are as true today as they were then.
I notice in the industry that there seems to be a lot of confusion about data governance and what it entails. In your opinion what is data governance?
Data is a core strategic asset of any organisation and should be governed like any other asset. Therefore, Data governance is critical to the management of the lifecycle of data in terms of collection, categorisation, storage, use and retention/destruction. The lifecycle also includes privacy, security, data quality, master data and Metadata management. It is important for an organisation to look at the lifecycle of data from a lifetime value mindset similar to customer lifetime value rather than in bits and pieces.
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Why do you think there is such confusion in the industry when it comes to data governance?
The number one reason is that traditionally the majority of the organisations never saw data as a strategic asset and as a result, managing the data was seen as I.T’s role. I.T is just a gatekeeper/custodian of data and it does not own data.
The latest phrase that is being used is “Data is the new oil”. Well, data has always been the oil of an organisation except that organisations never realised it as data was never considered as a strategic asset. But now with this whole new movement of a data driven world (thanks to Big Data), organisations have now started to realise that data governance is critical to being data driven and extracting value out of data. But organisations are confused about how to go about doing this so called “Data Governance” given that they have never done this before in terms of managing the internal data assets let alone external big data assets.
This has resulted in the creation of Chief Data Officer (CDO) role in an organisation where the CDO’s responsibility is to govern the lifecycle of data to help drive data driven business outcomes by working other CxOs such as Chief Analytics Officer, Chief Marketing Officer, Chief Digital Officer and CIO. Many ask the question of why we need a CDO when we have a CIO. Well, the CIOs were busy doing I.T related tasks only and never focussed on the “I” which is “Information” in their title. “I” does not mean Information Technology. It is information management and governance of the information asset which is the “crown jewel” of an organisation. So, I would call a CIOs role as “Chief Information Technology Operations Officer”.
Applying traditional data governance practices will be a challenge in this fast paced, high volume and volatile data environment as it could curb speed to market with new innovative products. How do you get the right balance in terms of innovation and speed to market and controls through data governance is now a hot and interesting topic.
How have you found it getting support from the top?
Where there is a will there is not just a way, but many ways. I have been very successful in educating executive’s right at the very top regarding the importance of data, the value it brings to the organisation if it is treated with the same respect as other assets and why it needs to be governed.
One thing I realised over the years very quickly is that it is not that an organisation does not want to deal with data governance, it is more of a lack of awareness and understanding than anything else. Therefore, educating the organisation on this is extremely crucial and this requires lots of patience and perseverance. In one of the organisations that I worked for, I created a true data driven culture through raising awareness.
This resulted in the CEO as the Information management champion of the organisation. Information management was a key measure in the balanced scorecard of the organisation which then because of a KPI in the balanced scorecard of all employees of the organisation. This work is now regarded as a global best practice case study for information governance.
So how do you improve data quality?
That's a good question. Data Quality is not an IT problem, it is a business problem.
The quality of data determines business outcomes, for example, pricing products in the insurance domain. Data Quality cannot be seen as a one off exercise or an exercise for a specific project. Unfortunately, the majority of organisations have this project mindset when it comes to data quality.
More than just cleaning up poor data, it is important that controls are implemented to protect, monitor and manage the quality of cleaned up data as, without controls, the quality of the cleaned up data will degrade as soon as the project is completed. Continuous improvement to the quality of data should be a discipline in organisations. It is important that every attempt should be made to fix the data quality issue occurring at the source when the data is captured. However, at the same time, it is important to consider cost, business benefits, priorities, operational impacts, etc to make an informed decision on a case by case basis.
How do you structure Data Quality Management within a Data Governance framework? What does a typical roadmap look like?
Data quality management is one of the key pillars that sit across the data governance framework. It is a key component of data management lifecycle. Data Quality by design thinking should be embedded as part of data culture in an organisation. This means planning for data quality at the planning and conceptualisation stage of any initiative that involves data consumption rather than being an afterthought that would result in data cleansing activities. A typical roadmap would include skills, capabilities, tools, processes, monitoring and measurements/metrics. It is important that a dedicated team looks after data quality by working closely with business and IT.
Does this mean you need a culture change?
Data governance is not a silver bullet. To me, data governance does not get addressed by just fixing the processes and technology. It is about changing the mind-set of people to understand that data is a strategic corporate asset and that it has to be treated and governed like any other strategic asset. This is the hard bit. Technology and processes are the easy bits if you can overcome these people issues.
It's one thing getting people to embrace this at the C level but what about the people at the lower lever? What challenges do you think they will have?
To me, data governance can only be effective if it is driven both top down and bottom up. Importantly, getting the C-level executives to recognise that data is a strategic asset and take accountability to govern the asset is fundamental to driving cultural change. Data governance embraced bottom is hard to succeed and this is evident from industry studies.
Any operational issues around data that people at the lower level face is always challenging to get senior level people’s attention when they do not understand the value of data as a strategic asset as they often believe that it is an IT problem and not a business problem. Traditionally in organisations, in any project of any size, the last thing people worry about or even think about is data management or even if some funding is there for data management, in any budget cut situation, data management is the area that gets cut out of the budget.
Appropriate training programs should be implemented to raise the awareness of the importance of lifecycle of data management to all staff in an organisation. In one of our business divisions, we have included rewards program for staff capturing quality data which has been a huge success.
What changes can we expect in the future?
Data is data irrespective of its type, size or velocity.
So, the size of data does not really matter……today big data maybe tomorrow it is massive data. But the principles of data management do not change. What will change are agile and better data management principles.
Traditional data management practices will not suit the current industry trends. It is good to see that data governance is now on the agenda of every organisation using data for analytics (thanks to Big Data) which to me should have been the case in the past. My view is that the success of an organisation becoming truly data driven will depend on the organisation’s appetite to treat data as a strategic asset and managing the asset through an effective and efficient data governance program at the organisation level. Data privacy is a subject that will be discussed a lot in the future.
As organisations are streaming ahead to provide one to one personalised customer experiences through advanced analytics by combining different sources of data about a customer, the issue around privacy and ethics will become an important discussion point. Data management and effective use of data will also determine an organisation’s success in implementing an effective digital transformation particularly when providing digital experiences to customers.