The future of data governance in a data rich world | Deloitte UK has been saved
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Organisations today are beginning to realise the value of the data they hold and generate and are actively looking to exploit it, ensuing they are getting the most from their data. While there has been a lot of focus on external data to enhance business, there is still a very small proportion of organisations that come close to exploiting their internal data. There are a variety of approaches to this, with many organisations calling themselves “data-driven” and others “data-centric”. So, what are the key points to consider?
The difference between data-driven and data-centric decision-making organisations
Data-driven and data-centric are not synonyms even though they are used almost interchangeably. In a data-driven decision-making model the focus is around building tools, expertise, and culture that act on the data. While in a data-centric decision-making model, data is considered to be the primary and permanent asset and applications take the backseat in terms of importance. In fact, data-drivem and data-centric decision-making models are two very different architecture types and the choice of one versus the other depends on the type of organisation and its goals.
Data-driven organisations are actively using data to make better business decisions. For example, recommendation systems for their customers to increase engagement or improved store layouts to maximise sales. Therefore, the first step to becoming data-driven is establishing the business questions that need to be answered using data. Then the analytics team has to enable and validate those decisions. Finally, the development team enables the optimization of the decisions and defines the data selection, collection and sourcing. There is no single business data model.
In data-centric organisations data is the primary and permanent asset and applications come and go depending on how useful they are. The first step to becoming data-centric is creating a single business model. Then, the applications which are going to be used by the business are created. The final is step is having an analytics team establish what value will be created based on the analysis generated by a single data model.
Data-driven vs. data-centric
When an organisation wants to make the switch from being process or applications driven to data driven decision-making they have to move from a place where each process and each application defines their data structures, and where it is very difficult for any real data sharing to occur. Each application deals with similar but not identical data.
Even if organisations manage to go through all these technical difficulties, they are still faced with a huge obstacle in their data journey – culture. In most organisations people are used to getting things done in a certain way and it can be hard to see the value in making the changes required to achieve data-centricity. Or employees are too swamped with their day-jobs and data is not seen as a priority.
Figure 1: The Difference Between Data-Driven and Data-Centric
Next steps in data governance
The focus of traditional data governance has traditionally been on policies, procedures and roles. The data governance of tomorrow is not only about ensuing people are following the right procedures. It is a balancing act of using data for operational effectiveness and decision making, while acting in line with regulatory requirements - and at the same time minimising the risks associated with poor data management. It is also about the culture of the company and enabling the organisation to have a clear strategy, vision and goals around data.
Getting value out of data is the end destination in a long and difficult journey and it is impossible to reap the rewards without putting in the hard work. Data governance is a great aid to this journey and is an enabler which can turn data from information to an actual asset.
If we want to harness the value of data, especially monetary value, we should govern it just like we would do with any other asset.
Figure 2: Currency vs Data Comparison
When thinking about data like you would money, the business and IT (traditionally seen as two very different entities) can work together to achieve the company’s goals. While data is owned by the business and it is used for strategic and operational decisions, it is managed by IT.
Success also needs to be clearly defined as a north star and it will be different for every organisation – while for some it may be monetary value, for others it might be better decision making.
For governance to be effective, it needs to be built-in by design into the applications and processes landscape (governance by design). This will help the organisation move away from being applications/process centric to being data-centric. It also should be embedded in every step of the data journey – from creation and definition to sharing, usage and disposal. Figure 3 is an example of the data governance components needed for each step of the data journey in an organisation.
Data governance is a key enabler on the journey to getting value out of data
Figure 3: A Typical Data Governance Journey
The governance of tomorrow is about ensuring that the right culture is in place to facilitate the necessary changes – culture is incredibly important. Governance also has to be holistic and tailored to the needs of the company. It must also be embedded across the entire data lifecycle and address data both at rest and in transit.
Do you want to be data-centric or data-driven decision-making organisation?
Organisations need to decide whether they want to be data-centric or data-driven and this very much depends on their data goals. Do they want to be a market leader or are they looking to put their house in order? The journey of each organisation is unique, and it should be tailored to its needs and long-term business goals. On the journey of reshaping data from information to an asset, organisations should focus on three key components: People, Processes and Technology. This will ensure success.
For more information on the future of data governance contact one of our experts (below).
Srikant (Sri) Kanthadai is a senior Director with the Data & Analytics Modernisation team focussed on the breadth of data management solutions (data governance, data quality, data privacy, master data management & data lifecycle management) and also focused on aligning our propositions to demands from the various market segments. Sri has wide experience in all aspects of data management around, data strategy, data governance, data quality, data privacy & master data management, having worked on multiple projects across various industry sectors. He is also acknowledged as a leading data management expert in the industry. Sri has over 25 years of experience in the IT industry and prior to Deloitte has worked at Capgemini (as Group IT Chief Data Officer and Global Head of Data Management), Cognizant (European Head of MDM) and with Hewlett-Packard in wide variety of roles. Outside of work his leisure pursuits are music, travel, reading & playing badminton.
Nataliya Dimova is a Senior Consultant in the Data & Analytics Modernisation team. Nataliya is an experienced consultant and her focus is on data trust, data governance and data privacy. Nataliya has an extensive experience in all aspects of data including data analytics, data governance and data trust, having worked on multiple projects across various industry sectors. Nataliya is passionate about helping clients to leverage technology and data in order to solve strategic business problems.