The challenge facing the newly appointed CDO: how to build a data governance policy from scratch

 
The other day, on 26/09, at the event CDO Day, I was very fortunate to participate in the expert panel entitled «The challenge of the newly appointed CDO: how to build a data governance policy from scratch» alongside Julio Valero from Banco Santander, Manuel Ferro from Abanca, and Jesús Armand Calejero from Funidelia.

The debate that ensued was very enriching, and the experiences shared will surely be useful to more than one of the attendees who have the difficult task ahead of them of implementing a data strategy and governance in their organisation. For this reason, and also at the express request of several people who were unable to attend, I do not want to miss this opportunity to share the main keys that were made during the session, expanding on them and including my point of view on each one.

I hope these lines serve as ideas and guides for those who may be seeking answers to their questions within the field of data governance.

Data culture and communication

Without a doubt, the main point to bear in mind is the data culture that exists within the organisation at all levels...

• «Are there specific departments or roles focused on data?»
• «Is the data present in corporate and employee objectives?»
• «Are there policies and procedures in place that focus on the use and processing of data?»
• «Do senior management, middle management and other employees consider data to be a strategic asset for the organisation?

The maturity of the organisation in terms of understanding data will determine the starting point of our model of government, and both the culture and the profiles of the people who make it up will indicate to us how quickly we will be able to move forward with change management or where we need to focus our efforts when undertaking initiatives.

Like any initiative that stems from the culture of the organisation and its employees, it requires a very important communication strategy, focusing on reaching all levels and giving visibility and importance to the changes that are undertaken, as well as involving all those concerned.

Data governance has three pillars: technology, processes and people; of which, without a doubt, the most important and complex element is people, as they are the ones who must embrace the data culture we want to establish so that they can carry out their daily tasks in the manner expected by the organisation to help achieve its objectives.

Stakeholder involvement

Following on from the above, the people involved need to feel empowered for decision-making and to be seen as part of the process of change, recognising its value while taking on a series of responsibilities regarding data that they may not have had until now.

It is very important to make the stakeholders that data governance does not consist of “put a spoke in the wheel” o “play the bad cop” but rather consists of providing the organisation with the necessary capabilities to create value to the business through better use and processing of data. To this end, it is essential to also provide them with the tools and the resources necessary because without them, your workload will increase and you will not see a quick return.

Formulas that can be used to achieve this involvement may include the inclusion of objectives o bonus with regard to data governance initiatives, involvement in committees or important meetings, the visibility before colleagues and managers or the granting of space for decision-making.

Equally important is the creation of a collaborative and interactive environment where data governance is built on the contribution of everyone, not just the involvement of a few roles and/or areas, as this causes frustration in some and misunderstanding in others.

Involvement of Senior Management

As is inevitable with any cultural change in an organisation, senior management must be the first to get involved in the initiative because, ultimately, they are the ones who will make the strategic decisions and priorities which will determine where resources are allocated to tackle them with guarantees.

In this case, it can be understood as another stakeholder that must be present throughout the entire governance model with its role and functions, but also your support is essential since it must empower those who require it and who are the first to expand that power. data culture to the entire organisation.

It is not easy to measure the ROI of a data governance initiative in the short term, but it is necessary if senior management is not the one deciding on on one's own initiative launch it with all its effects, we will be able to sell it properly to achieve the involvement that is so necessary, because without it we will not have the resources required to achieve our objectives.

First steps: the assessment

How can I be expected to produce metrics that will secure internal approval for this type of initiative when I don't even know where to start?

The first step after deciding that we want to implement data governance at all levels of an organisation is know where we are starting from.

Just as we need to understand the existing data culture or resistance to change that we may encounter, it is very important to know what we have, what we want to achieve, and what tools we have at our disposal to do so.

A good initial analysis will not only give us an idea of roadmap detailed and more likely to meet objectives, but it will also enable us to measure our KPIs before starting, so that they can be compared with the measurements at the end of the exercise and thus obtain the improvement results.

The importance of the use case

Once we have completed an initial analysis and have the approach in place As-Is vs To-Be At a high level, the next thing we need to do is select a use case and set ourselves a time limit to measure results. This step is very important as it will allow us to achieve a time to market quickly with a limited scope, and we will be able to measure what we have achieved in order to sell it internally.

One of the keys to making our use case a success is choosing one. appropriate and eye-catching. The choice of this use case is not trivial and depends on many variables that are largely conditioned by the culture and maturity of the organisation itself.

For example, we can consider the following scenarios for choosing a use case:

• Focus on one of the reports that are of most interest to senior management and implement a governance system end-to-end of the generation process and the data represented therein. This has the advantage that we can achieve good internal sales, as it is an important use case by definition and can surely be easily extrapolated to other areas due to its typology. cross However, we may also fall into the trap of choosing a complex use case that is too broad in scope and involves too many stakeholders, who will also be highly exposed to the organisation's management.
• Select an area of a department Specifically, in such a way that we reduce the scope and number of participants, but we may also be making the mistake of doing something very ad hoc and that we cannot then extrapolate to other areas, as well as choosing a use case that does not have much impact on senior management.
• Finally, we can look for something else. innovative and jump on board with a new strategic data initiative, whether it involves implementing new technologies, developing algorithms with advanced analytics, or capturing and processing new sources and types of data. Like everything else mentioned above, this also has its advantages and disadvantages. The upside would be that we would have the support of senior management and quickly get stakeholders on board, as it is a strategic initiative. Furthermore, by starting the initiative from scratch, we could work together and achieve milestones quickly. The downside would be the difficulty of measuring the results obtained from the implementation of governance in the initiative, as we would not be able to measure the before and after and would have to find another way to obtain these metrics.

Whatever the use case, what is clear is that we need what we do to be as extrapolatable possible to other areas and use cases in order to extend this governance model throughout the organisation, imbuing it with the data culture we talk so much about.

Metrics for measuring results

We have mentioned this above and throughout the article, but it is simply because of the importance of defining and measuring the metrics that will enable us to achieve a internal sale of our initiative.

The problem with metrics in government initiatives is that they are not easy to define, since in most cases we cannot obtain a Direct ROI in monetary terms.

Only in cases where the data has a economic value for our business or directly impact our income statement, we can calculate that ROI directly, but in other situations, we will usually have to infer that economic impact from the reduction in time, errors and costs and also taking into account the satisfaction of those involved with the new way of doing things.

In addition to metrics that help us with internal sales, it will also be very important to define metrics for monitoring the initiative and achieving objectives. These metrics must primarily cover the following aspects:

• Degree of government coverage (completeness of implementation)
• Stakeholder involvement (in terms of quantity and quality)
• Data quality (basic controls)
• Time spent on tasks (dedication in each team's day-to-day work)
• Identification of bottlenecks«

Technological solutions and tools

They are not essential from the outset, and it is important that we know when to incorporate them into our model, but they will certainly help us achieve our objectives and, above all, achieve the involvement of all those involved as soon as we include them in the photo.

To achieve this involvement, it is essential that they include a high degree of automation of manual tasks and that they offer a user experience pleasant and intuitive, where the learning curve for its use is not an added problem.

In this sense, technological solutions must be understood as accelerators y facilitators of change.

It is also important to understand that implementing a tool is not the solution on its own and that we will need integrate it into our ecosystem both technological and governmental, which is why it is also crucial that we make our choice based on our variables and conduct a detailed study of the available options.

Variables such as the capabilities of configuration y adaptation, the scalability and the interoperability are essential in a data governance solution in the current era, where it is very important that we can opt for fully agnostic to technology that we have available for data processing and that we can adapt to our governance needs while evolving as these advance.

Given this scenario, it is logical to consider whether the best strategy is to opt for a specific market solution offered by a supplier, carry out internal developments with in-house or external staff, or, finally, reuse tools available in the organisation with this approach. Normally, choosing one of the options does not usually exclude the others, and what is usually considered is an environment where different parts of different kinds fit together to form a puzzle that it fits in with our vision, which is ultimately what matters.

Conclusions

In conclusion, one of the most noteworthy aspects we were able to draw from the session was that there is no white paper or instruction manual that is fully applicable such as those that someone who wants to comply with specific regulations or install software might follow. Rather, it is a matter of collecting a series of best practices, implementation guides y experiences which must be transferred to the specific context of each organisation, always seeking to align what is implemented with the corporate strategy.

In line with the above, the difference between companies in different sectors and of different sizes was also evident, mainly due to:

• The regulations and standards they have to comply with
• The amount of data available and the ease of obtaining new data
• Investment in technologies capable of processing large volumes of data
• The importance of data in your strategy and the competitive advantage you can achieve by using it
• Direct margins obtained through the correct use of data within your particular lines of business

For a new CDO, implementing a data culture and governance within their organisation is a significant and challenging undertaking, but there are increasingly more mechanisms and information available to help them achieve their objectives.

I hope this short article serves to shed some light and guide those lost in the forest, just as anjanas…