The past few months have seen Data Management emerge as a hot topic, with many data initiatives having been kick-started without proper foundations or support structures.
This has been exacerbated by the silos that are naturally generated in organizations. However, businesses are recognizing the importance of establishing a strong foundation for building a data-driven organization. Some companies have acted early, while others have had to slow down to restructure in a more sustainable, organized, and governed manner. As a result, Data Management has risen higher on the priority list for those keen to evolve as successful data-driven organizations.
Over the years, we have worked with organizations at various stages, especially since the establishment of our Data Management Consultancy division in 2021. This has given us an in-depth understanding of the real-world challenges that organizations face in managing their data.
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Utilizing this experience, we have identified seven key trends in the Data Management field that companies should pay close attention to as we push through the remainder of 2023.
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Putting the Governance Model into practice
Many of our clients have a well-defined governance model on paper, but are struggling to implement it and establish a culture of “data accountability” in their day-to-day operations. This has been a common challenge in the past year, and we anticipate it will continue to be so in 2023.
Part of WHY this happens is a consequence of using the typical approach of operationalizing Data Governance through tools, which are not usually free (at least those with a suite designed for functional users, which are 95% of those involved in a data governance program) and whose investment justification is difficult to sustain (precisely because they do not understand the ‘return’ of the data governance program in directive layers).
Another common case is BECAUSE the governance model defined on paper does not adapt to the reality of the company (due to a lack of profiles with certain technical-functional skills, organizational culture, heterogeneity of business units, etc.). Defining a customized data governance model for each organization and being aware that a Wikipedia model is not necessarily the best solution is part of the beginning of this common pain point.
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Data Office redesign
Despite initial assumptions this was going to be a rare occurrence, we have observed numerous organizations in 2022 reviewing their data function’s organizational model.
Many companies are still starting to design a Data organizational cell as a spin-off from their digital teams (more commonly) or even still from technology (data under the direction of the CIO has little scope). And it is in this situation that doubts arise about both the suitability of the location and the separation of powers (functions) of the new Data area to be sized.
Organizations are also beginning to appear which, with a clearly mature Data Office, are considering needs not so much in terms of organizational redesign but above all in terms of talent and development of the profiles of the Data team. It is very interesting to see how the stewardship models as they were conceived fail (they have no long-term development), although this is a trend that we believe will be contrasted and extended in the future, it is still very incipient.
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The problem of data quality persists
A strategic use of data quality, aimed at driving change and achieving cross-cutting objectives, remains rare in organizations. This approach is undoubtedly the most effective tool available to a Data Office seeking to foster a data-driven mindset within its organization.
Data quality has to be used as the main tool to empower those involved in the Data Governance Program to perform their functions with visibility of their impact, and it is not always done this way. It must be.
It is also necessary to give an understanding to non-technical users (who represent sometimes more than 50% of the total data quality efforts/needs) about what data quality is or how it is calculated. Something so simple is not being done and is starting to attract more and more interest from CDOs and Data & Analytics leaders.
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Cloud platforms extend catalogue capabilities into data governance
Over the past few years, we have observed a development in the data catalogue solutions provided by major public cloud vendors such as AWS, Azure, and GCP. These solutions have been moving towards more user-centric and functional front-end capabilities.
We saw how Azure’s Purview was positioned somewhat further ahead of its GLUE Data Catalog counterparts from AWS or Google’s GDC, but with a more ‘defensive’ approach oriented towards regulatory compliance and control of data assets from a hub.
At the last AWS re:Invent we have seen DataZone (with a more ‘offensive’ and self-service oriented approach) presented as a solution beyond the old GLUE Data Catalog and the outlook is that this is the way forward. On the other hand, GDC has integrated its GDC catalogue service into another service with data marketplace ambitions called Dataplex.
Although we do not expect that in 2023 the large public cloud vendors will be able to raise their current tools to the level offered by traditional vendors of Data Governance solutions, we do believe that they will continue to invest in this line and get closer and closer to the end user (mostly non-technical). In the coming months, they will seek to cover and develop functionalities that were not contemplated until now (workflows between roles in the governance model, alerts, data marketplace, glossary of terms, improvements in lineage visualization, etc.) on top of their current capabilities of complete integration of the metadata catalogue and lineage.
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Engaging the organization in data and analytics projects
This is a consequence of both the difficulty in implementing data governance and the lack of strategic use of data quality.
Many companies have been deploying communication and change management programs for years, some even have Data Literacy programs in place, but the “last mile” is still seen as a failure.
In this sense, we see that work will continue and we believe that in 2023 this aspect will continue to improve a lot, perhaps it will be time to see great achievements and milestones in terms of change management and the achievement of a mindset that is no longer just data-driven but data-asset (making it clear that data is a valuable asset and not “something from IT”).
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The democratization of data: The great unknown everyone’s talking about
This is a topic of conversation that keeps coming up, but one that is rarely translated into something that is actionable, and simple to understand and address. Despite the desire of companies to become data-driven and mature in terms of data democratization, there is often a lack of clarity on how to translate these aspirations into practical implementation. As a result, there can be a silence or gap between the desired outcomes and the actual steps needed to achieve them.
We will keep on talking about the data democratization and there will be an increasing interest in knowing how to calculate it, something we have not yet seen and for which we have developed our own calculation formula: the Data Democratization Index (DDI), a practical and grounded way of measuring the degree of democratization of real data, which can be tracked and monitored to measure the impact of our data & analytics initiatives and their reflection in the daily lives of users or not.
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Step-by-step: realizing the value of data
Undoubtedly, this is, will be and will continue to be the CDO’s great challenge for several years to come. Knowing how to measure the value of data (both intrinsic and derived) and the impact generated in business terms by the different data & analytics initiatives led by the Data Office. Even some ambitious or challenging CDOs like the idea of building themselves a P&L, and it seems that this will become more and more common.
In our vision, we advise our clients to take a step-by-step approach. Begin by measuring the impact, translated into business terms (operational efficiency, additional revenue and risk mitigation), of data & analytics initiatives. A measurement agreed with the business (or functional users involved) through metrics previously established and contrasted with measurements prior to the implementation of these D&A initiatives.
Once we have measured dozens of D&A initiatives and learned to refine assumptions and previous premises in order to know more and better how to estimate the future impact, we will be able to move on to a second phase of measuring also the intangible impact (culture, customer or even employee experience) or even the intrinsic value of the data (the monetization of data that is so much talked about and so difficult to achieve, similar to the democratization we mentioned earlier).
This is a long process, but haste was never a good advisor. Learn to walk before you run.
