What is Data Enablement?

Another buzzword or a groundswell ?

While Data Management and Data Governance are still relatively recent practices for many companies, what value brings Data Enablement ? Is this yet another buzzword that will quickly fall into oblivion or is it a development that makes sense, a groundswell ?

While there seems to be widespread recognition that data is now an important lever for innovation, growth and competitiveness, few companies claim to have succeeded in creating a tangible impact.

The stakes are high : better understanding customers and trends, identifying new opportunities, enhancing operations or better managing risks.

Many companies have launched multiple Data projects in this direction. Setting up Big Data infrastructures and Data Lakes, democratizing data access and allowing a growing number of people to exploit data in Self-Service, deploying Analytics and Data Visualization tools are just a few examples.

And yet, leveraging the vast quantity of data generated by applications, processes, users and devices to create effective value remains a major challenge for many companies.

There are many reasons for the low number of organizations that have actually managed to adopt structured and repeatable enterprise-wide practices with tangible results. Beyond the factors that may be contextual to each company, several studies point to the same findings. Lack of awareness and qualified people, limited communication between departments, insufficient sponsorship, lack of incentives, tool adoption issues and a lack of Data culture are among the most frequently identified challenges.

In recent years, data management and data governance programs have been implemented in companies to lay the foundations for effective and sustainable use of data. Outcomes in most mature enterprises include the identification of roles and responsibilities, the definition of policies and rules, the implementation of data dictionaries and catalogs, as well as the enhancement of Data Management operations, such as data quality. Despite some encouraging progress, foundational disciplines like Data Governance are still perceived to be complex and difficult to implement at an enterprise-wide level.

Data Enablement is not (despite the temptation) a substitute for Data Governance and Data Management which remain essential components of the process. It’s an outcome-focused approach with a seemingly simple objective : ensure that the right data is provided to the right resource at the right time. And this, in conformance with regulatory standards and the rules of Ethics. Data Enablement is the action of providing to people and resources in organizations the necessary means to use data in an efficient, informed and responsible way at an enterprise-wide level.

From people’s perspective, Data Enablement consists in creating and reinforcing Data skills and know-how within the teams through training and other practical support means. It is also about creating a Data culture to raise awareness of Data stakes and associated practices, to facilitate collaboration between teams and to ensure the effective operationalisation of Data strategies. Successfully aligning teams at different levels of the organization is a critical success factor and helps prevent that "culture eats strategy for breakfast" as Peter Drucker put it.

In terms of technology, the approach places particular emphasis on the adoption of the tools by the teams. Various studies show that the success of technology projects is closely linked to the level of robustness and user-friendliness of the tools.

Finally, in terms of methodology, Data Enablement is not a project but a discipline that allows for a profound and lasting transformation of the company's data management practices. Unlike a project that has a beginning and an end, it must be considered as a continuous activity that must benefit from the ongoing engagement of individual contributors as well as executive-level sponsorship.

Data Enablement plays an even more important role in the context of the digital enterprise and the increasing use of data by technical resources such as Artificial Intelligence (AI). A few examples include in that regard the automation of business critical operations, predictions and automated decision making that may be sensitive in several aspects.

With a proactive approach that promotes data culture, encourages responsibility and initiatives at different levels of the organization. Data Enablement enhances existing practices to build trust and secure successful outcomes of Data projects.

Shelemat DANIEL, Août 2020