Technical data

How data went from technical resource to strategic business asset

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This article was written by Hassan Lâasri, consultant in data strategy, data governance and data activation.

Since McKinsey’s report on big data in May 2011, we have entered an era where virtually everything we do on this planet is designed and digitized to generate data, consume it, or both. Recent projects, including the Metaverse, want to translate Earth into a virtual data planet. Since that report, data has been seen as a strategic asset in any business whose business depends on data – not just Google, Amazon, Meta, Apple and Microsoft, which have all led the way. But before we get to that, the data itself has gone from a purely technical resource to a valuable asset.

Today, data is collected, organized and enriched, not only to evaluate the performance of the company, but to predict its future. Very soon, these predictions will largely be transformed into decisions and actions. The credit sector, the engine of the American economy, is already showing us the way.

Data: a resource that has become a company asset

Barely ten years ago, data was an IT resource that supported businesses and functions. As such, it was usually managed by the DSI, whose mission was to build the data architecture, choose a database supplier and design applications linking the databases to the needs of internal users. These apps were primarily descriptive analytics dashboards, which allowed companies to get a status of their performance against their goals. This is what is meant by economic intelligence (BI) in the financial departments of large organizations.

Then came the first metamorphosis. Dashboards have been enhanced with predictive analytics, whose scope of analysis is no longer limited to what has happened over the past months and years, but what could happen if there are significant changes in industry regulation, market dynamics and corporate strategy. This transformation has propelled the uses of data science and machine learning in today’s businesses and includes use cases in advertising, marketing, sales, customer relationship management and supply chain management.

Since this change, a new transformation is underway, starting in the banking, insurance and health sectors in the United States and China and crossing the oceans to land in Europe with promises, realities and new regulations. It consists of transforming predictive analysis into operational decisions. The promise of this new transformation is to create a virtuous circle where not only is data analyzed, but that analysis is transformed into decisions and actions that generate new data. This prescriptive analytics complements descriptive analytics and predictive analytics in the same way a robot learns to walk and walks while learning. You can compare this to reinforcement learning, but simpler technologies can do the job. For example, technologies used by large financial institutions can automate the process of lending to individuals based on data provided by individuals and learning models of calculated risk scores.

Contrary to what one might think, transforming data from a technical resource into a strategic asset is not easy. Indeed, all great historical companies dream of being like Google, Amazon, Meta, Uber and Airbnb, but they were not created with data or machine learning in their DNA. Therefore, their existing data cannot be directly activated for strategic competitive advantage. Incumbent companies need a new kind of data practice.

Data governance: a necessary step

It is not enough to bring all the data of the company into a data platform for the data to be transformed into knowledge, forecasts and decisions. Indeed, all data does not have the same age, the same structure, the same format, the same quantity, the same quality, and above all, the same usefulness. If an attribute is important for one job, it is not automatically important for another job, even within the same company. Each business has its own vision of a product, of the customer, and of any entity managed by the various players in the company. In the luxury sector, for example, a dress, a bag or a jewel, although considered as unique objects, are seen through different attributes according to the databases where the same objects are stored. Seeking to leverage all the data available in a business to extract predictions and decisions from it requires a new project, known as data governance.

Like any general title, there is no consensus on the definition of data governance, and it should not be confused with data management or quality management. For my part, I define data governance as the organization, processes and tools put in place so that data is ready to be activated by the models and algorithms of data scientists so that data science is able to hold his promises. Successful initiatives have always had data owners who are business people familiar with the needs of their profession, as well as technical teams often made up of data architects, data modelers, data engineers and sometimes of data scientists.

In an architectural model represented by tiers, where the highest tier corresponds to business needs and the lowest tier to technical resources, data governance would sit below data science and above data management. From a practical perspective, data governance integrates, unifies, and harmonizes the data that data scientists can use based on the data stored in source systems. Data governance is where corporate policies are set and industry regulations are enforced.

The multitude of regulations that are springing up all over the world, sometimes with regulations by states in the same country, make data governance even more complex and an even more sought-after capacity, the way data science l t was at the start of the data age. Data architects, data scientists and data stewards must now integrate discrepancies, such as the impossibility of exploiting data outside the territory where they were collected, or even of using recommendation algorithms without explanatory capabilities. Dreaming of a “one-size-fits-all” global data platform is no longer relevant. From now on, pragmatism prevails: one platform per continent, even per country; otherwise, the budget would be worthy of those of major transformation programs at the national level.

Future data transformations

In less than a decade, data has gone from a resource for evaluating business performance to an asset used to predict the future of business. It will soon become an asset to automate and improve decisions. These two rapid transformations were made possible thanks to an awareness of the strategic side of data governance, without which there would be no data intelligence. It helps transform businesses into lifelong learning organizations where data helps find opportunities, machine learning turns that data into knowledge, and AI turns that knowledge into action, thus closing the virtuous circle. what the data promises. Think of a marketing campaign where an AI uses data from previous campaigns to create a prospect profile, then chooses a communication channel to reach prospects, then selects appropriate messages for different groups of prospects, and finally collects new data to the next campaign. This AI will relieve marketers of routine work, giving them more time for design and creativity rather than campaign execution.

As for its future, no expert and no algorithm can predict it exactly. What is certain is that data interferes in all economic activities, to the point that it has become ubiquitous as an asset that financiers value in the same way as customer bases, patents, brands and other intangible assets. . Data, not algorithms, is what makes Google, Meta and Amazon the big three in digital advertising. Data is also what made Netflix and Amazon two powerful production companies. This explains why large organizations are investing in internal data marketplaces, where the goal is not just to store large volumes of data, but to ensure that this data is consumed as competitive knowledge. This also explains why new entrants prefer to capture data first, even if it means losing money, for a much greater return on investment.

Hassan Lâasri is a data strategy, data governance and data enablement consultant with over 15 years of experience in business transformation, revenue growth and cost reduction.


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