Data Management, A Clear Competitive Advantage
By: Alithya Group Inc.
"Artificial intelligence (AI) now provides access to a whole other scale and speed of computing and modelling capability that is ideal for handling big data, whether structured or unstructured."
While data is starting to attract attention, its potential still remains largely untapped. The arrival of big data and artificial intelligence has broadened our perspective and brought the possibility to anticipate change, control it and maybe even influence it. In that context, data management is a valuable differentiator, offering a means of staying a step ahead in a highly competitive market.
“Faced with an increasingly complex, ever-changing environment, companies must constantly adapt,” says Jean-Pierre Le Boeuf, a data management specialist with the Alithya Group. “To stay competitive, organizations need to know exactly what their production capabilities are and how to anticipate a market shortage or acceleration, let alone identify trends and innovate. That is where data management comes in, because it can provide some of the answers.”
In some highly technological fields, data is an integral part of a company’s activities. A biotechnology research company, for instance, relies on data to produce sample analytics for disease detection. To process its data properly and produce high-quality pharmacological studies, the company must be equipped with a series of very rigorous processes and sophisticated data management technologies.
Describe, predict and prescribe
Data management can be categorized into three distinct types that serve different purposes:
- The first use of data is to inform a situation through descriptive analytics. The idea is that we can only manage something we understand. For instance, if a real estate management group knows the precise status of its real estate portfolio in real time, it can manage its operations much more coherently and strategically.
- Data can also provide a window on the future through predictive analytics, using different modelling scenarios. In the insurance sector, for example, risk factors for accidents are calculated based on a driver’s age, health status and driving experience. This information then has an impact on the insurance premium charged. In other words, predictive analytics combines data and rules to identify a trend.
- And finally, artificial intelligence (AI) now provides access to a whole other scale and speed of computing and modelling capability that is ideal for handling big data, whether structured or unstructured. AI offers prescriptive analytics, which optimizes and enriches forecasting and facilitates decision-making by only selecting certain key indicators. AI is also a powerful tool use in the “What if “ scenarios analysis.
Well-managed data can produce reliable information
These three types of analytics are only possible if the data is reliable. To obtain reliable data, every stage of data management—definition, collection, storage, analysis and interpretation—requires the application of precise rules, rigorous processes and specialized tools, accompanied by clear responsibilities and scopes.
Jean-Pierre Le Boeuf has this to say on the subject: “After being entrusted to IT specialists for the longest time, data management is now shared between IT and business lines, which include data managers, commonly called data stewards. The involvement of these two groups is absolutely essential for effective, comprehensive and relevant data management.”
The business line specialists are considered the data owners. Their role is to determine the key data needed to achieve the business objectives and to define the associated rules and control their application. The IT teams are the technical custodians of the data, and see to the maintenance of tools and software used for data collection, storage and processing.
The role of data scientists trained in AI is also growing. We are witnessing a professionalization of data management, which is good news for companies, whose needs are skyrocketing.
Data, no longer just a by-product of the company's business, is now considered a true asset. Data management, in turn, is becoming a business discipline and a tool for change management.
The field of data management is changing incredibly fast. Companies need to jump on board fast and avoid falling behind, because catching up later could prove very difficult.