Previously reserved for a small group of specialists, Business Intelligence has been democratized so that each employee can access the data that is useful to him to make better decisions and take action.
Leaving the commercial sphere, it is now also at the service of the company’s support functions (HR, marketing, supply chain, etc.), thus multiplying the use cases for the company’s different business lines.
A strategy of acculturation to data has often accompanied this democratization, also called data literacy, to make all employees adhere to the company’s data strategy and to offer them the appropriate training. Today, customers are looking for solutions that enable them to manage better and make the data processing chain more reliable, which covers the entire lifespan of data in a company, to transform their heterogeneous data in silos. This data is consistent, of high quality and easily understood by users. What if the solution was in Master Data Management (MDM)?
Towards A More Agile Analytical Chain
The sinews of war in any business BI project is to implement an analytical chain that is agile and fast to provide quality, standardized and secure data to business users in near real-time.
The more complex international companies are with disparate decision-making systems, the more tedious this can be. However, once implemented, this analytical chain’s benefits make it possible to accelerate the provision of quality data in the right place and at the right time to cover constantly changing business needs.
Whether it takes place upstream or in parallel with a BI project, the implementation of an MDM solution significantly contributes to simplifying the implementation and management of the analytical chain and ensuring its flexibility. . The very essence of such a project is to obtain quality indicators, which, in turn, immediately provide up-to-date, reliable, enriched and consolidated data. The resulting analyzes are much more relevant and secure for decision-making at the right time.
The Rise Of New Data Architectures
Beyond the technical contributions, implementing an MDM project significantly accelerates the development of a data culture within the company.
During such a project, the IT and business teams must agree on a set of definitions to establish a common language, for example, what is a customer? A deal? This alignment between the actors on a shared vision will facilitate the provision of a joint database to the different businesses and thus concretize the strategy concerning new data architectures, like the data mesh and the data hub.
Finally, the correlation between MDM and BI projects will greatly facilitate initiatives around data since MDM brings a methodology and a culture which, defined upstream, help all players, whether IT or business, to collaborate and share a common vision of data. This is a significant benefit because when a business team thinks about its BI needs, it will also consider its data assets. There is then a work of architecture around the data, in which the business team is an actor and not simply a consumer.
Combining BI and MDM, therefore, seems evident for any corporate data strategy. While one or the other is possible, companies with difficulty building their BI will find their answers in MDM. Because today, if BI tools are easily exploitable by self-service businesses, the challenge lies in the provision of readable and understandable data. This is where MDM comes into play in the decision-making process. Beyond BI, MDM demonstrates project after project its ability to bring business value in other areas, such as productivity gains, cost savings and operational efficiency.
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