Consolidate, integrate, and clean data from a variety of sources; get a unified reference data base for your entire organisation.
While data is the oil of the 21st century, it has to be sorted, verified, secured, and managed so that management can use it as a basis for making informed decisions.
The objective of Master Data Management (MDM) is to create a single, main source of reference for all important data (business, marketing, manufacturing, operational, financial, etc.).
At Adastra, we have implemented and are now working on MDM projects. In practice, these are complex, very sophisticated tasks, as large organisations process tens of millions of data units from dozens of internal systems. We deal with both data access itself and means of handling data.
MDM issues are also often discussed at the level of the parent company and individual subsidiaries, as a single data unit is found in many systems and in many forms. And it then often happens that such redundant data is not consistent - either in terms of content or time.
89 % of executives agree that inaccurate data hampers an organisation's ability to provide excellent customer experience.
To deploy MDM solutions, we must first know the data in which we wish to implement MDM. First, we find out what data is located on what systems within the company. Then we unify it at a single location, where it can be analysed.
It is essential for the implementation of MDM processes that all data is stored in a central location. In the first phase, discovery therefore initially provides information about data systems in which data is located, and this data is then "transported" to a single location for further use. Input data from various systems is integrated using different platforms (Windows, Linux, iOS, Android), which provide various technologies (web services, REST API, SQL, CSV, MS Excel) in different fragments (increment, full frame). The entire data transfer process must be managed, because data units are provided at different intervals and are dependent on each other. We can monitor the management process and audit the results.
Mastering operations assume that the records with which they work have a certain data quality, as poor-quality data can lead to significant deterioration of the results of mastering. We evaluate the quality level according to the rules and then improve it, either manually or automatically.
Each quality assessment is a value that states the quality of a record and how it can be handled in subsequent processes. Extremely poor-quality records can completely change the view of the resulting mastering.
Data mastering is about how to consolidate data from multiple sources to provide a consistent view of records for surrounding systems. Mastered data therefore serves as a reference source for other systems. We usually add more data to it, so-called metadata, which indicates how to handle this data.
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One-third of data on customers or potential customers is, in some way, poor quality.
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