Enterprise Data Management (EDM) refers to a company’s ability to define, integrate, and accurately collect data from various sources with precision, attention, and effectiveness.
89% of business managers have stated that they use data to make most strategic and operational decisions. More and more companies are realizing the importance of having a functional Database and are increasing their budget for Data Analysis activities.
It is now evident that Big Data plays a key role in achieving all business goals.
In the EDM context, the main components are four: Data Governance, Data Integration, Master Data Management and Data Protection.
Data governance refers to a set of processes, roles, policies, standards, and metrics aimed at ensuring effective and efficient use of information that enables an organization to achieve its goals.
In particular, it represents all internal rules of the company and the procedures necessary to make continuous updates and improvements.
Governance ensures that the roles associated with data management are clearly defined and their responsibilities adequately agreed upon. Today, businesses are managing an incredible amount of information about customers, suppliers, employees and other entities, and if the data is used to better understand the market and target audience, the company can only benefit from success.
Data Integration is the process of merging data from different sources to arrive at a 360º vision of the business scenario, starting from assimilation, cleaning, mapping and transformation to the processing of Data intelligence that is easily accessible to those who access it.
Today, companies are implementing Data Integration initiatives to analyse and use information more effectively, especially with the spread of new Cloud and Big Data management technologies.
The main objective of this activity is the unification and accessibility of data, which are originally fragmentary, disorganized or inconsistent. In fact, Data Integration optimizes integration operations through the use of metadata and pre-packaged Machine Learning algorithms.
Solutions are not the same for all companies and the right formula varies according to individual needs and in relation to the reference market.
Master Data Management
Master Data Management (MDM) uses procedures very similar to Data Integration but with different purposes. In fact, it aims to make data accessible with a strategic business-oriented vision.
The importance of this activity, together with Data Quality, is based on the fact that it is possible to make the right decisions only based on consistent, reliable, and verified information. Models can make accurate predictions only thanks to correct data.
To understand MDM, it is important first to define “master data“, which includes crucial company data that includes information about products, suppliers and customers. When they are wrong, unstructured and hidden throughout the organization, they can negatively affect all aspects of the enterprise.
The presence of untruthful data causes confusion about KPIs, incorrect inventories, market acquisitions delayed, company reorganizations, and imprecise predictions of supply and demand. Without Master Data Management, it is difficult and not useful to develop winning and performing strategies.
Data Security has become a priority due to the increasingly sophisticated Cyber-attacks (click here to read our article dedicated to Cyber Protection) that make data protection essential.
Cyber Protection can no longer be considered an option, but something absolutely necessary. The goal is to secure all data in the various phases of Enterprise Data Management, both those in use and those stored and stored in databases.
ML and AI are strategic allies to face more evolved cyber attacks; sintelligence technological tools are very useful in the vulnerability testing of computer systems by security experts (also called Ethical Hackers), to check how difficult it is to have access to a company’s sensitive data or employees’ personal data.
We at Drive2Data believe that poor data quality significantly undermines business value. A proper Data Analytics strategy allows all the various departments to achieve business objectives.
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