Enterprise Data Management (EDM) is the process followed by organizations to store and govern the business's data correctly. EDM is about developing best practices for allowing users secure, accurate, and timely access to the data they need. Good data management enables users to share and access data with low costs and improved performance. Because of the data's sensitive nature for the organization and its employees, it also requires managing the company's human resources.
Data management may rely on software to handle large data quantities an organization produces. Successful data management ensures that all data streams and types are accurately identified, defined, and accessible. Some of the assets an organization may need to manage include data generated by social media, financial information, photos, Internet of Things data, supply chain data, metadata, and mobile data. However, most data types may be grouped under three large categories, transactional, analytical, and master data.
Analytical Data: analytical data is used to improve the business's decision-making. Typically, analytical data includes information stored in a database designed to enhance and enable data analysis. Analytical data may contain information on historical prices, volumes handled by the organization, purchases, or even sensor data. However, what makes data analytical is using it for enhancing decision-making processes.
Transactional Data: transactional data is generated and captured from the organization's daily transactions. Transactional data may record the time of a sale, the client, the port of entry, the price point, discounts, and any other quantities of interest associated with the transaction under analysis.
Master Data: master data is used to represent the business objects subject to transactions. Master data may be used to present parties such as individuals, vendors, or other businesses. It may also be used to create financial structures to define accounts, documents, and assets. Master data may define objects such as the products the company trades. Finally, it may identify sales territories, branches, and other locations. Master data offers organizations the ability o create a single definition for each different object it handles.
Main Components of Enterprise Data Management
Enterprise data management has six essential components. These components are data governance, data integration, master data management, data security, metadata management, and data quality management.
Data Governance: data governance is the process of ensuring the quality, usability, security, and integrity of the data collected and produced by an organization. Data governance also ensures the accessibility of all incumbents to the data in a form that can be used to generate the desired business outcomes. Data governance must comply with regulations, be auditable, and be accessed only by the permitted stakeholders.
Data Integration: data integration is conducted to offer "uniform access" to multiple, heterogeneous data sources. Data integration combines and transforms data from multiple sources to improve a business outcome. Data integration provides a single view or dashboard from data found in databases, data lakes, and data warehouses.
Master Data Management: master data management refers to creating a single master record for each object, person, thing, or entity in a business. With master data management, it is possible to make the "best version of the truth" by creating a golden record that contains only the essential information needed to define an entity. With master data management, companies reduce costs by having single sources of truth. Users have a higher trust in organizations using master data management because it guarantees that the information is current and necessary for the business to perform its operations.
Data Security: data security is described by IBM as the "practice of protecting digital information from unauthorized access, corruption, or theft throughout its entire lifecycle." Data security should protect the data from unauthorized access, inappropriate use, dissemination, modification, or deletion.
Metadata Management: metadata management is concerned with transforming informational assets into enterprise assets. Using metadata management allows organizations to improve the search and access to data and business semantics management and interoperability in the form of a mutual understanding of data assets and their definitions.
Data Quality Management: data quality is defined as the capability of data to help a business satisfy its technical and system requirements. Data is said to be of quality when it is fit for its intended purposes and uses in operations and decision-making processes. Determining data quality requires understanding two critical aspects, intrinsic and contextual. Data is said to have intrinsic quality when it is based on elements that are not dependent on any other factors, such as age, salary, and weight. Contextual data quality is defined as how each individual perceives the data. Some aspects used to measure contextual data quality are accuracy, completeness, validity, reliability, uniqueness, current, consistency, and accessibility.
Discussion on Best Practices in Enterprise Data Management
Best practices in data management lead to improved data analytics within the organization. Although some models differ in best practices, most agree that the process should start with understanding the business goals. Understanding the business goals allows the team to only store information that is of value to the organization. Next, the author will discuss some of the most important trends identified as best practices.
The focus must be made on data quality: this means that the company must ensure that the data is readily available, accurate, complete, and relevant. Data quality is a continuous process. The organization must ensure that the stored data meet the team's expectations and can be used in decision-making.
Following a 3-2-1 methodology: this methodology is used to protect the data by creating three copies of it in two different types of storage, with one of the storages being offsite.
Data recovery strategy: this practice requires organizations to understand their legacy systems and review them to identify gaps in their workloads, storage, and applications. It also means that organizations must identify the data that needs to be recovered and how often it must be backed up. Finally, it is necessary to conduct regular tests of the backups and their performance.
Data quality management software: These are tools used to automate the processes that make the organization's data fit for business analytics, business intelligence, machine learning, and data science purposes.
Commitment to data culture: this practice requires organizations to train their departments and leadership to prioritize data and data experimentation. To successfully achieve a data culture, it is necessary to have a strong and well-respected sponsor who enables strong collaboration in the organization.
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