Enterprise Data Management (EDM)

What Is Enterprise Data Management (EDM)?

Enterprise Data Management (EDM) is an enterprise’s ability to identify, integrate, restore, and stockpile data for internal applications and external communications. EDM exists to ensure the credibility of all the data assets of the organization. With EDM, companies can engage in informed decision-making and analytical planning through useful, accurate data.

EDM is also a concept that addresses the transmission of different data sets within processes and applications that rely on the consumption of these data sets to complete business processes or transactions.

The main purpose of EDM is the removal of organizational issues and conflicts resulting from the mismanagement of information and data, by implementing a structured data delivery strategy - from data producer to data consumer.

EDM is comprised of software applications, computing and network infrastructure, business logic, and policies used to manage enterprise data flow. An organization implements EDM through a variety of processes and requires collaboration through different departments, such as IT, finance, and operations.

Enterprise data management is important because it delivers a highly standardized and streamlined system to organizations. Through this system, companies can search, manage, access, store, and secure their data. Enterprise data management also ensures that companies can easily find and analyze the data for their internal analysis and thereby take well-informed decisions and define strategies around these decisions.

Components of Enterprise Data Management

• Data Governance: Data Governance is the umbrella over EDM. Each component will be guided by what governance criteria are agreed upon based on business goals and regulatory or geo-location requirements. An enterprise data management best practice is to establish Data Governance on the recommendations of standards such as ISO 38505. Data Governance will define the policies, metrics, roles, allowed formats, business continuity, storage, event management, and reporting expected by the organization. These guidelines must be flexible to promote agility within the organization based on its acquisition and consumption of data to make decisions or perform work tasks. Sound governance leads to data trustworthiness.
• Data Architecture: Data Architecture defines the guidelines for data at each stage of its lifecycle: acquisition, storage, usage, security, archival, recovery, business continuity, and deletion. This is the HOW TO DO IT and WHAT YOU CAN DO aspect of enterprise data management. The outcome will be the frameworks for master data management, data warehouses, data security, tools, and supplier SLAs.
• Data Security: Data Security concerns keeping data as per the rules of Data Governance, the roles profiles of data access via Access Control Lists or Data Directories, the prevention and protection of the enterprise, passwords, and access methods such as Multi-factor Authentication (MFA). Data Security helps ensure that data at rest and in transit is not susceptible to theft, corruption, leakage, or malicious destruction.
• Data Ingestion or Acquisition: Data Ingestion or Acquisition is the extraction of data from your manual or paper-based processes, corporate applications, or digital download and the placing of the data into its proper storage location. This activity is real-time, and continuous, and can result in high volumes of data requiring management. Data placement could be in virtual, Big Data storage, data lakes, data warehouses, or filing cabinets, or at service partners.
• Data Integration: Data Integration is making data accessible by validating that the data is of the correct quality and type fits the intended use. Any issues are alerted to the requestor for correction, trigger an event to obtain more current data, or initiate the deletion of the possibly corrupt data.
• Data Consumption: Data Consumption is where data creates value for an organization by being turned into information for decision-making, marketing to customers, helping streamline processes, sharing or selling data to partners, and proving to all concerned that the organization is trustworthy. Data Consumption supports Business Intelligence, Artificial Intelligence, analytics, data mining, curating, and automation to create a seamless, quality flow of data into whatever service needs it on a timely basis, maintaining confidentiality, integrity, and further availability. The outcome of Data Consumption is the capability of the organization to serve customers, help staff, create agile products and address audit concerns.

Enterprise Data Management Strategy Best Practices

• Perform Assessment: Businesses need a clear understanding of their data flows and the types of data they have in order to craft an effective data management strategy. This work can be time-consuming, but it is a worthwhile, important process that can help ensure the methods of management employed are well-matched with the data.

(Define the Process and Deliverables: Data management can be a nebulous term. It’s important for companies to outline what they hope to accomplish by implementing enterprise data management. Questions to define the focus of an enterprise management strategy include:

• • What are the end goals?
  • What is not in scope?
  • How will success be measured?
    Demands for data can at times be overwhelming and some data projects can be remarkably large. In those cases, a phased approach with incremental deliverables can work well.
• Determine Standards, Policies, and Procedures

Standards, policies, and procedures are invaluable guideposts, keeping data where it needs to be and helping to prevent corruption, security breaches, and loss of data. The success of standards and policies hinges greatly on the procedures in place to enable them. Procedures give staff members methods and tools they can use to meet required standards. Policies are also an important consideration when it comes to regulatory compliance, especially in highly regulated industries like healthcare and financial services. Not only do they protect data, but they also help prevent fines and penalties, and help preserve customer confidence.

• Educate and Inform Stakeholders: Enterprise data management is sure to fail if the standards, policies, and procedures surrounding it are not properly disseminated and emphasized. Additionally, enterprise data management strategies are better positioned for success if all of those who deal with data is on board with the project. Consider an education campaign to ensure a company-wide understanding of the enterprise data management goals, the methods to achieve them, and the reasons behind the initiative. This equips staff members with a full understanding of why certain rules are in place, instead of just asking them to blindly follow them.
• Emphasize Quality: Bad data is actually worse than no data at all. Adopting a culture of data quality will help protect your data’s security and integrity and ultimately preserve its worth. This is where data stewardship comes in. It’s important for businesses to remember how valuable their data truly is and how important it is to responsibly maintain its quality.
• Invest in the Right People and Technology: Understanding the art of managing data isn’t everyone’s forte. It’s best to have an in-house or consultative expert with experience establishing enterprise data management services and solutions. Their knowledge can help identify the right technologies to use for your particular use cases. They can also help your business avoid pitfalls, including accidental data loss or regulatory violations, thus aiding in a successful, smooth EDM implementation.