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.

What does Enterprise Data Management do for the Business?

Establishing a well-defined EDM framework primarily means defining and enforcing rules and regulations that will unify the way your business uses data in all its processes. Standardization across an enterprise is crucial for many reasons. First off, this involves defining workflows for every business process within the enterprise: the what, how, why, who, when, and where aspects of data.

EDM covers the entire organization’s data lifecycle:

• It designs and describes data pipelines for each enterprise data type: metadata, reference data, master data, transactional data, and reporting data.
• It specifies data structure definitions for each dataset being used: the user of a particular dataset will immediately have information about the type and structure of data contained in it.
• It ensures that business rules are valid and implemented in the right places within the process of transforming data into the final product.

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.

Implementing an Enterprise Data Management Program

Implementation of an EDM program encompasses many processes – all of which need to be coordinated throughout the organization and managed while maintaining operational continuity. Below are some of the major components of EDM implementation that should be given consideration:

• Stakeholder requirements: EDM requires alignment among multiple stakeholders (at the right level of authority) who all need to understand and support the EDM objectives. EDM begins with a thorough understanding of the requirements of the end users and the organization as a whole. Managing stakeholder requirements is a critical, and ongoing, process based in an understanding of workflow, data dependencies and the tolerance of the organization for operational disruption. Many organizations use formal processes such as service level agreements to specify requirements and establish EDM program objectives.
• Policies and procedures: Effective EDM usually includes the creation, documentation, and enforcement of operating policies and procedures associated with change management, (i.e. data model, business glossary, master data shared domains, data cleansing and normalization), data stewardship, security constraints, and dependency rules. In many cases, these policies and procedures are documented for the first time as part of the EDM initiative.
• Data definitions and tagging: One of the core challenges associated with EDM is the ability to compare data that is obtained from multiple internal and external sources. In many circumstances, these sources use inconsistent terms and definitions to describe the data content itself – making it hard to compare data, hard to automate business processes, hard to feed complex applications, and hard to exchange data. This frequently results in a difficult process of data mapping and cross-referencing. Normalization of all the terms and definitions at the data attribute level is referred to as the metadata component of EDM and is an essential prerequisite for effective data management.
• Platform requirements: Even though EDM is fundamentally a data content challenge, there is a core technology dimension that must be addressed. Organizations need to have a functional storage platform, a comprehensive data model, and a robust messaging infrastructure. They must be able to integrate data into applications and deal with the challenges of the existing (i.e. legacy) technology infrastructure. Building the platform or partnering with an established technology provider on how the data gets stored and integrated into business applications is an essential component of the EDM process.

Enterprise data management as an essential business requirement has emerged as a priority for many organizations. The objective is confidence and trust in data as the glue that holds business strategy together.

Enterprise Data Management Use Cases

Effective Enterprise Data Management aids organizations in the transfer of data to varying applications, processes, and partners with success, confidence, and ease. This streamlines all processes and improves operational efficiency and effectiveness.

Enterprise Data Management delivers another crucial benefit by reducing the internal time spent regulating new data. Effective Enterprise Data Management helps to manage and organize the constant changes and fluctuations in assets, which bolsters overall trust in an organization’s content and policies.

By defining, incorporating, and storing data in one organized, easily accessible system, Enterprise Data Management helps companies:

• Store, find, analyze, and use their data
• Operate from a data-driven, analytical perspective
• Make informed decisions
• Plan for the future
• Streamline processes
• Function effectively and efficiently
• Cultivate and lock in trust of assets

Enterprise data management ensures accurate, accessible data is stored in a standardized, secure way, and is absolutely essential for organizations to succeed.

Steps to Creating an Enterprise Data Management Strategy

1. Assess the Current Situation: Often, everything starts with assessing your current data practices, capabilities, and weak spots. Here, your IT team will need to get a clear understanding of how data flows through your organization, and what sources are used and which aren’t but might be beneficial. This step will set the stage for effective data management. Of course, assessing your current situation might be quite time-consuming. In particular, if you work with a lot of digital information and are only getting started with systemizing your approach to it. Nonetheless, don’t rush this step, as it will ensure that the final result truly caters to your unique needs and challenges.
2. Define Needs and Objectives: After performing the assessment of your initial requirements, it is imperative to note them down along with the final objectives you’re hoping to achieve. This will help keep the team on track and also allow you to quickly determine the success of implementing an EDM strategy. So, make sure you answer the following questions:
   • What are the end goals?
   • Are there any pressing issues that should be prioritized?
   • What kind of analytics do you want to run?
   • Which data is needed and where will it be stored?
   • Who will be responsible for ensuring the completion of this undertaking?
   • Are there challenges you already foresee?
   • What are the KPIs which will help measure success?
     For some organizations, the implementation of a company-wide data management strategy may take a lot of time. In that case, stick to the agile approach and define incremental deliverables and goals. In the end, they will all add up and leave you with a success-ensuring plan.
3. Identify the Needed Tools: Once you are clear on the objectives you are looking to reach, it’s time to identify the software that will help you achieve the set-out goals. Here, make sure you take your time to think over what kind of hardware or software you need to establish a strong data infrastructure. Perhaps, you already have some of the required solutions, and they need minor tweaking or modernization. Or, you might just be working with a few solutions, like a CRM, ERP, and CMS, and are ready to take it up a notch by centralizing everything with a data warehouse. Whatever the case might be, you need to determine if any solutions are missing from your tech stack and decide how you will acquire them. Do you want a custom-built tool, or will you go for an out-of-the-box one? If it’s the former, do you prefer outsourcing development to an external vendor or handing the project to your internal IT team, if you have one? These are the questions you’ve got to consider at this stage.
4. Implement Data Management Solutions: Finally, you can implement the tools you’ve identified in the previous step and start capitalizing on the benefits they bring. At this stage, if you’ve chosen the custom route, your team will focus on the solution’s UI/UX design, backend, and frontend development, and testing. After this part of the process is complete, don’t forget that any software needs to be monitored and maintained. Otherwise, it might quickly become outdated and deliver poor results. So, don’t forget to ensure you’ve got a team keeping track of its performance.
5. Establish Data Governance Policies: Once everything is in place, or even while the needed solutions are being implemented, you can begin establishing your data governance policies. We’ve already touched upon this crucial component of an enterprise data management strategy above, but it’s worth reiterating. Essentially, determine the necessary standards, policies, and procedures that will be followed by the entire organization to prevent security breaches, data corruption, and loss. Moreover, don’t forget to account for regulatory compliance and include the necessary guidelines in your documentation. This is particularly relevant in highly regulated sectors like finance and healthcare. Your EDM software will likely already be compliant if you’re working with experienced development specialists. However, it’s still crucial for your policies to also incorporate proper data usage procedures. Overall, just focus on outlining how you ensure data quality, security, privacy, and transparency in the processing of digital information.
6. Train Your Employees: Lastly, after you’ve revamped your data management processes and established a thorough strategy, it’s essential to conduct some employee training. Without this step, all of your hard work could be for nothing. Consider setting up a company-wide education program that will train the relevant staff on any new software and explain why certain changes were enacted. Most importantly, equip the team with the knowledge they need to continuously reach your enterprise data management goals.

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.

Benefits of Enterprise Data Management

Having made enterprise data management a top priority, organizations ensure that the data is stored in a secure place and is made available to users whenever they need it. By ensuring the following protocols are taken care of, enterprise data management brings about significant benefits:

• Getting access to data of high quality for performing analysis
• Making sure that the data is compliant with standard guidelines and regulations
• Consolidation of data from multiple sources for enhanced efficiency
• Ensuring a stable and consistent data architecture that can scale up with the organization

Moreover, the task of data analysis and other relevant activities can be performed more efficiently because people know exactly where to find the data they need. Additionally, if the data is well organized and stored data can help organizations to identify any sort of discrepancies in the data and the accessibility of data among the user group. This can help data managers to define the right table structures and make those easily accessible to users.

See Also

Data Architecture