Data is the currency of the modern world. Companies, organizations, and entire governments rely on data to function effectively. This is why all data needs to be stored securely and optimally – especially in a business. The integrity of data is a crucial element of any business model. In this blog article, we’ll be discussing the meaning and function of enterprise data management and how an enterprise data management system can elevate data quality. Let’s begin by getting a clearer definition of the term.
What Is Enterprise Data Management?
Enterprises create massive amounts of data each day that need to be properly collected and curated in a way that makes management, retrieval, and analytics easier and streamlined. A report found that the Big Data market size is expected to grow to US$ 4.102 billion by the year 2028 - a shift brought about by more organizations realizing the potential of information handling. The report also stated that mass digital transformation has pushed more industries to value real-time analytics, processing, and scalable infrastructure for data.
So, what is Enterprise Data Management (EDM)? Enterprise data management refers to a set of processes, practices, and activities that govern the storage, protection, retrieval, and general management of data for a business. The concept includes the management of your actual workforce about data storage as well – data cannot be properly stored without the correct actions by the organization itself. The basic goal of managing enterprise data is to provide accurate, organized, and useful information that can feed analytics and inform business decisions in the long run.
Generally, the inventory for Enterprise Data Management strategy consists of both internal and external company assets. The enterprise data management framework is used to regulate the large amount of data produced in a standardized and credible manner to ensure reliable business insights later on. We’ll now explore the core elements of enterprise data management to better understand how it works.
Key Elements of Enterprise Data Management
Enterprise Data Management relies on several core features that allow businesses to store data properly. It’s the cohesion and holistic approach to each of these elements that ensures an accurate, functional, and efficient approach to an Enterprise Data Management solution. These are some of the core parts of the EDM framework:
Data Integration
This is the process of combining data from several different sources into a unified repository. This simplifies collaboration across different systems while providing actionable and valuable data in a singular step. Data integration also drastically reduces errors, reworking, and time wastage.
Data Governance
Data governance consists of a set of policies and processes that create internal and external structures of accountability to ensure that data responsibilities are assigned correctly. This data management model fosters ethical responsibility across every level - including stakeholders. An effective enterprise data governance framework example ensures regulated and factual data while protecting user privacy.
ETL (Extract, Transform, Load)
ETL – or Extract, Transform, Load – is a process used to replicate data and move it from one system to another. ETL will usually extract data from places that are not optimized for analytics and move that data to a more centralized host – such as a data warehouse.
Data Warehouses
A data warehouse can be defined as a structured, large collection of business data. This place is used to store, process, and analyze information. Data warehouses can be cloud-based or on-premises and usually keep the most recent and authentic data which can be used to gain insight and make business decisions. While the repository of the data warehouse may be similar to that of a data lake, the data warehouse will stand as a “single source of truth” by acting as a more refined archive for data. The difference between a data lake and a data warehouse is that data lakes will store data in any raw format and tend to be more unstructured.
Data Quality Management
The next data management operating model is data quality management – or DQM. These are processes that focus on ensuring the quality of the data collected by data cleansing, doing integrity checks, and ensuring data enrichment and quality assurance. The better the data quality within an organization, the smoother it will run.
Data Stewardship
Data stewardship processes are centered on supervision, execution, operationalization, and the management of data lifecycles. This ensures that relevant data is accessible, compliant, and secure at all times. This includes defining data models, model maintenance, data documentation, data cleansing, and defining policies.
Data Security
Naturally, data security is an integral part of the EDM process. This aspect includes any method of securing and protecting data from within the business. This includes protection from theft, leakage, destruction, and more. Sangfor’s XDDR platform provides holistic and coordinated data protection that will seamlessly enhance data security.
An effective enterprise data management framework will make use of all these elements comprehensively to ensure absolute data integrity. Now that we know what the EDM framework is made up of, we can go on to explore the importance of enterprise data management as a whole.
Importance of Enterprise Data Management
Essentially, enterprise data management services are used to collect, secure, and refine accurate and credible data into actionable stores for businesses. It is an essential part of creating a standardized and compliant system for the safety and usefulness of data. Here are some of the reasons why enterprise data management is important:
- Makes data accessible across all levels.
- Provides actionable information for data-driven decision-making.
- Streamlines processes.
- Improves data consistency.
- Helps businesses plan for the future based on collected data.
- Cultivates data responsibility across sectors.
- Ensures compliance and regulatory standards for data.
- Protects and upholds user privacy and confidentiality.
- Provides the basis for strategic planning.
- Reduces the time spent reconciling differences in data, processes, and storage.
- Enhances employee trust and loyalty.
- Improves management of enterprise data analytics.
Enterprise data management architecture forms the basis of a streamlined and credible organization that values its data to make informed decisions. With an effective EDM strategy in place, businesses can make statistically sound choices and use a data-driven perspective to strengthen policies and processes. Next, we can explore some examples of enterprise data management in action.
Examples of Enterprise Data Management
Enterprise data management might seem like a complex concept but the general idea is quite simple at its core. These are some real-life data framework examples that businesses can use to streamline data processing:
- Asset Management: To effectively manage data in a secure and organized way, businesses need to manage several assets. These include, but are not limited to:
- Financial information
- Photos
- Graphics
- Videos
- Inventory
- Internet of Things data
- Social media
- Mobile data
- Metadata Management: Metadata provides context and history to your data stores and helps to form a useful and structured analysis. Metadata management ensures accurate and consistent data across all levels of the business.
- Data Cataloging and Discovery: This is the creation of a repository of all available data assets within a business. Discovery and cataloging ensure that no data goes unseen or under the radar in your data control framework.
- Data Lifecycle Management: The data lifecycle management process involves managing data throughout the different phases of its lifespan. This approach prioritizes disaster recovery options and data security.
Examples of EDM options make it easier to visualize the benefits and processes of the tactic. Another key element of the Enterprise Data Management framework is Master Data Management (MDM). Let’s take a closer look at this aspect of data processing.
What Is Master Data Management (MDM)?
Master Data Management (MDM) is a process of managing, organizing, and synchronizing master data or “true” data. Enterprise Master Data Management integrates different types of data into a consistent and unified version by removing any duplicates, applying modeling rules, and aggregating reporting records. This ensures that all employees have access to a single and accurate version of data at all times. Now, let’s compare the MDM framework to what we know about EDM services.
What Is the Difference between EDM and MDM?
The Master Data Management framework is like enterprise data management in many ways. When using MDM processing, businesses are isolating a true version of data into a master record. This ensures a standardized and compliant data set across all areas. Essentially, EDM services stand for the same qualities that value quality data governance and integrity. The main distinction between the two is that EDM processing is typically suited to financial and capital markets while MDM is a more “generic” form that can be used in sectors like healthcare and manufacturing.
Financial sectors value security data such as terms and conditions, prices, corporate actions, and more. These are covered in depth using Enterprise Data Management services. However, Master Data Management can focus more on entity data – about individuals, corporations, and the like. Nonetheless, some financial services can still fall under the wing of the Master Data Management framework as well. We’ll now focus on the concept of Enterprise Data Management Software.
What Is Enterprise Data Management Software?
Enterprise Data Management Software is the application of EDM principles in a way that supports data analytics within a business. EDM software consists of tools and technologies that streamline processes, ensure data integrity, and enhance productivity. EDM software can be categorized into three types:
- Enterprise Resource Planning (ERP) to help processes across various business functions.
- Customer Relationship Management (CMS) to manage interactions with potential customers.
- Supply Chain Management (SCM) to manage the flow of goods, data, and finances.
An effective Enterprise Data Management Software will store data, improve accessibility, automate services, and establish standardized processes. It will also ensure data security and regulatory compliance while offering real-time analysis of data to inform decisions. While EDM software can be a great help for businesses to get started, it’s important to approach data integrity from all angles. Let’s review some Enterprise Data Management framework best practices that you can put in place.
Enterprise Data Management Framework Best Practices
When it comes to data safety, accuracy, and value, it’s crucial to have your entire organization involved in the process. Sharing responsibility builds a better front for data integrity and encourages collaborative effort across the company. These are some of the top Enterprise Data Management framework best practices that your business can put in place:
- Conduct an EDM Audit – Do a detailed audit to assess the effectiveness of EDM solutions and identify which areas need extra attention or more work to ensure better data processing. This also gives you a full view of the organization’s assets to provide a better starting point.
- Have a Clear Goal – Most businesses have very specific needs and targets to reach in their data processing journey. Set up a realistic strategy for achieving the goals that are most important to your company.
- Identify Data Management Leaders – Ensure that your data management process is headed by a strong and responsible leader who values your company’s goals.
- Invest in Better Data – Your EDM framework will work better with high-quality data input. This also ensures a better Return on Investment.
- Use Data Governance – Ensure the proper data governance is put in place by having strict rules and standards for your data.
- Educate Employees – Creating awareness is a crucial step in any data processing project. Try to educate your workforce on data management and share responsibility across all levels.
- Use the Correct EDM Tools – Source the best enterprise data management tools and technologies for an Enterprise Data Management platform that suits your business needs.
The enterprise data management framework prioritizes accuracy, integrity, and functionality. This is why businesses of all sizes should invest in proper Enterprise Data Management solutions to keep their workflow streamlined and their data efficient. There are several ways that EDM has changed the way we view data and several ways to improve our data management through the best practices mentioned above. Invest in a better data controls framework and future for your company with an EDM strategy that understands your needs. For information on cloud computing or cybersecurity services, contact Sangfor Technologies today and see how much your data means to us.
