The client is a leading manufacturer and distributor of high-quality construction equipment based out of Europe. The company is well-known for delivering high-quality construction equipment and services all over the globe. In today’s complex business scenario, establishing a data governance program may seem like an uphill task even for well-established companies. The client, a multinational conglomerate in the industrial and manufacturing sector was no exception. With business units spread across Europe, the client faced several master data management issues that left a measurable impact on their business operations.
What is Metadata Management?
Metadata is like a golden thread that interconnects different functions of an organization. It comprises heterogeneous, heavily distributed and hybrid mixes of data from cloud systems. Metadata management deals with the management of complex data through analytics, operations, discovery, collaboration, and compliance. Metadata management acts as a key enabler in the data-driven decision-making process of a business. For any organization, all the data-driven actions pass through metadata when the customers browse through the website, run a query or make a virtual view.
Businesses need to understand and analyze their data to survive and grow. Metadata management can provide the required processes to gain this insight and succeed. Want to know how? Get in touch with our experts.
Why is Metadata Management Important?
With the growing volume of data, metadata management has gained huge importance. Although it sounds a little geeky, it is a crucial component of records management and data governance. Here are a few reasons why metadata management is important:
Reason #1: Increases the longevity of datasets
The lifespan of a particular dataset can be very short due to the unavailability of relevant metadata. The growing complexity of data with the increasing constraints of time and funding also contributes to the shorter life span of the datasets. But metadata management can counteract this tendency of data degradation efficiently.
Reason #2: Facilitates data reuse and data sharing
Metadata management ensures that the complex and complicated data is easily analyzed, interpreted and shared. It provides detailed instructions to interpret unfamiliar data accurately and helps in data integration.
Reason #3: Helps in maintaining historical records of long-term datasets
The datasets may encounter inconsistency in methods and instrumentation during the ongoing long-term projects. Metadata management helps in maintaining the historical records of such long-term datasets, thereby assisting in data governance. Furthermore, metadata management is important for data integration that contains different parameters but shares common temporal or spatial domains.
Reason #4: Assists in improving interoperability and integrating resources
Metadata management makes it easy for humans as well as machines to understand the different resources. Also, it facilitates the understanding of how data is exchanged among many systems with different data structures, operating systems, and interfaces.
Reason #5: Protects resources and their future accessibility
The digital information today has become very fragile and is susceptible to corruption. Metadata management helps in archiving and preserving such information by tracking the lineage of the information and describing its characteristics. Also, it helps in understanding the behavior of the information to prevent its replication in the future.
Steps to Effective Metadata Management
Step #1: Meta repository creation
There are several repository tools available in the market that profess to create metadata repository. But all such tools may not suit your business. Most of these tools focus on one specific aspect of metadata. Therefore, you need to collect a set of metadata about each element of your business and relevant to your organization in the following six categories:
- Business metadata
- Core and technical metadata
- Data quality metadata
- Search metadata
- People metadata
Step #2: Identify core attributes
Develop key business queries to get a fair idea about the underlying attributes which is necessary for analysis. It is important to identify and understand such attributes as they play a crucial role in downstream analytics.
Step #3: Acquire the metadata
Once your foundational capabilities are established, the next step that comes is implementing the meta acquisition process. Most of the categories of metadata need proper processes for acquiring it from the people who work in various functions of the organization.
A properly built metadata management model has the potential to give a 360-degree view of how operating systems in your organization are connected together. Such models can help users to solve issues based on different attribute types and names. Request a free demo to know more about how metadata management models can help in creating custom metadata types.
Step #4: Create a protocol and maintain consistency
Data keeps on changing constantly with the growth of new business initiatives every day. Such changes result in long-term data instability. Therefore, it is necessary to create a more efficient process to monitor data streams for change. This is the most difficult but important step in establishing an effective metadata management process.
Metadata Management Vs Master data Management
Metadata management talks about the content in the data that is gathered and helps the organizations in uncovering the facts hooked to their data. Through metadata management, users can decode the data in terms of what it represents and derived. Metadata management consists of all the entities and attributes, programs, calculations, reports etc. The nature of metadata depends on what it talks about.
Master data management talks about the data that consists of entities like products, services, people, organizations etc. It usually talks about non-transactional data which usually covers:
- Internal entities such as employees, departments, and products
- External entities such as suppliers and customers
- Information of organization such as reporting hierarchies and the chart of accounts.
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Today, it’s the successful use, understanding, and strategy of a company’s data that defines the success of businesses. Comprehending your data and identifying ways to implement it brings up a whole range of questions, both from the stakeholders and users. Such questions include: What is the procedure for storing data? How do we know it’s timely and accurate? Can we rely on it? Data management and data governance can efficiently answer these questions. Though both these terms are used interchangeably, they are entirely different programs. In this article, we aim at clearing all doubts you may have about data management and data governance.
What is Data Management?
Best seen as an IT program, the goal of data management is to control and organize the resources of your data so that it is reliable, accessible, and timely whenever users call on it. The IT team in your organization who is responsible for data management may trust on a customized, comprehensive collection of practices, processes, theories, and systems that gather, validate, organize, store, protect, process, and maintain data. If the data is not treated properly, it can become useless or corrupt. Data management encloses the entire lifecycle of a data asset. Data management comprises several related categories and fields and includes the following:
- Data architecture
- Data governance
- Data warehousing
- Data quality management
- Metadata management
- Business intelligence and analytics
- Data security management
What is Data Governance?
If data management is the logistics of data, then data governance can be considered to be a strategy to study them in a structured manner. Data governance is bigger and more holistic than data management as it is a crucial business program. The goal of data governance is to find tangible answers to how a company can prioritize and determine the financial benefits of data while mitigating the risks of poor data in the business. Data governance helps in determining the use of data in specific scenarios.
To ensure the reliability, safety, and trustworthiness of all data, data governance requires the involvement of all stakeholders in the business. Determining your data governance plan includes various practices, processes, and theories. It is likely to overlap with several areas of data like compliance, security, usability, privacy, and integration. The ultimate goal is to determine a comprehensive way to control data assets so that the company can get the absolute and most desired value from the data.
Benefits of Data Governance
- It increases the value of your company’s data
- It helps in cutting costs within other subsets of data management and help in focusing on important areas to work on.
- It assists in boosting the overall revenue of the enterprise
- It aids in standardizing data systems, procedures and policies.
- It ensures proper regulation and compliance procedures.
- It promotes transparency and security
Want to know how data management or data governance can help you curate, process, and analyze the huge data sets of your business?
What is data management?
Data management refers to the systematic organization and maintenance of data processes. It is concerned with the implementation of policies and procedures that puts organizations in control of their business data. Data management tools are used to minimize the risks and costs of legal complications, regulatory non-compliance, and security breaches. It also provides access to accurate data whenever required, without ambiguity or conflict, consequently avoiding miscommunication.
Data management best practices
Effective data management is all about solving business issues and improving data trustworthiness through the effective and seamless integration of information with business processes. Here are five data management best practices to follow:
Focus on data quality
In the run for attaining better data management, several companies often tend to ignore the importance of data quality. Poor quality, outdated, or inaccurate data are major barriers to managing a company’s data successfully. Hence, cleaning up data before it is used in any analytics processes is essential. Issues including inconsistent and non-standard formats to spelling error can result in poor-quality data, so having the right data management tools to identify and fix these issues are vital.
As data volumes grow, it’s likely that so too will the amount of duplicated data across the business. Particularly in environments that are transitioning from highly siloed structures to a more centralized system, redundant data takes up space and slows down processing. Therefore, one of the key data management best practices includes identifying duplicate data and deleting it without impacting any processes that rely on it. Spotting data that’s no longer relevant and removing it are also crucial data management best practices.
Prioritize data security
Data security should always be a critical element of focus during data management. Also, with the new government regulations on data security and security breach, this has become more important than ever before. Companies must also ensure that they access adequate control and encryption over the data, and the physical security of data must not be overlooked.
Enable easy access
Possessing enormous amount of data becomes invaluable to any business unless the relevant personnel is able to access it easily as and when they require it. Developing effective access controls is always a tricky balance between convenience and security. However, it is well worth taking the time to ensure that each individual has the level of access that’s appropriate for their role, rather than implementing blanket policies which are either too restrictive for some people or may allow others to access data they should not be given access to.
Formulate a recovery plan
Every company is now transforming into a data-driven business. One of the most damaging problems a firm could encounter is to lose access to their own database. Whether it is a hardware failure or power issues, or even human error, any interruption to a company’s data flow can render an organization completely unable to do business. So, any data management strategy becomes incomplete until it includes a clear backup and recovery plan that clearly indicates how often different types of data need to be backed up and what steps need to be taken to recover them in the case of any mishap.