According to a study by Gartner, poor data quality is the main cause of 40% of all business initiatives that fail to achieve their targeted benefits.
A survey conducted by Forrester found that 77% of companies struggle to achieve their data-related objectives due to a lack of understanding of their data or a lack of governance processes.
Likewise, there are more reasons that make it mandatory to manage data effectively. To overcome these problems, you should understand different types of data and how to manage them. This blog is specifically dedicated to various types of data management. These managements can be associated with a database, file, document, master data, data warehouse, governance, and metadata.
So here, we’re going to discuss how these management practices work and help.
Let’s get started to know what several types of data management practices are.
1. Database Management
A database is a set of data in abundance, which is kept in a structured format in a centralized location. There can be a large number of data there that need a long time to manage. For addressing the problem of long turnaround time, here are some tips:
• Determine what data you need to store and organize in the database, and how they will be used.
• Now, select the best-fit database management system (DBMS) that you find absolutely appropriate to meet your organization’s needs. Some of these can be MySQL, Microsoft SQL Server, Oracle, and PostgreSQL.
• Then, you need to define the structure of that database. So, create a schema that reveals the structure and relationships between tables in the database. It will enable you to organize it in a logical and efficient manner.
• You may use the DBMS or software to create the database and tables based on the schema design.
• Now that you have a structure, add data to the tables in the database. You can do it either manually or through automated data import processes.
• There should be an access control system. So, ensure to have it so that only authorized users can access the database and data.
• Once defined the authority, you should regularly monitor and maintain the database to ensure it runs smoothly and efficiently. This can include tasks such as backing up data, optimizing queries, and performing routine maintenance tasks.
This could be done later when the entire database management system is aligned and smoothly working. Understand the changes in your organization’s needs. Then, you may need to upgrade the database to add new features or capabilities.
2. File Management
This type of data management involves organizing files and folders on a computer or network to ensure that data can be easily accessed and stored in an organized manner.
Here are some simple steps to manage your files effectively:
• You should start with understanding what the files are for, and how they will be used.
• Then, create a logical folder structure that makes it easy to find and access files.
• Labeling is essential now. So, you should use descriptive names that accurately reflect the contents of the file, and make it easy to search for and identify files.
• Now, you have a file management system. Group all related files together in a single folder, or you may use tags or labels to categorize files.
• Then, you should regularly review and delete files that are no longer needed, to free up space and reduce clutter.
• There may be chances that you frequently update files. In this case, use a file versioning system to keep track of changes and ensure you always have access to previous versions if needed.
• Thereafter, create regular backups of important files to protect against data loss because hardware failure, malware, or other issues are observed commonly.
• Then, you need to secure the sensitivity of the files that contain confidential information. For them, use appropriate security measures such as encryption or password protection to ensure they are protected from unauthorized access.
3. Document Management
As the name suggests, it is related to the document management of the organization, which can be concerned with the storage, and retrieval of documents in a secure and efficient way.
Discover how you can manage important documents online effectively:
• Select such document management system (DMS) that meets your organization’s needs. For this, you have some popular options like Google Drive, Microsoft OneDrive, Dropbox, and Box.
• Now comes defining accessibility. So, you need to create a logical folder structure that makes it easy to discover and access documents.
• To recognize each folder easily, use descriptive names so that you can accurately understand the contents of the document, and hence, can search and identify documents in no time.
• As in the previous section, set up access controls to ensure that only authorized users can access folders if they have the appropriate level of access.
• Herein again, ensure that you keep an up version of folders. It helps in keeping track of changes and ensures you always have access to previous versions if needed.
• It’s all about sharing folders via collaboration features. Allow multiple users to work on the same document simultaneously, and also track changes made by different users.
• To speed up the management process, use automation tools. They can help in streamlining document workflows, such as automatically routing documents to specific users for approval or review.
• Being regular with their backup is a necessity. So, keep backups of important folders to protect against data loss in case of hardware failure, malware, or other issues.
• Disallow hackers to violate the privacy or confidentiality of your data by taking appropriate security measures such as encryption or password protection.
4. Data Warehousing
It’s a broader term that involves the use of large, centralized data repositories that can be accessed by multiple users for analytics and reporting. Managing it would be a daunting task. These tips can help you a lot:
• To start with, try to understand what business problems the data warehouse is intended to solve, and what data will be needed to support those requirements.
• Create a schema or structure for your data that defines relational databases or tables in the data warehouse. Here also, you can discover how the data will be loaded and transformed.
• Then, select a data warehousing platform resonating with your organization’s needs. You have options like Microsoft Azure SQL Data Warehouse, and Google BigQuery. So, select wisely.
• This is for handling a large volume of data in a warehouse. You can integrate tools and processes to load data into the data warehouse, either through batch processing or real-time streaming.
• This phase is dedicated to cleansing and transforming data as needed to ensure data quality and consistency. It provides complementary and relevant databases.
• Now comes the monitoring. Regularly monitor the data warehouse performance, including query performance, data loading times, and storage usage.
• If monitoring is going on well, turn to optimize your data warehouse for smooth performance. It will later help in analyzing query patterns, indexing data, and partitioning tables.
• The last tip will be concerned with the security and backup of your data. Don’t compromise with these two things if you consistently deal with a data warehouse.
5. Master Data Management
Under this type of data management, you need to manage and maintain a single, consistent view of important data entities such as customers, products, or suppliers, across multiple systems and applications.
• Take into account that master data is the core data, which is shared across different applications and business processes, such as customer data, product data, and supplier data. So, you should be extra conscious or alert when you share or manage your databases.
• Identify the master data entities that are significant for the business, such as customers, products, suppliers, and employees.
• Define the attributes for each master data entity, such as name, address, contact information, and account information.
• Clearly establish data governance policies for managing master data, including data quality, security, and compliance requirements.
• Now, you need to assign data owners and caretakers the responsibility to manage specific master data entities, ensuring compliance with data governance policies.
• Then, develop procedures for inputting and maintaining master data, including data entry forms, validation rules, and approval workflows.
• In the end, you should select processes and tools to ensure data quality, including data profiling, data cleansing, and data validation.
• Before monitoring, you need to ssystems, to ensure consistency and accuracy of data.
• Obviously, the last tip is associated with the tracking of your master data quality: Regularly monitor the quality of master data to ensure that it is accurate, complete, and up-to-date.
6. Metadata Management
This type of data is related to micro details, such as data location, structure, and meaning, to help users to better understand and use the data.
With these suggestions, you can manage your metadata effectively:
• You should start with establishing metadata standards that define the types of metadata that will be collected, the format in which metadata will be stored, and the procedures for maintaining metadata.
• Once defined the standards, create a centralized repository for storing metadata, such as a metadata management tool or database.
• Document metadata for all data assets, including data sources, data transformations, data mappings, and data quality rules.
• Basically, metadata is used to track the lineage of data, which includes its origin, transformations, and consumption. So, to ensure data accuracy and consistency, you need to create this lineage.
• Create tags to implement search capabilities for metadata so that users can find and access data assets quickly and easily.
• Come up with well-defined procedures for managing changes to metadata, including metadata updates, deletions, and additions.
• Regularly monitor the quality of metadata to ensure that metadata is accurate, complete, and up-to-date.
Data management involves organizing, storing, protecting, and maintaining data. There are various types of data management, including database management, file management, document management, data warehouse management, and metadata management. End to end data management are helpful in analytics, whichinvolves establishing policies and procedures for managing data, ensuring data quality and security, and complying with legal and regulatory requirements. Managing master data is also important for ensuring consistency and accuracy of core data across the organization.