Data has always been a vital part of business success. As the world graduates toward digital information, strategy is becoming more important than ever. As businesses increasingly rely on immaterial assets to create value, Data Management continues to grow in its usefulness and necessity for all businesses.
This article will explore the following ins and outs of Data Management:
- What is Data Management?
- What is Master Data Management (MDM)?
- What is Metadata Management?
- Data Lifecycle Management Definition
- The 3 Main Goals of Data Lifecycle Management
- Data Management Best Practices
- Data Management vs. Data Security
What is Data Management?
Data Management is defined as the practice of collecting, keeping, and using data securely, efficiently, and cost-effectively. Ultimately, it is just as the name suggests. Data Management is managing data most effectively. This effective management includes everything from the setup to the daily operations of your business intelligence.
Of course, there are multiple ways to manage your data successfully, depending on your business, the amount of data you are managing, and the purpose of the data.
Nevertheless, there is always a common goal of Data Management: To help people, organizations, and connected things (IoT) optimize the use of data. This optimization should always take place within the bounds of policy and regulation. That way, anyone using that data to make decisions can maximize the benefit to the organization they represent without fear of repercussions or unintended wrongdoing.
Unfortunately, being in control of data can be a privacy nightmare, even as a business owner. Your customers trust you with this information, and so, it is your responsibility to keep that information safe and secure. However, you need access to that information to work for you and the company as a means of big data.
To walk this fine line as a business owner, or IT professional, you need to implement robust, practical, and reliable Data Management tactics.
What is Master Data Management (MDM)?
Master data management (MDM) is a technology system that allows business intelligence and information technology to work cohesively to ensure uniformity and reliability throughout the big data system.
MDM organizes multiple data sets, ensures that the big picture emerges, and provides the most accurate version of the cumulative data.
When Do You Need MDM?
Businesses usually do not need MDM unless they hold more than one copy of data about a business entity. For instance, if there are multiple accounts of the same data, especially of a business entity, an MDM will ensure that the information coming from the business entity is universal.
Without using an MDM in this situation, the inconsistencies could cause inefficiencies in operational data sets. Such inefficiencies will hinder the business’s ability to report and analyze, ultimately rendering all collected data useless.
What is Metadata Management?
Metadata management is managing metadata for big data or content data. Metadata is a term often used by marketers concerning SEO (Search Engine Optimization).
Social media marketers use metadata (or tags) to define content data, pictures, articles, and videos to search engines (like Google).
Although Metadata Management usually deals with a database within a company or entity, the use of Metadata Management is comparable. Metadata Management uses metadata to organize and define different data sets within the data management systems. With these metadata markers, users can find the information they seek quickly and seamlessly match similar data sets.
For more information, refer to our complete Metadata Management guide.
Data Lifecycle Management Definition
Data Lifecycle Management (DLM) is defined as the process, policies, and procedures of managing business data within an organization throughout its life. This concept means the purpose of DLM is to stay with the data it gets assigned from the moment of creation through retirement.
However, this lifecycle method is not a product but an approach to managing an organization’s data.
The 3 Main Goals of Data Lifecycle Management
DLM has three main goals to complete before it finishes its management cycle:
Data Security and Confidentiality
Throughout the data lifecycle, it is inherently important that the method remains secure. Having good security and confidentiality practices will enable you to use the data as you see fit, without any issues arising.
Here are some security tips for helping your data management run smoothly:
- Always Audit and Log Data Access
- Only Store the Data You need
- Encrypt Data at Rest and In-Transit
- Know Where You Put Your Sensitive Data
- Keep Your Network Configuration Fresh
Availability at All Times
If your data system has severe downtime issues, it doesn’t matter how secure or comprehensive it is; the system is useless. Therefore, you need to ensure the system is live and available at all times.
If an authorized person needs information from your data system, that information should always be available. The business world never sleeps. So, your big data access shouldn’t sleep either.
Long-Term Structural Integrity
Finally, the third goal of DLM is maintaining the system’s structural integrity long-term.
During the creation of a business, the owner often creates something to outlast them. The same is true for the method of enterprise data management. While individual pieces of data may become obsolete, your data management system should endure, bringing new data in and helping your business evolve with the times.
A lot is changing in the business world. It seems like businesses need to make another major chance to survive every day. So your DLM system needs to prove its worth by persevering and adapting to keep your data relevant and useful.
Although, as you can see, these goals don’t get based on time. Instead, they get founded on the structure, reliability, and robust nature of the DLM approach. This lifecycle is indeterminate, success-driven, and strict.
Fortunately, the beauty of such an approach is that it has proven to be an accurate measurement of successful Data Management.
Data Management Best Practices
Ultimately, Data Management is only as good as the raw data and the original method of taxonomy applied to that data.
So, to give your Data Management system the best shot at success, follow these best management practices:
Provide Strong and Consistent Naming Stipulations
The naming devices followed throughout your data-saving methods are the foundation of your Data Management system. Follow strong, consistent, and memorable naming conventions throughout your company, and you give yourself a boost in the quality of your Data Management.
Yet, if you fail to implement a strong naming stipulation, you are putting yourself at a major disadvantage before you even start.
Plan Your Data Storage Carefully
The place you choose to store your data automatically becomes the home for everything you build. Whether your company should keep your data inside your facility or through a cloud-based system doesn’t matter. The only aspects of storage that matter are security, safety, and reliability.
When deciding the right data storage approach, think about which option will work best with the three DLM goals. The answer should help you narrow down your options significantly.
Keep Metadata Descriptions in Mind
Metadata should contain information about the data’s content, structure, and permissions. The reason this description is important is that the data remains easily discoverable.
Much like naming your data, you need to have a reliable system through which you and others importing data can maintain consistency.
Here are some suggestions on catalog items to use to create your metadata:
- Data author
- What the data set contains
- Descriptions of fields
- When/Where you created the data
- Why was this data created
- How you create the data
Data Management vs. Data Security
Data Management and Data Security are both related, as you can’t have successful Data Management without successful Data Security. Although the two entities complement one another in their functions, they serve different purposes.
Here is a breakdown separating Data Management and Data Security:
Data Security Explained
Data Security is the practice of keeping the data and the users safe and secure. Security should be the primary concern for any company, but a lack of Data Security can create a dangerous vulnerability for businesses and customers.
Data Management focuses on using that secured data to the best of the company’s abilities, with the intention of discovery and innovation.
For more information about data security, refer to our Data Security Guide.
In closing, Data Management is an ideal practice for companies that want to use their data to the fullest. So, that should be every company, as data is of no use without proper management, delegation, and service.
Satori, The DataSecOps platform, provides capabilities such as continuous sensitive data discovery (including integration with data catalogs). Satori also builds a continuously updated data inventory as data is being accessed. To read about what Satori does, read about some of our key capabilities:
- Fine-Grained Access Control
- Dynamic Data Masking
- Decentralized Data Access Workflows
- Data Access Auditing & Monitoring
- Continuous Data Discovery & Classification
To learn more about Satori, go here.