How could organizations function efficiently in today’s data-driven world? And the answer is efficient data management. According to 8allocate.com, while 71% of business leaders acknowledge the value of data, only 19% have robust data management strategies in place. The wide disparity between these two numbers underlines an important issue: even though many companies believe in the benefits of data, few are very active in using it effectively.

Noteworthy is that effective data management is not only a best practice but also a matter of survival. 89% of professionals agree that effective data management will be increasingly important to ensure the long-term success of the business. As companies keep making decisions based on data, a good data management process can strongly affect their chances of thriving and adapting in a fast-changing environment.

What is Data Management?

Data management is a process related to storing, utilizing, and accessing data in a very secure, efficient, and economical way. It includes the creation and enforcement of policies that guarantee the accuracy, availability, and protection of data throughout its life. The goal of data management is to enable people and organizations to make the best use of data possible within policy and regulatory constraints to make better decisions and improve operational efficiency.

Today’s digital economy has made data as liquid as financial capital. Good data management practices enable organizations to utilize vast amounts of data. 80% of organizations that have implemented real-time analytics have witnessed revenue increases, which shows how huge the advantages of a well-engineered data management process could be.

Key Practices for Efficient Data Management

Data Management best practices are critical for organizations, as the amount of data collected is huge and must be maintained for decision-making. Poorly maintained or mishandled data can be a lost resource or result in operational bottlenecks, process breakdowns, and excessive risk exposure. Therefore, a sound data management process should be a strategy for every enterprise. Let us look at some important data management practices that you can adopt.

1. Build Strong File Naming Conventions 

The foundation for efficient data management begins with the ability to find it easily. You will require strong file naming and cataloguing conventions in order to find the data easily to utilize it on time. Imagine you forgot the name of the file, and in such cases, no advanced and robust software can help you out.

Hence, to start an efficient data management process, it is best to create a reporting or file system that is user-friendly and has descriptive and standardized file names. This makes it easy to find and format files and allows users to discover datasets easily, keeping long-term access in mind.

For instance, you can list dates using a standard format, either YYYY/MM/DD or DD/MM/YYYY. For mentioning time, it is best to go with Unix timestamp or standardized 24-hour notation, like HH:MM:SS.

2. Create a Data Governance Framework

Effective data management is built on a strong data governance framework. This framework should specify data ownership, stewardship, quality standards, and access restrictions. It should also establish roles, responsibilities, and procedures for data management inside your company. It involves:

• Define your team members’ roles and responsibilities. This might include assigning the data owner overall responsibility for data assets and data custodians who oversee daily data management.
• The data governance framework should consist of creating and implementing data policies and standards. The policies address topics including data security, privacy, quality, and retention and specify how information should be gathered, kept, accessed, and utilized inside the company. The standards should address data formats, naming conventions, and metadata.
• The framework must define the workflows and activities of data governance. This includes data profiling, data quality assessment, data categorization, data access requests, data issue resolution, and the documentation that must be defined.

3. Consider Metadata for Datasets

Metadata is nothing but descriptive information about the datasets that you are using. So, metadata should contain information about the data’s content, structure, and permissions, so it becomes easy to discover in the future and to remember the data as well. Having the metadata information sorted out helps search the dataset easily, and you can depend on being able to discover it even after several years down the line.

If you are wondering how you can catalog items, there here are some categories that you can use:

• Data author 
• What data does this set contain?
• Fields description 
• When/where the data was collected?
• Why was this data created, and how?

Storing and cataloging your data according to the above fields will help create and understand data lineage as the data flows, from tracking its origin to reaching the destination. It becomes super simplified in situations when data mapping and documenting data relationships is required. Metadata that highlights a secure data lineage is another important step in building a robust data governance process.

4. Prioritize Data Security

When you are focused on committing to data quality, you will have to invest your time and resources in creating a secure environment with strong security practices. To start with this, first of all, ensure that your organization’s network is strong and that the employees are well aware of basic cybersecurity best practices and the role their awareness plays in preventing cyberattacks.

An organization is always one step away from falling into the trap of cyberattack. Hence, it is best to keep the security walls up. You should establish appropriate measures to defend the data’s environment against cyber threats and maintain a clean data environment. 

Keep updating software to get the latest security patches and updates, install anti-malware protection, and install threat detection solutions. Invest in access management. Encrypt your data and train staff in cybersecurity best practices to further secure your data management strategy. Make an incident response for quick action if a threat does arise. 

5. Streamline Data Integrations

Data can be stored in different silos in a company, making it difficult to access and analyze. Automation and integration technologies can assist in simplifying the procedure by allowing data to move across systems without interruption and removing the need for human data entry or data translation. 

You can organize data from several sources into a commonly used format or platform by implementing data integration solutions. It offers a single perspective for analysis, reporting, and decision-making. Data integration:

• Improves data accuracy and consistency
• Speeding up data access
• Complies with the established standards as it is transformed and cleansed in the process
• Enhances data quality with the data validation test

Effective data management helps the organization build a secure and reliable system, understand your valuable customers, evaluate customer acquisition channels and help your business scale. 

Some Other Tips for Efficient Data Management Process

Here are some other tips that you should keep in mind to streamline data management and improve its efficiency:

• Consider a variety of storage solutions, like desktops/laptops, networked drives, external hard drives, cloud storage, and optical storage. 
• Utilize the 3-2-1 methodology for storing the data. This is how it works: store three copies of your data using two storage methods with one of them being stored off-site. 
• Introduce multiple documentation levels, like project-level, file-level, software used, and context, to provide a full idea of why the dataset was created and how it can be used. 
• Set up a team which standardizes the data management process and trains the team to adhere to the rules and regulations to ensure strong data collaboration across the organization. 
• Along with best data management practices, invest in high-quality data management software. The software comes with end-to-end capabilities and features to streamline the data management process, from creating the right datasets to data extract scheduling that works for your business needs. 

The Bottom Line

Efficiency in data management acts as a guide to navigate the maze of the data-driven environment. The data management process is all about focusing on retaining data quality, consistency, accuracy, and reliability. So, preserving its key features—validity, accuracy, completeness, timelessness, integrity, and consistency—is a part of the process. 

Maintaining the key features of data includes various steps, including data standardization, profiling, cleansing, validation, conducting root cause analysis, and reporting. That’s why you should invest in hiring a professional who can assist with an efficient data management process, and Hyqoo can help you hire one or an entire team within 2-3 days. Connect with our experts today!