What is Data Management, and Why Does Your Organization Need it? – Challenges and Best Practices

Does your company manage data effectively? A good data management framework helps businesses avoid data breaches and inefficient operational tasks, which are costly mistakes. 

Continue reading to learn what data management is and the ideal framework to follow to organize your data well. In the end, you’ll also find the best practices to enable effective data management strategies within your organization. 

What is Data Management? 

Data management involves gathering, storing, processing, and accessing data in a structured way. The ultimate goal is to ensure that data is available accurately, reliably, and consistently. As part of effective data management, data lineage ensures that organizations can trace the origin and flow of data, providing insight into its transformations and ensuring quality.

In other words, data management enhances how responsibly and efficiently data is utilized in an organization. For example, a company stores employee details in an organized way, such as in a file system or in a particular database schema, to easily access whenever required. 

Types of Data Management 

• Relational data management: organizes data into rows and columns, forming a table. For complex data, it creates multiple tables and builds relationships among them. Most popular databases, such as MySQL, Oracle database, and SQL Server, manage data this way.
• NoSQL data management: stores and manages semi-structured or unstructured data. This type offers flexible schemas that can process any storage structure for building modern applications. 
• Object-oriented data management: stores data as objects that contain data itself, its properties, and methods that can be performed on it. 
• Distributed data management: Data is stored, processed, and accessed across multiple servers. This enables parallel processing, improving the speed and performance of various operations on data.  
• Data warehouse management: This centralizes the current and historical data from multiple sources for easier access, simplifying the extraction of insights and reporting. 
• Master data management: This approach creates a unified master data service that consolidates data from different domains. This data is shared across multiple departments and systems, making it accessible to them at any time. 

Importance of Data Management

According to Statista, the data has grown enormously and is expected to grow exponentially. With such massive data being used, we should ensure it is well-managed to expect intuitive results from it. Here are a few benefits of implementing an effective data management strategy. 

1. Removes redundancy

Businesses often store the same data in different forms at multiple locations. For example, sales data might be stored in both finance and marketing systems. This possibility of having the same data across systems increases data redundancy. 

An effective data management strategy removes redundancy by providing consistent and accurate data access for users.

2. Maintain data integrity

Data management contains validation rules that constantly verify data accuracy and consistency to promote trustworthiness. By regular checks and cleansing of data in data management, data integrity is maintained throughout the lifecycle. This enhances decision-making and reporting capabilities within the company. 

Moreover, data integrity maintained by data management gives you confidence that any data you hold and utilize is safe and in compliance with regulatory rules.

3. Simpler data sharing

Data silos means data is maintained by one domain and not fully accessible to other domains within an organization. Data silos are such a big problem that it causes employees to lose 12 hours a week chasing data. 

Therefore, efficient data sharing allows multiple users to access and share information. Data management policies make this possible within your organization, fostering collaboration. 

For example, a data catalog with a metadata management layer simplifies data discovery, and stakeholders can access that particular dataset and share it with the team seamlessly. 

Moreover, the privacy control framework of data management also ensures the security of shared data. That is, authentication is required to access shared data, eliminating the risk of data breaches. 

4. Backup and recovery

Data management includes the process of backup so that data can be easily recovered in case of accidental data loss or data breaches due to malware. 

A good data management framework creates a copy of your data, either locally or in the cloud, depending on your company’s budget and requirements. Whenever your main data is lost for any reason, you can get back to this copy and restore it. 

Challenges in Data Management

• Massive data storage: Large enterprises likely have to deal with enormous amounts of data from sales, customers, marketing, production, and more. Therefore, it is challenging for them to maintain such huge amounts of data consistently, accurately, timely, and with high quality. 
• Duplicate data: Sometimes, different departments store the same data in multiple forms. For example, the accounting team might refer to the buyer as a “client,” while the contact center team calls them a “customer,” even though both refer to the same person. This redundancy leads to duplicate data and higher storage costs.
• Lack of data governance: Data governance is the set of policies and standards an organization’s data should follow to ensure safe, accurate, and only useful information is retained. An inefficient data governance framework can lead to critical issues like uncertainty in data definitions, non-compliance with legal regulations, and a lack of accountability. Using a structured Data Governance Checklist can help organizations to overcome these issues.
• Data security: Confidential and financial data often need to be given extra care to keep them safe from hackers’ hands. With improper security measures, the sensitive data is prone to unauthorized access and breaches that can cause financial loss or reputational damage to your company. Thus, effective data management with robust security controls should be in place. 

Best Practices to Implement Effective Data Management Strategies

• Set data quality standards: Maintaining high-quality data is essential to maximize its value. Without defining what quality means to your organization, you’ll never know how certain your data is and can’t establish trustworthiness. So, set clear data quality standards for your company and ensure your data adheres to them.
• Design scalable data architecture: Your company’s data architecture should be able to accommodate the exponential growth in volumes of data and changing market dynamics. That’s why you should implement a scalable data management framework that is flexible to changes.
• Regular audits: Regularly revisiting and updating data management practices ensures the measures are relevant to evolving business objectives, changing customer demands, and technological advancements. Therefore, continuous testing and frequent audit checks should be implemented to keep data management strategies updated. 
• Training and development: The workforce that handles your data should be well-equipped with the skills required to implement data management practices effectively. For this, management should ensure the employees are provided with the right training and resources.

Conclusion 

Data is an organization’s most valuable asset if managed properly. It can enhance business decisions, discover loopholes, and improve operational efficiency. Therefore, organizations should ensure their data management practices are efficient and up-to-date. 

To derive value from data, the workforce should have sufficient data management skills and training to maintain consistent and accurate data throughout the lifecycle. Efficient data management also plays a crucial role in reducing company costs by eliminating redundancy and siloed systems. 

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