Business leaders often make corporate decisions in haste, based on data that is neither thoroughly analyzed nor understood. This can pose a serious threat to data-driven decision-making. Additionally, businesses risk losing a competitive edge by not following important business analytics practices. To get the full benefit of data-centric activities, businesses must follow several core Data Management principles. 

The Data Management-first approach involves developing powerful systems that can collect, analyze, and manage the data deluge without compromising the integrity of the systems. Data Management principles collectively define the “concepts, roadmaps, controls, and best practices” surrounding the management of data. 

These principles usually govern the sound practices for organizing, storing, retrieving, and protecting data. The primary aim of Data Management is to develop an organized data system that can aid the business analytics process. This may include setting effective data standards for inter-department collaboration, achieving data goals, and designing data models.

Organizations achieve their pre-defined business goals through effective Data Management strategies, which are solidly based on the business’s core Data Management principles. The Data Management process and Data Governance play a crucial role in helping organizations manage their data and data assets. Adopting sound Data Management principles, rules, and best practices ensures the optimal use of an organization’s data. 

With recent advancements in data analytics and database technology, data-powered algorithms have been a key driver of Data Science projects. The emergence of the new AI-driven, data-first era has necessitated a deep understanding of the fundamental concepts of Data Management.

Data Management Team: Roles and Responsibilities

Data Management teams are responsible for implementing core Data Management practices within their organizations. They use reactive change-management techniques to address issues like duplicate records and demanding technology requirements for storage. Additionally, they establish monitoring protocols to ensure that all aspects of the organization work according to plan.

Perhaps the most crucial aspect of Data Management is Data Quality. A Data Governance team must be established to oversee this process, with domain stewards responsible for specific data domains. To improve Data Quality, communication of a common set of standards is necessary to ensure consistency across the organization. 

These are some core Data Management team functions:

• Prioritizing data literacy and education to help prevent mishandling by their team members
• Adapting to new practices as the situation demands
• Setting workflows for data collection, storage, and analysis
• Undertaking training on a chosen Data Management platform to get the full benefits of its capabilities
• Creating a blueprint for how projects and programs will use and analyze data throughout their lifecycle
• Putting proper policies and procedures in place to ensure that Data Management activities are consistent

Here is a cheat sheet for Data Management professionals to consult.

Key Data Management Principles to Follow

Data Management encompasses a wide range of activities, including third-party data integration, real-time data integration, streaming data integration, change data capture, data protection, data redundancy reduction, management of values structure and use, ensuring high-quality data output, and much more.

A Data Management strategy should determine how all these elements should be implemented within an organization. This blueprint provides a framework for creating projects and programs that ensure important data is collected accurately, stored securely, and used effectively. 

Key components of the Data Management process include the following (in no particular order):

• Setting up a Data Governance Council responsible for overseeing all data policies and ensuring the policies align with the organization’s goals. A comprehensive governance program helps organizations achieve business objectives by providing effective strategies and methodologies for managing data.

• Putting proper data collection policies in place to ensure the authenticity and reliability of stored data. 
• Ensuring accurate retrieval of archived data. Processes must be implemented to ensure business information is managed with reasonable policies and controls. 

• Managing a Data Governance program to control an organization’s data assets. Data Governance refers to the process of creating policies, workflows, standards, and metrics that ensure that data is accurate, complete, and secure. Data owners and domain stewards are responsible for specific data domains, ensuring that governed information is high quality.

• Creating data lifecycle strategies and defining corporate standards for common data definitions. 

• Setting up sound practices for identity control and access to organizational data assets. This includes providing guidelines on practices, processes, and use of other management teams’ policies to ensure uniformity in storage and other aspects of data handling. 

• Setting up data usage strategies for meeting all compliance requirements while reducing risks related to misuse or loss of sensitive information. This principle enables businesses and organizations to meet regulatory requirements while ensuring quality information is available to make decisions.

• Metadata management through well-defined data models and consistent methods for data analysis. According to Michelle Knight of DATAVERSITY®, metadata provides “context, content, and structure” for data. Thus, well-managed metadata can lead to accurate data every time. 

• Data Quality Management is essential for ensuring the accuracy and consistency of data used across the organization. Data Quality Management principles usually include data audits, which can help reduce costs by removing duplicates and automating data processes. 

Conclusion

Business managers must understand the importance of effective Data Management, which can make or break an organization’s success. Failing to manage data properly can lead to inaccurate results, delayed decision-making processes, and lost opportunities. 

A company’s preparedness to manage its data is crucial for improving operational effectiveness and gaining potential competitive advantages over its rivals. Good Data Management can also enable better decision-making, which is essential for investors, auditors, and other stakeholders. Additionally, they establish set monitoring protocols to ensure that all aspects of the organization work according to plan.

Data Management principles provide a framework for organizations to use data ethically and responsibly, ensuring the success of the organization while maintaining strong relationships with all stakeholders. By adopting these principles, organizations can integrate their data practices into their overall business strategy, which will help them achieve long-term success.

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• Source: https://www.dataversity.net/9-key-data-management-principles-and-practices/