What is Data Management?
Data management is an information technology (IT) practice that organizes and controls data resources to make them more accessible, reliable, and timely. As the amount of information increases, data management is required to distinguish what is valuable versus noise. In addition to guiding the storage solutions used for different types of data, data management also aims to protect against breaches and malicious activity. Today, businesses are gathering more and more data from various sources to make better decisions. Companies that focus on using these data management systems efficiently can adapt faster, helping them respond quickly to changes in the market.
Data Governance vs. Data Management
These two terms are often confused. However, data governance is the strategy behind data management. An organization’s data governance strategy aims to minimize business risks associated with poor data by establishing policies, procedures, and guidelines for managing the assets. The strategy also defines roles, responsibilities, and processes for data access and appropriate usage to prioritize the financial benefits of data.
Data Management Systems
Data management systems are like digital organizers that store, manage, and discover data and provide a range of other valuable services. These services include analyzing data to find patterns, using artificial intelligence to make predictions, employing machine learning to improve processes, and generating reports to summarize important information.
These systems often include one or a combination of databases, datahubs, data lakes, and data stores. They range in size from large data warehouses to smaller data marts that support specific systems and analytics platforms. Data management systems also involve modeling, which maps how information flows across systems. Another component, data integration, enables the operational and analytical use of raw data from a variety of sources. In data governance, policies and procedures ensure that the organization’s data is uniform. Errors and inconsistencies are corrected in data quality management.
IT teams regularly connect with business teams to learn about upcoming trends and gauge their future data and analytics needs. This collaboration ensures that data management systems are fit for their intended purpose. Databases must also be maintained, protected, managed, and processed. Some organizations have moved to open-source software for big data management (such as Apache’s suite of software: Airflow, Spark, and Cassandra) and other database management systems (such as PostgreSQL or MySQL) to keep a check on costs.
Essential Components of Data Management
Some of the modules that make up an effective data management system in any organization include:
- Data Ingestion and Processing: Data management systems must be able to ingest and integrate data from various data sources that can reside on-site or in cloud data warehouses. The data can be ingested in batches or as a real-time data stream. Furthermore, the systems should be capable of cleansing the data. Ensuring your data is accurate and reliable is essential if you want to use it for multiple business operations, such as reporting and analysis.
- Privacy: Effective data management systems should incorporate strong security measures, protecting information from unauthorized access and other threats. It should also protect critical data from potential malicious use. In addition, they should be able to recover lost data in case of disaster. Rules should dictate how long data can be retained and still remain compliant with internal enterprise governance policies or government regulations.
- Reporting and Analytics Enablement: Your data management system should be compatible with reporting and analytics solutions. This means ensuring your data is available, reliable, secure, and well-governed. Centralized storage of all data assets saves time and eliminates team silos.
- Usability: Data management systems should have a user-friendly interface or user experience (UX). A difficult-to-use data management system defeats the purpose of having one in the first place; your team won’t use it. Instead, the system should offer self-serve capabilities that can reduce the complexity of data consumption and reduce maintenance costs.
Benefits of Data Management
Effective data management can provide a company with immediate benefits. First and foremost, it will improve decision-making. With well-organized and easily accessible data, decision-makers can access pertinent information quickly. Similarly, a well-organized and managed system will help improve data quality and ensure accurate, complete, and up-to-date data, leading to higher-quality records.
An added advantage is heightened data security. Beyond preserving the confidentiality and integrity of sensitive data, proficient data management includes safeguards against unauthorized access. Threats may emanate not only from external sources but also from within an organization. Next, an effective system will help improve collaboration. Team members can work together more efficiently using data management tools and processes. Finally, a well-run data management system will reduce costs associated with storing, maintaining, and retrieving information.
Figure 1: Benefits of Data Management
Data Management Challenges
Of course, nothing comes easy. To earn these benefits, some industry challenges must be overcome. We’ve already touched upon one of those critical hurdles: data governance. Managing your data effectively requires a robust data governance framework. For an organization to manage data effectively, it needs to establish policies, procedures, and standards that adhere to the best practices in the industry.
We mentioned data quality as one of the benefits, but it can also be a significant challenge. Organizations must develop processes for verifying their data’s accuracy, completeness, and consistency. Security is another challenge. Keeping data safe from unauthorized access, theft, and misuse requires constant vigilance. The same caveat applies to data privacy. Organizations must implement policies and procedures that protect client and employee data from unauthorized access, use, or disclosure in the wake of growing concerns over data privacy.
Data integration can be another struggle. Consolidating data across various sources into a unified view is easier said than done. The data’s lifecycle must be considered; not just the amount of time it resides in an organization’s possession, but the application of standardized storage and protection rules from day one through deletion.
Finally, analytics can be complicated. The goal is to extract value from the data, so to make informed decisions about business operations, customer behavior, and industry trends, organizations need to develop the proper strategies and tools to analyze and interpret their data.
Best Practices of Data Management
When a company decides to implement an effective data management system, there are some best practices it can follow to ease the process.
Figure 2: Best Practices of Data Management
Data Management’s Importance in Pharmaceuticals
The pharmaceutical industry has unique data management needs that set it apart from other industries. For instance, data management is vital in collecting and storing clinical trial data. Clinical data provides insight into a potential drug’s safety and efficacy. Researchers can also uncover disease mechanisms and potential drug targets by analyzing large datasets. This information can lead to the development of new drugs and help researchers design trials that are more likely to succeed. This data is also essential for regulatory agencies to assess the risk-benefit profile of new drugs.
As previously noted, robust data management can help with regulation compliance, which is a foundational aspect of the pharmaceutical industry. The US Food and Drug Administration (FDA) requires precise documentation of drug development and trials. Similarly, regulations such as the General Data Protection Regulation (GDPR) in the European Union, the California Consumer Privacy Act (CCPA), aggregate spend reporting rules, the Health Insurance Portability and Accountability Act (HIPAA), Do-Not-Call (DNC) regulations for telemarketing, and the Physician Data Restriction Program (PDRP) further emphasize the importance of robust data management practices. These regulations necessitate sound data management practices, shielding organizations from penalties and legal entanglements.
In pharmaceutical manufacturing, data management and analytics are essential for ensuring the consistency of products. Analysis of manufacturing data helps companies identify issues, streamline production processes, and ensure adherence to regulatory requirements.
Data management and analytics are also essential in the marketing and sales of pharmaceutical products. Identifying opportunities for growth and optimizing marketing strategies can be accomplished by analyzing sales data and market trends.
Conclusion and Takeaways
Data management is the cornerstone of a thriving, data-driven organization. It secures business success by facilitating informed decision-making, optimizing efficiency, ensuring regulatory compliance, and fostering innovation. Organizations that commit to robust data management will excel in an era marked by rapid change. Embracing data management is an investment that pays off in the form of sustained competitiveness and growth, and is essential for organizations to flourish in the data-driven era.
This article is contributed by Pulkit Dhingra, Director at Axtria.
