Machine learning is propelling large businesses with powerful, data-driven insights. However, the question "what is data management?" isn’t exclusive to enterprise-level companies. Data management encompasses a range of concepts and processes, which can seem overwhelming. But even small teams can leverage data effectively to boost revenue, enhance productivity, and improve customer experiences.

Data Lifecycle Management (DLM)
The key stages of DLM include:

DLM is particularly important for large enterprises or organizations that deal with vast amounts of data, where data must be categorized into different tiers based on its importance, sensitivity, or usage frequency. In such cases, DLM often involves advanced automation to streamline the management of data at each stage, helping businesses reduce costs, improve efficiency, and stay compliant with data regulations.

• Aggregation: Collecting and consolidating data from various sources.
• Retrieval: Accessing and extracting relevant data efficiently.
• Usage: Utilizing data for analysis, decision-making, and business operations.
• Storage: Organizing and maintaining data securely for future use.
• Transfer: Moving data between systems while ensuring integrity and security.
• Deletion or Destruction: Safely removing or permanently erasing data to prevent unauthorized access.

Business applications and the databases that support them come in a wide range of sizes and complexities, so it’s important for each company to adopt a data management approach that fits its specific needs and technological landscape. The steps involved in data management are not one-size-fits-all, and companies should assess their current systems and objectives before determining the best approach. Depending on the size and maturity of the organization, some stages may need to be expanded, added, or prioritized to meet particular goals.

For example, data cleansing might be a relatively minor and straightforward task for a startup with limited data. In this case, it could be performed as a one-off task or during the initial stages of data entry. However, for larger, enterprise-level companies dealing with vast amounts of complex data, data cleansing might be a more intensive, ongoing process. These organizations may need to prioritize it earlier in the data management pipeline to ensure that the data they work with is accurate, reliable, and aligned with business requirements. Proper data cleansing can prevent costly errors, ensure the integrity of decision-making, and improve overall operational efficiency.