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5 Data Management Best Practices to Help You Do Data Right 

Today, small enterprises and large businesses across the globe recognize data as their most valuable asset. Few data management best practices, guidelines, and strategies guarantee your data is aligned and accurate with your business objective. 

The true value of your data is hidden in what you do with it. Every company has immense potential to help them gain a competitive advantage. If you want to unlock its potential, you should manage your data properly. In this guide, you will learn about data management, increase productivity and best practices to get your data ready for analytics. 

What is Data Management?

Data management refers to the process of collecting, organizing, storing, and maintaining data in a structured and efficient manner. It involves various activities and strategies aimed at ensuring the quality, accessibility, security, and usability of data throughout its lifecycle.

Data management includes several key aspects such as data collection, data storage, data organization, data integration, data quality, data security, data governance, and data lifecycle management. Let’s understand these key aspects:

  1. Data Collection: It is the procedure used to compile accurate and pertinent data from a variety of sources, including databases, systems, applications, and outside sources.
  2. Data Storage: To assure availability and accessibility, data must be stored in a structured manner using databases, data warehouses, or cloud-based storage solutions.
  3. Data Integration: Combining information from several sources or systems to produce a coherent and consistent view. It entails lining up data formats, resolving contradictions, and getting rid of duplicates.
  4. Data Quality: Ensuring that data is accurate, comprehensive, consistent, and reliable. Data cleaning, validation, standardization, and monitoring are all components of data quality measures.
  5. Data Security: Implementing measures to protect data from unauthorized access, and breaches, and ensuring compliance with data privacy regulations. This includes data encryption, access controls, and data backup strategies.
  6. Data Governance: Establishing policies, processes, and procedures to define roles, responsibilities, and accountability for data management. It ensures data integrity, compliance, and alignment with business objectives.
  7. Data Lifecycle Management: Controlling data generation, storage, retrieval, archiving, and disposal throughout its lifecycle. It entails setting up data archival plans, data cleansing, and retention durations.

Effective data management is crucial for organizations as it enables informed decision-making, supports business operations, enhances data-driven insights, improves efficiency, and mitigates risks associated with data inconsistencies or inaccuracies.

5 Data Management Best Practices to Get Your Data Ready for Analytics

  • Simplify access to traditional and emerging data: Increased data volume typically leads to improved predictive capabilities, emphasizing the significance of having a substantial amount of data accessible to business analysts and data scientists. With greater access to data, it becomes more feasible to swiftly identify the most effective predictors for specific outcomes.
  • Strengthen the data scientist’s arsenal using advanced analytics methods: Summary statistics assist analysts in comprehending the distribution and variability of data, considering that data doesn’t always follow a normal distribution, which is assumed by many statistical techniques. Correlation analysis reveals the variables or combinations of variables that hold the highest predictive power, considering the strength of their relationship and the potential influence they may exert on each other.
    1. Scrub data to build quality into existing processes: A significant portion, approximately 40 percent, of strategic processes experience failure due to inadequate data quality. By utilizing a data quality platform that adheres to the best practices of data management, you can seamlessly integrate data cleansing within your data integration workflow. This approach improves performance by shifting processing tasks to the database level. Furthermore, it enables the elimination of invalid data according to the specific analytic method employed and enhances data by employing binning techniques, which involve grouping data that was originally in smaller intervals.
  • Shape data using flexible manipulation methods: To prepare data for analytics, it is necessary to merge, transform, denormalize, and occasionally aggregate data from various tables into a single, expansive table known as an analytic base table (ABT). Modern solution providers offer user-friendly, graphical interfaces that simplify the process of data transposition. These platforms also offer additional transformation options, such as frequency analysis, data appending, partitioning, and merging data, as well as various summarization techniques, providing a comprehensive suite of reshaping capabilities.
  • Share metadata across data management and analytics domains: By employing a shared metadata layer, you can consistently replicate your data preparation procedures. This facilitates collaboration, offers lineage details regarding the data preparation process, and simplifies the deployment of models. The advantages of implementing a common metadata layer include increased productivity, enhanced accuracy of models, reduced cycle times, improved flexibility, and the availability of auditable and transparent data.

In Conclusion

Today, analytics is one of the emerging IT topics- it is inevitably a very dominating and growing technology. But as you unfold the magic and power of analytics, it is important to remember that underlying analytics is data. Don’t underestimate how significant it is to do your data right. 

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