Data Management Best Practices: Challenges & Recommendations

Data management is a collection of processes for organizing and storing data so it can be easily found and efficiently used. These processes include acquiring, validating, storing, protecting, and processing data. Good data management practices are essential for any business or organization to improve efficiency, reduce costs, and minimize risk.

The top challenges in data management are integration, automation, quality, security, and analysis. The dangers of improper data management are data loss, data breaches, and low-quality data, which can lead to financial losses, legal liability, and reputational damage. This article will discuss these challenges and the best practices to tackle them and mitigate their associated risks.

Data Management and Its Top Challenges

Summary of Top Data Management Challenges

In recent years, the volume and complexity of data have grown exponentially, making it more difficult and expensive to manage. Several factors contribute to data management challenges, including the following:

• The increasing volume, velocity, and variety of data
• The need to store data in multiple formats and locations
• The need to share data across multiple departments and organizations
• The challenge of maintaining data quality and integrity
• Security and privacy concerns associated with data
• Costs associated with data management

Due to these challenges, data management has become a critical function that can give organizations a competitive advantage. Organizations that invest in data management position themselves to make better decisions, improve operations, and protect their data assets.

The table below lists the top data management challenges and summarizes the associated risks. In the next section, we will discuss these challenges in detail and provide best practices to remove or reduce the risks described.

Best Practices in Data Management

Data Integration

Data integration combines heterogeneous data sources from various systems. Extracted data is joined, merged, and enriched with other data sources to create more meaningful and valuable data for analysis. The following best practices apply in this area:

1. Remove or Reduce Data Silos: Data silos complicate the data integration process due to the increased number of systems, file formats, and access restrictions. To determine which data sources are potential duplicates, first, take these actions across all silos: 
   1. Define and implement file and table naming conventions. It is easier to catch duplicate data sources when they are named exactly or similarly.
   2. Catalog metadata for all data sets, including author, last modified date, and column definitions. Statistical analysis of this metadata helps surface similar datasets.
   3. Disaggregate rich data (e.g., addresses) across multiple columns. Breaking down rich data into smaller parts enables removing the common parts across multiple datasets.
2. Deduplicate Data: Removing duplicate data simplifies and speeds up integration by eliminating the need to apply the same transformations to multiple data sets.
3. Define and Implement Data Retention Policies: The value of data degrades over time. If it is too stale to have any analytical value, remove it from the source systems to improve file and table scan times. 
4. Use a Modern Data Integration Framework or Platform: Data engineers should not write custom connectors to extract data from systems. Data engineering platforms, like Nexla, already provide this functionality. 

Treat data integration as an essential aspect of the long-term business strategy. Since data integration is often the first step of an end-to-end data pipeline, failing to adhere to best practices will turn it into a resource-intensive process. Multiple teams will end up doing similar integrations for their own needs, duplicating effort and wasting company resources.

Data Pipelines

Data pipelines are a set of processes that move data from one place to another. They typically include steps for data extraction, loading, and transformation. Manually creating pipelines is very challenging because such pipelines lack consistency (standards) and documentation. It is very time-consuming and prone to errors. Scaling and maintaining the pipeline quickly becomes difficult.

Manually triggered bespoke pipelines make the root cause analysis of failures challenging. Data lineage and tracking become nearly impossible. This often leads to multiple runs of the same pipeline and may cause duplicate data insertion. These and other related issues increase the likelihood of unreliable data pipelines, missed reports, or delayed dashboard updates.

Automation removes the need to manage pipelines manually and reduces the human errors associated with manual work. It frees data engineers to work on business-specific problems that require creative solutions and also speed up data delivery, yielding reduced time to analytics. 

Following the best practices below mitigates these risks and helps build reliable data automation and orchestration procedures:

1. Use Industry-Standard Tools: Automation and orchestration is a mostly solved problem, with plenty of open-source and proprietary solutions in the market. Pick the best one based on your time, budget, and skill constraints. 
2. Build Idempotent Pipelines: In data engineering, idempotency is a property of pipelines that can be run multiple times with no further impact beyond the initial execution. It is possible to re-run such pipelines safely without worrying about duplicated data or other side effects.
3. Define and Implement Service-Level Agreements (SLA): Give promises to downstream consumers about the quality and timeliness of your pipelines, and then keep your promises. Likewise, be demanding of upstream providers regarding what they will deliver you.
4. Monitor Continuously: Instant notifications from one or more channels enable acting on pipeline failures quickly. Use them. Arbitrarily checking data pipelines for failures is a terrible alternative, though it’s even worse not to check at all until something breaks or someone complains. 

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Data Quality

High-quality data is consistent, accurate, relevant, reliable, and complete, and producing it is a constant process. The degree to which it is sufficient depends on the specific needs of each organization. Still, these are some general best practices for data quality management: 

1. Establish clear and consistent definitions of all data elements: This ensures that the data is being interpreted and used in the same way by all stakeholders. This is particularly important in large organizations where data may be shared across multiple departments.
2. Set up processes for monitoring and auditing data quality: This can be done through data quality assessments, audits, and ongoing monitoring of data flows. It helps ensure that data is accurate, complete, and consistent, which brings us to the next practice.
3. Regularly review and cleanse data to ensure accuracy and completeness: This is a continuous effort that involves identifying and correcting errors and inconsistencies in the data, as well as filling in missing data.
4. Implement data governance policies and procedures: Data governance refers to the policies and procedures that are in place to ensure that data is managed and used appropriately within an organization. This includes defining who is responsible for managing data, setting standards for data quality, and establishing processes for data security and privacy.
5. Invest in data quality tools and technologies: These solutions help automate and streamline the data quality process. They include tools for data profiling, data cleansing, and data monitoring. By investing in data quality tools, organizations can improve the accuracy and completeness of their data and reduce the time and resources needed to maintain data quality.
6. Educate all users on data quality standards and procedures: Reaching data quality standards require organization-wide efforts. Provide the data users training and education on data quality standards and procedures. This may include training on data definitions, data governance policies and procedures, and data quality tools and technologies.
7. Perform cleaning steps on data systems, not on the analysis or presentation layers: This helps ensure that the data is accurate and consistent throughout the organization and that all stakeholders are working with the same data. Performing cleaning steps on the data systems also helps reduce the time and resources needed for data cleaning, as the data only needs to be cleaned once.

Data governance is an adjacent challenge to data quality, so the best practices to tackle these challenges are often complementary. Putting influential and accountable data stewards from business and IT in charge of data quality and governance is a big jump start. Treating data as an organizational asset and encouraging and promoting data-driven decision-making are other best practices.

Data Security

Data security is probably the most complex data management challenge because it requires maintaining a delicate balance between security and accessibility. One area of concern is that we want to make it impossible for anyone without proper permission to access data. The best practices to secure data are the following:

1. Data Encryption: Data should be encrypted at rest, so even if a device containing encrypted data is stolen, the data remains practically useless without the encryption key.
2. Physical Security: As the name suggests, physically lock devices that contain sensitive data. 
3. Data Destruction: Periodically delete data in accordance with data retention policies. As discussed before, the value of data degrades over time. If there is not much value left in the data, it is better to get rid of the dead weight—data can represent a security risk even if its value has become diminished. 
4. Data Privacy: It’s best practice to hide sensitive data such as date of birth, social security number, and home address. The table can still be shared, but the personal information would be obfuscated from the view of engineers and administrators who handle the data.  

On the other hand, while securing data is essential, we don’t want to force employees to jump through hoops to access the data they need for their day-to-day responsibilities. These are some of the classical solutions to data accessibility challenges:

1. Role-Based Access Control (RBAC): Identify the standard roles in the team or organization, such as analyst and developer. Then identify the data sets that these roles need to access to perform their duties, create these roles in the data system, and assign the roles to the users. Finally, grant read or write privileges on the corresponding data sets to these roles. Do not give access directly to users; use roles instead. Users come and go all the time, but roles rarely change.Since RBAC is a common practice, many tools and technologies support it. Therefore, before coming up with your own set of roles and permissions, check other processes and solutions in the organization to see if you can re-use anything that is already in place.
2. Follow the Principle of Least Access: Make sure to give each role the lowest level of access sufficient for it. This practice reduces the risk surface when a user’s credentials have been stolen. 

Data Analysis

One of the primary goals of data management is to get valuable insights from the data and help make data-driven business decisions. It is easy to lose focus and forget the purpose of tackling all these data management challenges. Keeping your data integrated, in a high-quality state, safely stored, and accessible certainly has many inherent benefits. However, a higher return on data management investment comes from analyzing this data. While not needed for every business or use case, feeding high quality into machine learning and artificial intelligence models also yield better outcomes. 

Here are data analysis best practices to increase the value gained from data even further:

1. Define the Goals and Objectives of the Analysis in Advance: This will help ensure that the right data is collected and analyzed and that the results of the analysis are aligned with business goals.
2. Choose the Right Data Analysis Tools and Methods for the Job: Data analysis tools fall on a wide spectrum of sophistication and complexity ranging from Microsoft Excel to open-source projects like  Apache Spark, so it is important to select the ones that are best suited to the task at hand.
3. Clean and Prepare the Data Before Performing Any Analysis: This step is crucial for accurate results. It’s also a reminder that data analysis is one of the main reasons for doing data management.
4. Interpret the Results of Data Analysis in the Context of Business Goals: This will help make sure that the findings are actionable and can be used to make better business decisions.
5. Adopt Collaboration Tools. The analysis of data often requires cross-departmental input from business users, data analytics, and data scientists. Self-service and collaboration define modern data engineering platforms, which invite users with different levels of technical expertise to exchange perspectives using collaboration tools built into the data engineering platform.

Additional Data Management Best Practices

Here are a couple of general best practices that are broadly applicable to data management and don’t fit under the previous sections:

• Use Skilled Data Processionals Wisely: There is a significant lack of skilled data professionals in the industry, so if you have a person like that on your team or in your organization, make sure to prioritize their work based on business imperatives and use them efficiently. 
• Empower Employees with Proper Tools: Purpose-built software applications are best suited to repetitive tasks that require no creative thinking. Invest the time in creating automation for mundane analysis so that your human assets can focus on subjective analysis.

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Conclusion

Proper data management is essential to get the most value out of data. It has multiple aspects, each with its own challenges, risks, and best practices to mitigate them. In this article, we discussed the top data management challenges in the categories of integration, automation, quality, security, and analysis. 

Data integration best practices ensure that data is efficiently extracted from multiple sources and then joined, merged, deduplicated, standardized, and enriched. They prevent wasting valuable resources when multiple teams perform the same or similar integrations for their own needs.

Data pipeline automation means putting automation and orchestration at the heart of all processing steps. It enables the building of reliable pipelines and prevents data value degradation by promptly making data ready for analysis. Built-in monitoring ensures that the appropriate teams are immediately notified of pipeline failures.

Data quality best practices are all about increasing trust in data by making it clean, consistent, accurate, reliable, relevant, and complete. They do so mostly by implementing data governance policies and procedures and investing in data quality tools and technologies that include automated data quality monitoring features. 

Data security is mostly concerned with preventing data loss and unauthorized access without completely locking data behind closed doors. It requires a fine balance between security and accessibility. Data encryption and role-based access are time-tested best practices for data security requirements. 

Finally, data analysis is often the ultimate goal of dealing with all these data management challenges. It helps organizations make accurate, measurable, and data-driven decisions. It can provide a significant return on investment. 

It’s challenging to recruit data engineers and develop customized platforms for data analysis. That’s why modern data management platforms use self-service, no-code techniques to make it easy for business users and data analysts to directly partake in data analysis and focus on solving business problems rather than developing software.