Data Management Best Practices

Data Management Best Practices

Data management best practices are a must for businesses that rely on the information they collect and use. These techniques help them ensure data accuracy, accessibility, and security.

The right team of professionals can provide your business with the tools to better manage and organize information gathered from different software solutions. This will allow for more meaningful insights into the company processes and business operations.

Cybersecurity Measures

Nearly every business captures personal identifiable information (PII) from customers. Customers expect brands to safeguard this data, and brand confidence declines when data breaches occur. Compromised PII can also damage an organization’s reputation and cause financial loss due to lost revenue and productivity.

Effective cybersecurity relies on data governance and data catalogs to understand where data resides, whether it should be encrypted and which users and groups have access to it. These capabilities help organizations identify and prioritize security measures.

To combat cyber criminals and avoid paying ransom, teams need modern data management security solutions that deploy a defense-in-depth cyber resiliency architecture with strict access controls and guidance guardrails. Cohesity next-gen data management delivers key capabilities in five critical areas to defend against cyber attacks, ensuring teams have the confidence they need to stop bad actors in their tracks. The solution also protects against disaster recovery challenges, including hardware failure and natural disasters. These capabilities are vital to an organization’s overall data protection strategy.

Data Quality Measures

Data management organizations take measures to ensure that the data they collect is of high quality. These efforts are focused on ensuring that data is accurate, complete, consistent, timely and unique. This helps make the company’s analytics and AI/ML initiatives more reliable.

Accuracy measures how close the data is to the real-world entity it is meant to represent. Data profiling and initial assessments of the current state of data sets identify accuracy issues.

Completeness identifies whether all required data points are available in the data set. For example, if the customer survey asks for a middle name but the data set only has first names, this metric will identify incomplete data. Consistency measures how well the data values drawn from multiple sources synchronize and agree with each other. This is tracked through comparing data sets and checking for duplicates. It also tracks whether the data conforms to standards (i.e., does the data fit in the expected formats). Frequent checkups of this kind are known as in-line data quality measurement.

Data Access Measures

Data management is essential to ensuring that business operations are informed by the highest-quality, most reliable information possible. It helps ensure that teams are working with the same version of truth and avoiding costly mistakes caused by outdated or inaccurate data.

Secure access to this data allows organizations to take well-informed choices, acquire a competitive advantage, increase operational efficiency and adhere to compliance and security requirements. They may also prevent data breaches by limiting the access rights of individual users.

This requires establishing access privileges using a role-based approach. For example, the RBAC model is an efficient way to allocate privileges based on the specific needs of each user, making sure that they are only accessing the data they need. It also eliminates the risk of data being stolen and sold on online marketplaces, where unscrupulous competitors can use it to preempt your business plans or gain insights into your audience. These measures can reduce the cost of acquiring, retaining and protecting information.

Data Integration Measures

Data integration involves connecting disparate software systems and data sources to create a single, unified view of information. This approach also makes it easier to integrate with emerging technologies, such as artificial intelligence tools that can analyze massive volumes of data to identify patterns and predict outcomes.

The data management process enables you to filter and aggregate data early in the data integration process so that it is more manageable. This can help you reduce the number of data points you need to store and process, as well as minimize storage costs.

Data management processes are critical for effective business operations and regulatory compliance. They help you ensure that your data is accurate, secure and accessible. They also enable you to collaborate more effectively with colleagues, especially when working remotely during the Covid-19 pandemic or other times of disruption. By limiting data silos, you can achieve better results and boost employee morale. Data management can also help limit the impact of cybercriminals.