7 Powerful Data Management Best Practices for 2024

Introduction

In today’s digital landscape, businesses collect extensive data, acknowledging its essential role in informed decision-making. Yet, with this reliance on data comes the critical responsibility of efficient management and careful handling.

Why is this responsibility so crucial?

Effective data management serves as the cornerstone for improving decision-making, streamlining operations, and reducing expenses by ensuring consistent access to high-quality information. But where do you begin? Here are some data management best practices to kickstart your journey.

1. Create a Centralized Data Repository

Creating a centralized data repository is essential for businesses to derive value from their data and ensure data integrity and accessibility. Why? Because it is crucial to get a 360-degree perspective on business metrics, and for that, your models need data from different sources. An SSOT (Single Source of Truth) is a data warehouse or repository that provides a complete and authoritative picture of a company, allowing users to access all the data they need without jeopardizing any mission-critical database

For example, to build a customer score model, you may need credit risk scores, customer service history data, client location details, etc. While some of this data may be owned by your finance teams, other data points may be owned by your sales and marketing teams. Building holistic data models by ensuring data access to different teams in a secure fashion with appropriate controls is essential for a successful SSOT implementation.

In today’s data-driven world, businesses rapidly generate massive amounts of data. Managing this data effectively and timely is critical for decision-making. The answer lies in the concept of a SSOT, which is the key to unlocking the potential of this data, empowering businesses to make sense of it and make accurate business decisions.

Recommended Reading: How to Ensure Customer Data is Up To Date.

2. Define Clear Data Governance Policies

Data governance is the foundation of effective data management. It involves defining policies, procedures, and guidelines for managing data throughout its lifecycle. By establishing clear data governance policies, organizations can ensure that data is accurate, consistent, and accessible to authorized users. This includes defining roles and responsibilities, establishing data quality standards, and implementing data classification and security measures.

3. Facilitate Access with Stringent Controls

Ensure that essential data doesn’t vanish within servers; empower your team with accessible information while upholding necessary controls. Valuable data often remains underused due to key personnel being unaware, impeding effective utilization.

Establish a comprehensive record-keeping system outlining available data and access privileges. Utilize customizable dashboards to monitor specific company metrics, enhancing visibility during company-wide discussions.

Implement data classification techniques to restrict access based on job roles. For example, confidential contract details might not be relevant for a junior market research analyst.

4. Implement Data Backups and a Disaster Recovery Plan

Data loss poses significant financial and operational risks. The loss of critical data can result in downtime and missed opportunities, impacting your business’s functionality. Various factors like accidental deletions, cyber theft, or physical damage to servers can contribute to data loss.

To ensure continuity, establishing data backups and a disaster recovery plan is imperative. Engage with your data server vendors or cloud providers to institute necessary data recovery protocols and synchronization points. Develop comprehensive business continuity plans that serve as a reference during unforeseen circumstances.

5. Continuously Monitor and Improve Data Management Processes

Data management is an ongoing process that requires continuous monitoring and improvement. Organizations should regularly assess their data management processes, identify areas for improvement, and implement necessary changes. This includes monitoring data quality metrics, analyzing performance indicators, and soliciting feedback from users. 

By continuously refining data management processes, organizations can adapt to evolving business needs and ensure the effectiveness of their data management practices.

6. Prioritize Data Security

Did you know that the cost of data breaches is expected to top $5 trillion by 2024?

Ensuring the safety of your company’s data should be a top concern for all teams. Confidential financial information, like budgets and investment plans, if breached, can result in a loss of competitive advantage, damage to reputation, and regulatory penalties.

It’s crucial to verify that any vendors or partners comply with the highest data protection standards. Implement data security tools that encrypt sensitive data and facilitate secure sharing.

Additionally, enforce standard security protocols for accessing data daily, such as mandating two-factor authentication for employee access to company apps. Employ data classification systems to limit access to authorized individuals. Conduct regular security awareness training sessions for employees, at least every six months.

7. Enhance Decision-Making with Predictive Analytics

While not a conventional aspect of data management practices, leveraging predictive analytics is crucial for unlocking the full value of data. Data’s inherent worth lies in its predictive potential, enabling the forecasting of future outcomes.

For example, let’s say you want to anticipate when a customer will pay theirdue invoice.Analyzing past payment trends and integrating variables like previous payment dates and invoice amounts into a predictive model allows for estimating the likely payment date.

Integrating the appropriate data into robust forecasting models facilitates predictions in complex business scenarios. Presently, predictive technologies are increasingly sophisticated and accessible. Employing tools like predictive analytics software empowers accurate anticipation of business outcomes, enabling proactive responses to challenges and opportunities alike.

HighRadius solutions offer AI-driven capabilities to forecast payment delays andpredict payment dates, effectively identifying and managing at-risk customers.

Wrapping Up

The primary goal of data management is to extract maximum value from the information collected. Establishing robust data management practices requires ongoing, sustained efforts.

Adhering to the discussed data management best practices across your organization and leveraging appropriate technological tools to support data management, sharing, and governance are pivotal.

HighRadius’ cloud-based AR solutions enable the creation of a cohesive ecosystem utilizing data from various sources, seamlessly driving all pertinent operations.