How to Implement Data Management Strategies in Your Organization?

Well, the world is dynamically inclining towards technologies, especially digital technologies. Data flows from diverse devices. Containing and processing such a large amount of data is a necessity. You cannot take this matter for granted. It is a pool where knowledge lies in abundance. So, you cannot afford to lose even a few bits. What to do?

It’s simple. Manage your data well and effectively. Though challenging, you cannot afford to skip its handling. Mishandling means losing its essence and maximum value. According to Gartner, poor data quality leads to losing around $13 million every year. This is a very big amount.

This post will introduce you to some proven strategies for managing data effectively. So, let’s get started.

Understanding the Importance of Data Management

A report by Forbes states that poor data quality costs an average of $15 million every year, which is a hefty loss for a business. Another report by IBM predicts that poor or bad data quality can cost up to 20% of revenue annually.

Would you ever like to bear such a massive loss? Certainly, nobody wants to lose such a hefty amount of hard-earned money every year. The aforesaid statistics prove that data handling is essential for its accuracy, consistency, and integrity. It requires proven strategies to implement and manage records. Let’s check them out below:

Step 1: Assess Your Current Data Landscape

The very first step in executing data management strategies is to closely monitor and assess your organization’s current capacity or condition for handling it. Certainly, it requires aligning your data sources to comprehensively audit all sources from where the records are coming in, their formats, and their quality. Monitoring the point of entry of records is necessary because it is where mistakes start blooming. Discover other areas of improvement and potential challenges as well. They can be data silos, inconsistent data standards, or obsolete data handling or processing practices or technologies.

Overall, your data specialists should have complete details and knowledge of how to develop customized data management cycles and solutions.

Step 2: Define Clear Data Management Goals

A clear understanding of data guides data specialists toward defining specific data management goals. Ensure that these goals resonate with your business objectives. These goals could be anything, encompassing improving data quality or enhancing data governance practices. They can also be dedicated to defining smooth and seamless data access and utilization.

Overall, whatever goals you set, they should ensure accountability and a well-defined roadmap for execution.

Step 3: Establish a Robust Data Governance Framework

This step is dedicated to establishing a governance framework. Robust data governance sets up strong and effective data management. Several policies are drafted, processes are defined, and procedures are set up according to the sensitivity factor. For governance, data must be integrated, secure, and compliant with government policies.

Additionally, a robust framework should be drafted, which clearly states who is going to be accountable for which type of data and to what extent. The same goes for the rule for establishing data quality standards and access controls. Gartner has found that organizations following high-quality governance secure 50% higher ROI from their data and analytics initiatives.

Step 4: Invest in Data Management Services

The next step is to effectively leverage data management services. In cases of inadequate resources, outsourcing can be an excellent alternative. They employ qualified experts, tools, and resources to align them with data management processes like cleansing, verification, and analysis.  All of these should also be helpful in minimizing risks and optimizing data utility.

Deploying cloud-based storage, for example, can be helpful in scaling up the space if the inflow of data is overwhelming.

Step 5: Embrace Data Integration and Automation

An organization may require additional data to add value to its records. In that case, data integration proved helpful. So, while implementing data management strategies, ensure that there is scope for data integration with your database from diverse sources. It backs automation. Consider a CRM, where customers’ data continues to flow in and the company interacts with them in real time. It helps with repetitively interacting with customers. So, this integration should not be accessible to unauthentic people. This is how a database can be kept error-free.

According to IDC, organizations investing in data integration and automation see a 30% reduction in operational costs and a 50% increase in data processing speed. It clearly proves that data integration and automation are essential.

Step 6: Educate and Empower Your Team

This strategy advocates for educating personnel and strengthening your team. The successful execution of data management strategies also requires collaboration across your organization. Also, embracing new technologies is necessary, which again requires investment in comprehensive training programs on data management best practices, tools, and technologies. This training empowers them with knowledge and expertise. In addition, it is also necessary to take ownership of data quality and compliance. The respective managers or heads should be accountable for it.

Step 7: Continuously Monitor and Adapt

Monitoring is as important as policing sensitive records. Because data management is an ongoing process, proper tracking of what is happening, evaluation and adaptation of new technologies can guide the timely overcoming of any gaps or errors. Experienced data specialists should be deployed to regularly assess how effective your data management strategies prove against predefined key performance indicators (KPIs) or metrics.

As you closely monitor, areas with gaps will be easily identified. So, that can be fixed. Embracing new technologies, advancements, and regulatory changes-anything can be required and will work. So, it is necessary to be agile and responsive. These practices can add a competitive edge to your data-driven organization.

Conclusion

Implementing effective data management strategies can be helpful in harnessing the full potential of your database. It requires multiple steps to execute a proven strategy for leveraging the potential of data management services. Properly managed data can introduce you to gaps in its quality, which are likely to create bad decisions. So, the management of data should be strictly governed by rules and strategies.