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Use risk mitigation in your data quality policy

Having understood how important data quality can be, you may be looking for ways of immediately improving quality data within your organization.

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As businesses race towards collecting and analyzing data, many have fallen short of maintaining the quality of this data. Implementing a data quality strategy requires planning and investment. In addition to cleansing incoming sources for potential issues, your company should also develop an organizing principle around maintaining high information standards. This means regularly analyzing data so as to gain a better understanding of quality determinants. It also means keeping up with all sources of business information so you can identify trends from the past, cover quality gaps, and correct accuracy issues.

Data quality determines your ability to develop a risk mitigation plan. Indeed, having data that contains numerous issues will cloud your insights and affect the accuracy of future projections. Low-quality data also limits your ability to understand your risk environment. And if your business is unable to predict and respond to risks, the likelihood of disruption is high.

What Is Risk Mitigation?

Risk mitigation is the process of identifying, analyzing, and responding to risks in a timely manner. Every business faces a certain level of risk. This is why understanding your risk environment and developing a plan for mitigation is crucial. Data has become the driving force behind an effective risk management strategy. By using a data-driven approach, businesses are able to gain quality insights and increase their understanding of various determinants of risk.

Quality and accurate data is the lens through which you can gain foresight into your risk environment. This information is what will be used to identify, analyze, and mitigate threats that face your business. Because risk mitigation is a continuous process, it evolves with industry trends and other factors that affect your company’s operations. The best way for you to keep up with threats is by ensuring that your incoming data sources are continuously analyzed. Taking a proactive approach to data analysis will give you a deeper understanding of your information streams and how you can continue to improve data quality.

Integrating Data Quality Into Your Risk Mitigation Plan

The challenge that many businesses face is integrating data quality into their risk mitigation plans. Some companies use old or inaccurate data for risk management, while others simply cleanse incoming streams of data without developing a framework for quality management. Managing data on an adhoc basis will give you limited control over quality issues. And if your business is only able to fix gaps in data at the last minute, your ability to check such data for completeness, accuracy, and insight will be limited.

The good news is that you can improve on data quality by implementing a specific framework. By focusing on guidelines, data streams, and business processes, you can establish a strategy for continuously analyzing data and using it as a tool for risk mitigation. Furthermore, understanding what affects the quality of business data will enable you to plug any loose holes and correct errors before your data is used in downstream processes.

A data quality strategy is essentially a process that makes data management easier in your company. But what specific workflows should be included in this strategy? Begin by analyzing all elements of incoming data. How does this information look like? In what form is it collected, stored, and analyzed? What systems are used to facilitate data analysis? Which types of risk could affect the integrity of this process? By digging deeper into your data management workflows, you can identify problematic areas and evaluate controls that are currently in place. Furthermore, continuous analysis allows you to focus on the quality of individual data streams rather than just the overarching process.

A data quality strategy influences many different aspects of your business. For example, integrating high-quality standards across the organization will improve productivity and fuel growth. High-quality data also makes it easier for you to keep track of data governance, compliance, and other similar requirements. With these elements in place, developing a risk mitigation strategy will be cheaper, more convenient, and more effective.

How To Ensure Data Quality In Your Organization

Having understood how important data quality can be, you may be looking for ways of immediately improving quality data within your organization. You can use many different tools (that you already have) to streamline incoming data sources while keeping up with continuous analysis. For example, gaining insights from past data is a good place to start when discovering trends about previous data quality. By understanding the level of errors in past data streams, you can use these insights as a building block for the future.

Intelligent tools such as AI allow you to leverage machine learning for your data quality strategy. Previous errors can be automatically detected and corrected, while new data controls can be programmed for automatic implementation within your data streams. In this way, you can maintain a responsive data quality strategy that reflects the goals and environment of your business.

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