4 steps to improve the data quality of your organization

4 steps to improve the data quality of your organization

The world is being overrun with data, challenging organizations to manage huge volumes of data and derive valuable insights for decision making. But data is only useful if it is high quality. According to IBM, bad data costs the US alone $3.1 million per year. These costs include the time and effort employees put into correcting bad data and errors.

Why data quality is important

Data quality is a base for data analytics and data science. It is a measure of how well-suited your data is to accomplish a particular task with accuracy and consistency. Good data helps an organization make key spending decisions, improve operations, and develop growth tactics. Even though technologies like AI and machine learning have huge potential to handle large volumes of data, they need good quality data to produce reliable results quickly.

As data is an integral part of an organization, data quality impacts many aspects, from marketing to sales to content creation.

Good quality data helps you in informed decision making, target the right audience, drive effective marketing campaigns, strengthen relationships with customers, gain competitive advantage, and so on.

Benefits of improving data quality

In this competitive era, organizations try to understand their customers better and make better financial, marketing, and development decisions based on real data for a better ROI. Bad data is nothing but unstructured data which may show quality issues like inconsistency, inaccuracy, insufficiency, or even duplicate information. It could be misleading and even more harmful for a business than a lack of data at all.

Benefits of improving data quality

Improving the data quality of your company can result in the following advantages:

  • Data-driven decision making: Decision making is based on solid reasoning, and the right data can only help make wise business decisions and provide the best outcomes.
  • Customer intimacy: Drive marketing and customer experience by analyzing entire consumer views of transactions, sentiments, and interactions by utilizing data from the system of record.
  • Innovation leadership: Learn more about your products, services, usage trends, industry trends, and competition outcomes to help you make better decisions about new products, services, and pricing. ​
  • Operational excellence: Ascertain that the correct solution is given quickly and reliably to the appropriate parties at the appropriate price and cost. Improve processes by automating them and using the relevant data to do so.

Challenges faced while maintaining data quality

  • Data debt reduces ROI: If you have a lot of data debt, you won’t be able to get the ROI you want.
  • Lack of trust leads to lack of usage: A lack of data confidence in your organization leads to a lack of data consumption, which has a detrimental impact on strategic planning, KPIs, and business outcomes.
  • Strategic assets become liabilities: Poor data puts your company in danger of failing to meet compliance standards, resulting in millions of dollars in fines.
  • Increased expenses and inefficiency: Time spent on correcting inaccurate data equals less workload capacity for essential efforts and an inability to make data-driven decisions.
  • Adoption of data-driven technologies: Predictive analytics and artificial intelligence, for example, rely on high-quality data. Delays or a lack of ROI will come from inaccurate, incomplete, or irrelevant data.
  • Customer experience: Using bad data to run your business can hinder your ability to deliver to your customers, increasing their frustration and reducing your capacity to retain them.

Improving data quality with Neal’s methodology

Maintaining high levels of data quality allows organizations to lower the expense of finding and resolving incorrect data in their systems. Companies can also avoid operational errors and business process failures, raising operating costs and diminishing revenues.

Good data quality enhances the accuracy of analytics applications, leading to improved business decisions that increase sales, improve internal processes, and provide firms with a competitive advantage over competitors. High-quality data can also help increase the use of BI dashboards and analytics tools; if analytics data is perceived as trustworthy, business users are more inclined to depend on it instead of making judgments based on gut feelings or their own spreadsheets.

Neal’s methodology can help improve data quality with these four phase steps as below:

Methodology for improving data quality

Step 1: Define the data environment and business landscape for your company

The primary phase identifies the basic understanding of your data and business landscape, as well as the core data quality principles and skills that IT requires to effectively improve data quality.

Step 2. Analyze your priorities for improving data quality

You must first identify and prioritize the data-driven business units before you can begin tackling specific, business-driven data quality projects. This will guarantee that data quality initiatives are in line with the company’s goals and priorities.

Step 3. Create a data quality program for your company

Determine the exact problems with data quality that they are experiencing and design an improvement plan to remedy it after selecting whose data will be fixed first based on priority.

Step 4. Develop and sustain your data quality practice

Make sure the data quality concerns don’t resurface now that you’ve implemented an improvement strategy. Integrate data quality management and data governance procedures into your organization’s data governance practices and strive to improve your company’s overall data maturity.


Data quality is a critical issue, which makes it even more astonishing that there are no uniform definitions or established frameworks in place to guide users through the process of data quality assessment and cleansing. As a result, standardization and transparency of the process have yet to be achieved. In our work, we propose one possible explanation: data requirements vary depending on the project and the subjective factors associated with the individuals assessing the quality of the data. That’s why Neal aims to help organizations create dynamic and flexible data quality frameworks that can scale with the business requirements.

Seeking help to improve the data quality of your organization? Contact us to get started!