12 Actions to Improve Your Data Quality (2023)

Every year, poor data quality costs organizations an average $12.9 million. Apart from the immediate impact on revenue, over the long term, poor quality data increases the complexity of data ecosystems and leads to poor decision making.

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The emphasis on data quality (DQ) in enterprise systems has increased as organizations increasingly use data analytics to help drive business decisions. Gartner predicts that by 2022, 70% of organizations will rigorously track data quality levels via metrics, improving it by 60% to significantly reduce operational risks and costs.

“Data quality is directly linked to the quality of decision making,” says Melody Chien, Senior Director Analyst, Gartner. “Good quality data provides better leads, better understanding of customers and better customer relationships. Data quality is a competitive advantage that D&A leaders need to improve upon continuously.”

12 Actions to Improve Your Data Quality (1)

No. 1: Establish how improved data quality impacts business decisions

Identify a clear linkage between business processes, key performance indicators (KPIs) and data assets. Make a list of the existing data quality issues the organization is facing and how they are impacting revenue and other business KPIs. After establishing a clear connection between data as an asset and the improvement requirements, data and analytics leaders can begin building a targeted data quality improvement program that clearly defines the scope, the list of stakeholders and a high-level investment plan.

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No. 2: Define what is a “good enough” standard of data

To improve data quality, first it is important to understand what is “best fit” for the organization. This responsibility of describing what can be defined as “good” lies with the business. Data and analytics (D&A) leaders need to have periodic discussions with business stakeholders to capture their expectations. Different lines of business using the same data, for example, customer master data, may have different standards and therefore different expectations for the data quality improvement program.

No. 3: Establish a DQ standard across the organization

D&A leaders need to establish data quality standards that can be followed across all business units in the organization. It is likely that different stakeholders in an enterprise will have different levels of business sensitivity, culture and maturity, so the manner and speed with which requirements of DQ enablements are met may differ.

“This will enable stakeholders across the enterprise to understand and execute their business operations in accordance with the defined and agreed-to DQ standard,” says Chien. An enterprise wide DQ standard will help educate all involved parties and make the adoption seamless.

No. 4: Use data profiling early and often

Data quality profiling is the process of examining data from an existing source and summarizing information about the data. It helps identify corrective actions to be taken and provides valuable insights that can be presented to the business to drive ideation on improvement plans. Data profiling can be helpful in identifying which data quality issues must be fixed at the source, and which can be fixed later.

It is, however, not a one-time activity. Data profiling should be done as frequently as possible, depending on availability of resources, data errors, etc. For example, profiling could reveal that some critical customer contact information is missing. This missing information may have directly contributed to a high volume of customer complaints and would make good customer service difficult. DQ improvement in this context now becomes a high-priority activity.

No. 5: Design and implement DQ dashboards for monitoring critical data assets, such as master data

A DQ dashboard provides a comprehensive snapshot of data quality to all stakeholders, including data from the past to identify trends and patterns that can help design future process improvements. It can be used to compare the performance over time of data that is critical for key business processes. This enables the organization to make the right business decisions to achieve the desired business objectives based on trusted quality data.

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DQ dashboards also reflect the impact of improvement activities, such as incorporating new data practices into operational business processes. They can be customized to meet the specific needs of a business and it shows how much trust you can put in your data.

No. 6: Move from a truth-based semantic model to a trust-based semantic model.

The source of data is not always internal, where data quality can be controlled and maintained right from the beginning. In some cases, data assets are acquired from external sources where the DQ rules, authorship and levels of governance are often unknown. Hence, a “trust model” works better than a “truth model.”

This means that, rather than thinking about key enterprise data as being absolute, organizations must also consider its origin, jurisdiction and governance — and therefore the degree to which it can be used in decision making. D&A leaders can implement mitigation measures when trust levels are not maintained.

No. 7: Include DQ as an agenda item at D&A governance board meetings

D&A leaders need to link DQ initiatives to business outcomes, which will help track the investments in DQ improvement against the business objectives. “To get the board’s attention, it is important that the impact of DQ improvement is communicated to the board in a language they understand best — business and revenue impact,” says Chien. The board needs to have clear visibility of the DQ improvement progress and challenges, and they need to get this information on a regular basis.

No. 8: Establish DQ responsibilities and operating procedures as part of the data steward role

A data steward is responsible for ensuring the quality and fitness for purpose of the organization's data assets, including the metadata for those data assets. In more mature organizations, a data steward’s role is also to champion good data management practices, and monitor, control or escalate DQ issues as and when they occur.

D&A leaders need to include this role in their D&A strategy, so that DQ is measured and maintained regularly in a systematic manner. Create a governance scope and stakeholder map that will allow a clear understanding of how DQ issues are managed.

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No. 9: Establish a special interest group for DQ across BUs and IT, led by the chief data officer team or equivalent body

A dedicated group that has representation from BUs, IT and the office of the CDO that collaborates for DQ improvement can be a great investment of time and resources. Such collaboration enables better organizational management of risk. It also creates more opportunities for reducing operational cost, and encourages growth through shared and consistent best practices.

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No. 10: Establish a DQ review as a release management “stage gate”

Review and update progress to make timely corrections and checks. As the organization’s maturity to handle DQ initiatives improves, identify and circulate the best practices that have been impactful.

No. 11: Communicate the benefits of better DQ regularly to business departments

D&A leaders need to measure the impact of the improvement program, and communicate the results periodically. For example, a 10% improvement in customer DQ can be linked to a 5% improvement in customer responsiveness, since customers can be serviced better and faster by customer care executives due to the availability of good-quality, trusted data.

“It is not only important to have the board’s attention in DQ improvement, but also for it to be a sustainable practice. It is important that the benefits are communicated to the board periodically,” says Chien.

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No. 12: Leverage external/industry peer groups, such as user groups from vendors, service providers and other established forums

D&A leaders can connect the enterprise with DQ peer groups and encourage organizational maturity in this area. This will enable them to exchange alternative perspectives on best practices and insights into the approaches others are taking to address similar challenges.

12 Actions to Improve Your Data Quality (2)

FAQs

What are the 5 data quality? ›

There are five traits that you'll find within data quality: accuracy, completeness, reliability, relevance, and timeliness – read on to learn more.

What are the steps to achieve success in data quality? ›

Data Quality – A Simple Six-Step Process
  1. Step 1 – Definition. Define the business goals for Data Quality improvement, data owners/stakeholders, impacted business processes, and data rules. ...
  2. Step 2 – Assessment. ...
  3. Step 3 – Analysis. ...
  4. Step 4 – Improvement. ...
  5. Step 5 – Implementation. ...
  6. Step 6 – Control.
6 Mar 2017

What is data quality with example? ›

Data that is deemed fit for its intended purpose is considered high quality data. Examples of data quality issues include duplicated data, incomplete data, inconsistent data, incorrect data, poorly defined data, poorly organized data, and poor data security.

What are the 10 characteristics of data quality? ›

Terms in this set (10)
  • Accuracy. The Data is Correct. ...
  • Accessibility. The Data is easily obtained. ...
  • Consistency. The data is reliable. ...
  • Comprehensiveness. The required data is included. ...
  • Currency. The data is up to date. ...
  • Definition. The data and information in the health record are clearly defined. ...
  • Granularity. ...
  • Relevancy.

What are the 7 aspects of data quality? ›

How can you assess your data quality? Data quality meets six dimensions: accuracy, completeness, consistency, timeliness, validity, and uniqueness.

What are the six qualities of data? ›

What are the 6 dimensions of data quality?
  • Accuracy.
  • Consistency.
  • Validity.
  • Uniqueness.
  • Integrity.
29 Aug 2022

What are the 8 dimensions of data quality? ›

Garvin has developed a framework encompassing eight dimensions of quality: performance, features, reliability, conformance, durability, serviceability, aesthetics, and perceived quality (Garvin, 1988).

What is good data quality? ›

What does good quality look like? Good quality data is data that is fit for purpose. That means the data needs to be good enough to support the outcomes it is being used for. Data values should be right, but there are other factors that help ensure data meets the needs of its users.

What are the 10 steps to success? ›

Here is a list of 10 tips to help you become successful in your life:
  1. Be committed. Through commitment, you can gain motivation to pursue success. ...
  2. Learn from the journey. ...
  3. Have fun along the way. ...
  4. Think positively. ...
  5. Change your perspective. ...
  6. Be honest with yourself. ...
  7. Take away distractions. ...
  8. Count on yourself.

How can you improve data quality and accuracy? ›

10 Tips for Maintaining Data Accuracy
  1. Tip 1: Create a centralized database. ...
  2. Tip 2: Capture and store all data results. ...
  3. Tip 3: Don't put pen to paper. ...
  4. Tip 4: Assign permissions to change data. ...
  5. Tip 5: Keep data sources in sync. ...
  6. Tip 6: Standardize the data entry process. ...
  7. Tip 7: Simplify the data entry process.

What are the 14 steps in data processing? ›

Generally, there are six main steps in the data processing cycle:
  1. Step 1: Collection. The collection of raw data is the first step of the data processing cycle. ...
  2. Step 2: Preparation. ...
  3. Step 3: Input. ...
  4. Step 4: Data Processing. ...
  5. Step 5: Output. ...
  6. Step 6: Storage.

What are data give 5 examples? ›

In our day to day life, we can collect the following data.
  • Number of females per 1000 males in various states of our country.
  • Production of wheat in the last 10 years in our country.
  • Number of plants in our locality.
  • Rainfall in our city in the last 10 years.
  • Marks obtained by students.

What are the 4 categories of data quality? ›

Four Categories of Data Quality Management
  • Assess. Poor data quality and data quality management impact the business through inefficiencies, errors, additional costs or even fines. ...
  • Remediate. ...
  • Enrich. ...
  • Maintain.

What are the 11 characteristics of quality information? ›

Those discussed above and included in Table 1.1 and Figure 1.6 include: understandability,relevance (or reliability), timeliness (or availability), predictive value, feedback value, verifiability,neutrality (or freedom from bias), comparability, consistency, integrity (or validity,accuracy, and completeness).

What are the types of quality data? ›

7 Types of Data Quality
  • Relevance. Data that is useful to support processes, procedures and decision making.
  • Timeliness. How quickly data is created, updated and deleted.
  • Precision. The exactness of data. ...
  • Correctness. Data that is free of errors, omissions and inaccuracies.
  • Completeness. ...
  • Credibility. ...
  • Traceability.
6 Nov 2016

What is data quality checklist? ›

A data quality checklist is used by companies to locate and fix any errors related to data entry. The everyday nature of dealing with data, including entering the data, reviewing it, and signing off on its validity, leaves huge potential for error and certainly wastes a lot of time.

What are the 9 dimensions of quality? ›

At Zeenea, we believe that the ideal compromise is to take into account nine Data Quality dimensions: completeness, accuracy, validity, uniqueness, consistency, timeliness, traceability, clarity, and availability.

What are the 8 aspects of quality explain? ›

Garvin proposes eight critical dimensions or categories of quality that can serve as a framework for strategic analysis: Performance, features, reliability, conformance, durability, serviceability, aesthetics, and perceived quality.

What are top 3 skills for data quality officer? ›

The following data quality skills will be required for this role: Data profiling. Data discovery. Information chain analysis and management.

How can you improve data quality and accuracy? ›

10 Tips for Maintaining Data Accuracy
  1. Tip 1: Create a centralized database. ...
  2. Tip 2: Capture and store all data results. ...
  3. Tip 3: Don't put pen to paper. ...
  4. Tip 4: Assign permissions to change data. ...
  5. Tip 5: Keep data sources in sync. ...
  6. Tip 6: Standardize the data entry process. ...
  7. Tip 7: Simplify the data entry process.

Why is it important to improve data quality? ›

Improved data quality leads to better decision-making across an organization. The more high-quality data you have, the more confidence you can have in your decisions. Good data decreases risk and can result in consistent improvements in results.

How can data quality and reliability be improved? ›

6 Ways to Make Your Data Analysis More Reliable
  1. Improve data collection.
  2. Improve data organization.
  3. Cleanse data regularly.
  4. Normalize your data.
  5. Integrate data across departments.
  6. Segment data for analysis.

What is data quality explain? ›

Data quality is a measure of the condition of data based on factors such as accuracy, completeness, consistency, reliability and whether it's up to date.

How can you improve data collection? ›

7 Points to consider if you aim to boost your data collection prowess
  1. Strategize, filter, and set objectives. ...
  2. Consider the importance of customer and behavior-related data. ...
  3. Use effective data collection tools. ...
  4. Identify key metrics and data sources. ...
  5. Identify who will review your data.

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