Data Quality

What is data quality

Data quality measures the condition of your data, using factors such as accuracy, consistency (in all fields across data sources), integrity (whether the fields are complete), and usability. An exemplary score in all these fields equals high-quality data, the best kind to use for processing and analysis. 

Data is one of your company’s most important resources. Businesses lose up to 20% of revenue due to poor data quality practices (Kissmetrics). Your business intelligence is only as good as the data you feed it. Make sure your data analysis isn’t losing value because what’s feeding it is inaccurate.

High-quality data has become essential as data-driven companies increasingly rely on insights, customer databases, and marketing campaigns to drive key business decisions. Inconsistent or incomplete data can reduce customer confidence and weaken your market position.

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How do I measure data quality?

To make sure your data is working for you at full capacity, assess your data quality regularly. A study by Gartner cites six reference points for data quality management:

  • Consistency: When one piece of data is stored in multiple locations, do they have the same values?
  • Accuracy: Does the data accurately describes the properties of the object it is meant to model?
  • Relevance: Is the data appropriate to support the objective?
  • Existence: Does the organization have the right data?
  • Integrity: How accurate are the relationships between data elements and data sets?
  • Validity: Are the values acceptable?

Most companies rely on software to identify and correct errors. There is a crowded market of data quality tools that ensures an organization’s data is clean and useful. Many programs offer more than just data assessment, with features such as program management, roles, use cases, and processes (such as those for monitoring, remediating, and reporting data quality) and setting up organizational structures. Some of the most popular current market offerings include Informatica Data Quality, Microsoft Data Quality Services, Ataccama ONE, and Oracle Enterprise Data Quality.

Good news — if your data is not in good condition, it can be repaired. The following four steps can be used to create a strategy to conquer data quality challenges.

  1. Establish company departments and executives to oversee data and supply them with the tools they need to establish effective quality standards.  
  2. Measure the value of data. Be proactive and measure the quality of your information assets, as well as the cost of poor-quality data. These numbers can be linked directly to key business metrics for optimal performance. 
  3. Make accurate time frame estimates to deploy data quality software. Many companies overestimate this amount of time, which leads to distrust between business operations and IT. 
  4. Get the most out of your data quality tools. This type of software is not cheap, so optimize your cost by remaining flexible and driving value while maintaining your high standards.

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Data quality management

An effective data management strategy is essential to prevent a data crisis in your organization. Data management is made up, first and foremost, of the team that will be stewarding operations. This can include a CDO to ensure top-level management involvement, a program manager to oversee daily activities, and a business analyst to define organizational needs, and ensure that these needs are communicated to developers. 

Data profiling is another key element of data management. Its steps include reviewing the data, comparing it to metadata, running statistical models, and final reporting. Data profiling establishes a starting point for the process and helps the organization set standards. Data repair can follow profiling in the management process. It involves determining why and how the data errors occurred, and the most efficient method to fix errors.

With the vast amount of operational, transactional, and other sources of data that companies generate nowadays, it’s no wonder that they’re looking for a single source of truth to manage data and stay relevant. A BI platform like Sisense can combine data from multiple sources and make sure the data you rely on is always accurate and up-to-date. Then you can spend your time on strategic tasks rather than devoting resources to data warehouse maintenance.

In conclusion

Data quality is like oral hygiene: we all know we need to do it, but sometimes we get lazy. But it’s crucial for higher operational efficiency, to realize cost savings, and a stronger decision-making foundation backed by data. With high-quality data — even faced with rapidly growing volumes — a successful analytics operation is easier to manage and more accurate and will provide dynamic insights for everyone.

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