With the emergence of Big Data, companies have collected large amounts of data, but in order to use this data correctly, it is very important to ensure its quality and implement Data Quality strategies within a company.

Poor quality data can have a significant impact on the organization and lead to poor decisions, as shown in a 2021 report by Experian, in which 95% of company managers reported a negative impact on the business due to poor data quality. These effects range from negative customer experiences to loss of customer trust.

But the good news is that improving data quality can reduce these negative impacts.

But what is Data Quality?

Probably one of the best known definitions of data quality is “fitness for a particular purpose”. However, beyond this simple definition, it is necessary to consider the steps that need to be taken to achieve it.

This definition implies that the data is fit for purpose to achieve business objectives, make informed and effective decisions on an ongoing basis, and optimize future operations.

While this simple definition is a good starting point, it does not inform how to approach improving data quality for individual use cases. More specifics are needed for that. Defining data quality in an operational sense is about understanding the extent to which data quality is fit to serve a particular purpose.

To do this, data quality must be defined in context. For example, if the objective is to identify all paid invoices for May, the definition of high quality data might be data that only represents paid, not outstanding, invoices for that month, with no duplicates and corresponding invoice numbers.

Why is it so important?

The quality of the data will directly impact decision making, the effectiveness of the software you are developing and even the analysis tools.

Let’s imagine we are developing a new marketing campaign based on the location of customers and their latest purchases. If we are not able to know for sure where customers are located, what their last purchases were or even if our database contains duplicate records, we will not be able to benefit one hundred percent from that marketing campaign. This is where having quality data that allows you to make informed decisions comes into play.

The Dimensions of Data Quality

A data quality dimension is a measurable quality or characteristic of data, which will help us define data quality requirements. By controlling these dimensions, we will ensure that our data is reliable and represents reality.

Here we will present the six dimensions you will need to consider to assess the quality of your data:

Accuracy
Data precision measures the accuracy and completeness of particular data. Basically, it assesses the extent to which the information accurately reflects the event or object described.

Integrity
Data integrity measures the number of null or missing values. It can be at the level of individual data, tables or the entire database.

Coherence
When we talk about coherence, we are referring to the uniformity of your data across multiple sources and platforms.

Validity
The validity of your data refers to compliance with defined data formats and constraints.

Opportunity
Assessing opportunity involves measuring the degree to which your data is current and relevant. For example, if you have data on your company’s finances, it is important to know whether it is from last week or 2001.

Uniqueness
Uniqueness of data is the measure of how distinct and non-repetitive the data values are. Or, put in simpler terms, the identification and elimination of duplicate information and records.

Challenges in Developing a Data Quality Strategy

As we have seen, improving data quality is a constant challenge for organizations, and requires the execution of effective practices along with a holistic approach to data management.

Here we show some examples of the challenges we may encounter during the development of a Data Quality strategy and some practices we can implement:

• Large Data volume: In the era of Big Data, companies generate a large volume of data on a daily basis, so managing and ensuring the quality of that data can become overwhelming.
• Diverse data sources: Data comes from a variety of sources, so integrating and validating all this data can be complex.
• Unstructured data: With the growth of unstructured data, such as images, videos or free text, validating and assuring the quality of this data becomes an additional challenge.
• Changes over time: Data can change over time, requiring regular updates and quality monitoring.
• Duplicate data: The presence of duplicates in a database will negatively affect data quality and hinder decision making.

Practices for improving the quality of your data

• Establish clear data governance policies and procedures that address data collection, management and storage.
• Data validation and cleansing to eliminate duplicate, incorrect or incomplete data.
• Automating processes to ensure timeliness and accuracy in updating data.
• Use of metadata to describe and tag data. These can help maintain data integrity.
• Staff training on the importance of maintaining good data quality, i.e., fostering a data culture.
• Continuous monitoring and auditing to detect and resolve data quality issues in a timely manner.
• Collaboration between all areas of the organization to ensure common data consistency and accuracy.
• Periodic evaluations to identify areas for improvement and measure progress.
• Implementation of adequate data security to protect against external and internal threats.
• Updating of technology related to data management and analysis tools in order to improve data quality and usage.

How Keepler can help you improve data quality

At Keepler we have created a methodology for calculating data quality [DATA QUALITY WAVE] to help our clients define a method according to their context and needs that facilitates the adoption of data quality as a key tool on the road to Data & Analytics excellence.

This method is developed in 6 phases, ranging from the initial awareness of the need to formalize the calculation of data quality to that state of excellence in its management and continuous improvement. Thus cultivating a data culture throughout the entire process.

We work hand in hand with the technical and functional teams, advising and guiding them at every step, thus creating a method in which the technical and functional teams of our clients converge. In this way, we obtain a framework that is easily scalable and which adapts to a governed environment.

If you want to know more about how Keepler approaches data quality in governed, multidisciplinary and end-to-end environments, contact us and let’s talk.

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