Data Quality and Data Integrity: How are they different and their importance?

The difference between Data Quality and Data Integrity

Data Quality and Data Integrity play a distinctly significant role in maximizing the accuracy of datasets.  Source: Echo Internal Source. Created on December 19, 2022.

Data is a valuable asset for any company as it helps with informed decision-making and enhances the company’s capability to respond to changing market trends faster. Companies can better understand their customers, detect business opportunities, analyze competitors’ performance, and manage their marketing and sales strategies. But for all of this, it is necessary that companies are able to acquire datasets that are of high quality and maintain integrity.

Although the terms data quality and data integrity are often used interchangeably, it is important for companies to understand the difference between the two. That is because the parameters and metrics that define them are different, and they can have distinct impacts on the business. 

To understand their impact better, let’s look at the two terms separately.

What is Data Integrity?

Data integrity determines how reliable a dataset is. 

It is the process that ensures data remains accurate and consistent throughout its lifecycle. If we consider the term ‘integrity’ alone, it signifies the state of being complete or being in an unimpaired condition. Similarly, when a dataset has no missing values, contains all the information that is expected out of it, and is easy to read and analyze, it is considered to be of high integrity.

What is Data Quality?

Data quality determines the usability of a dataset. 

It is a process to ensure that the dataset is capable of serving the intended purpose such as decision-making, planning, and operations. The term ‘quality’ determines a degree of excellence, where there is little error and the outcome satisfies a set of requirements. On a similar note, data quality ensures that a dataset is complete, has all the attributes, and the information can be used to address real-world situations.

The difference between Data Quality and Data Integrity:

Both data integrity and data quality are based on several factors that help them stand out. However, it is interesting to note that while data quality is a determining part of data integrity, it is not the only determinant.

The three factors that monitor data quality and integrity are:

Data Integrity

   – Accuracy

Accuracy determines how closely the dataset’s information matches the real-life value. An accurate dataset shows you or gives you information as it is, for example, if a building is painted red then the dataset should also tell you that it is red. Data accuracy plays a pivotal role in developing business operations such as planning, budgeting, and forecasting.

   – Consistency

The term ‘integrity’ itself denotes a state of internal consistency. Consistency has a lot to do with the content of the data rather than its structure, which means it requires attention to detail. For example, if the entire dataset follows the format Street No_Street Name throughout but only one row shows an altered format Street Name_Street No, it can lead to an inaccurate analysis of the dataset.

     – Context

To put something in context means to put it into a situation so that we can explain why it happened. Similarly, a dataset is only useful when it has a purpose to fulfill. Otherwise any dataset – no matter how clean or how accurate – are after all just a collection of numbers. Datasets can only retain their integrity when they have met that contextual utility.

        Data Quality

   – Freshness

A high-quality dataset will always ensure that its information is up-to-date. Stale data is misleading as it will not provide the most recent information and hence can prevent businesses from reacting to what is happening right now. Good quality data maintains that timeliness.

   – Validity

Data is only valid if it follows a specific format. For example, if the format of a place’s name is Place_City_Country, then the data that is added in the format Place_Country_City will be considered invalid. Validity ensures that the dataset maintains a consistent structure for quicker, accurate analysis. 

     – Completeness

Data completeness maintains that a dataset has no missing information. It is comprehensive and can include all the attributes that are necessary to make an analysis when the data is put into context. Incomplete data can result in costly mistakes as they only provide partial information and assumptions. 

Which is more important? Data Quality or Data Integrity

Data integrity and data quality are both equally important. While it is essential to recognize the difference between the two, it is also vital to know how the two are interrelated. 

Data integrity cannot exist without data quality. Data integrity is where the process of achieving good-quality data begins. It is in this step of the process that you check the data for accuracy, that it is consistent throughout the set, and has a context as to why someone would need that data. Data quality on the other hand is the milestone that you want to reach through a vigorous process of updating your data, putting it into understandable formats, and ensuring that it has no missing information. 

Data integrity and data quality aren’t optional process but are necessary to attain reliable data. 

How does Echo Analytics ensure Data Quality and Data Integrity?

Our detailed methodology has not only scaled up our datasets within a time span as short as six months but also maintained high-quality standards. Our datasets come with assured integrity that they are easily understandable, complete with all the necessary information, and have been used by businesses across several industries – assuring their multiple contextual utilities. You can find out in detail more about how we structured our data integrity and quality methodology, in the video below.

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