Important metrics to measure data quality before building any model

There are several checks need to be done to ensure the quality of data in use before building the model.

Design by Analytics India Magazine

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Data quality is used to describe the usefulness of the information obtained. Data is available everywhere and it grows linearly with time. Data is a major fuel to proceed ahead with the implementation of any data science and machine learning model building. It is crucial to have the right data to come up with reliable models for any task. So this article provides a brief overview of some of the important metrics to assess the quality of data to be used. These metrics are crucial and must be experimented with to measure the data quality before building any model.

Table of Contents

  1. Data – An overview
  2. The necessity for assessing the quality of data
  3. Data Quality Evaluation metrics
  4. Summary

Data – An Overview

As mentioned earlier data is a certain set of information available which will be majorly of two types namely Qualitative and Qualitative. As the name suggests Qualitative data is data that mainly signifies the characteristics and it is not measurable while Quantitative data is data that can be measured or quantifiable and be represented in certain units.

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There are other broad classifications under the umbrella of qualitative data as nominal and ordinal data and quantitative data as discrete and continuous where each individual classification has specific characteristics.

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The below image represents the pictorial categorization of data and its types.

The necessity for assessing the quality of data

So before looking at the metrics for assessing the data quality let’s have a look at why data quality assessment is crucial. Quality data shows signs of reliability and helps us to achieve better decision-making for any task. Quality Data and Quality Decision making go hand in hand and as mentioned Data is the major fuel. 

Data Quality in short can also be termed DQ. Higher the Data Quality better would be the solutions delivered. Moreover, when the Data Quality is high the machine learning algorithms implemented tend to work better and show faster, more accurate, and reliable results. So as mentioned earlier if the Data Quality is low it would lead to unreliable results.

For example, let us consider working for a business firm. So for businesses, quality data or in simple terms can also be termed proper or accurate data is crucial. Say suppose the quality of data is not considerable, we would end up with wrong business solutions or lead to the loss of business or higher cost of operations for the firm by making wrong decisions.

Considering all these factors we can say that before taking up any decision making it is very crucial to assess the data quality.

Data Quality evaluation metrics

We have already seen the importance of Data Quality in the earlier sections and now let’s focus on some of the important data quality evaluation metrics.

Among various metrics of data, the most important qualities any data should have is listed down below. They are:

  • Validity
  • Accuracy
  • Completeness
  • Consistency
  • Uniformity
  • Relevance

Now let’s have an understanding of these metrics one by one.

Validity of Data

As the name suggests quality data goes hand in hand with appropriate/valid data collection. It is easy to collect huge amounts of data, but it is relevant to collect or utilize valid data for better insights. Nowadays collection of valid data is easy by setting up certain constraints while data collection not only helps us in obtaining relevant data and quality data but also helps us to reduce data storage costs and computation time.

But in the current era of the massive growth of data, the validity of data initially cannot be expected sometimes, but valid data can be obtained by performing necessary data cleaning and also understanding from the clients the most valid data and how each feature of data is important in order to come up with appropriate business solutions.

So as mentioned valid data is directly related to meaningful and required data and also chain linked with appropriate inferences being made.

Let us understand the metric of validity of data from the above-mentioned example. So for business firms validity of data plays a crucial role with respect to quality data collection wherein the data type must be appropriate for example amount should be numeric and the account number should be categorical. Also, the validity of data comes under acquiring data under a proper range/scale and invalid formats. Suppose the shipment date for the business firm should be in the proper format of MM-DD-YYYY.

Accuracy of Data

In simple terms, let’s term the accuracy of data as the right data available. So accurate data depicts the right set of information under each of the features. So considering the previous metric of data validity in short it can be summarized as valid data with accurate information that helps us to obtain the right solutions and the other way round would lead to unreliable solutions and serious consequences as the solutions provided would be wrong due to the inaccurate data. So it is very important to have accurate data in order to provide effective solutions.

So understanding the accuracy of data with respect to business firms, data obtained has to be accurate to evacuate the possible outcomes of faulty predictions which in turn leads to wastage of money and resources causing serious consequences.

Completeness of Data

Completeness of data means whether we have all the required information to provide reliable solutions. So once the above-mentioned data quality parameters are addressed, that is once when valid and accurate data is obtained we have to look into obtaining complete information from the data. Data completeness helps us in easy accessing and retrieving data required at any point in time and moreover, it is a tedious task to handle incomplete data as it might require subject matter expertise in the respective domain to ensure completeness of data.

So to understand the completeness of data with respect to business firms, the data has to be complete in terms of no presence of missing values or missing data records. So if a business firm wants to analyze its frequent customers and if there is a presence of missing information which is very crucial to analyze the frequent customers it would lead to a faulty prediction or unreliable prediction. So in this way, we can say that completeness of data is a crucial factor for data quality assessment.

Consistency of Data

Consistency of data can also be termed reliable data. So the consistency of data is also one of the important data quality metrics, unlike others. So consistent data means the data which do not change abruptly and turn out to be unreliable. Similar to the other data quality metrics it is important to have consistent or reliable data because if data is inconsistent it would lead to wrong business decisions and solutions.

So to understand the consistency of data with respect to business firms, data consistency goes hand in hand with proper and consistent data governance. The data has to be governed appropriately and made sure all the users see the same data at a given point in time.

Uniformity of Data

Data uniformity basically suggests the data on a common scale of comparison for all the information available. Uniform data helps us in merging data from different sources flawlessly and also uniform data helps in easy retrieval of data as required. Uniform data also helps us in effective data analysis.

So to understand the uniformity of data for business firms, data available or data governed should have a high quality of uniformity or it should be on a common scale to make the right predictions. Absurd data quality can lead to faulty predictions and severe consequences.

Relevance of Data

Relevance of data or relevant data in any domain is a subjective talk as in each domain certain features might stand highly relevant and some may not. So relevant information in any domain can be deduced by subject matter expertise in the particular domain of work. It is unnecessary to keep irrelevant data as it simply shoots up the storage cost of data and also considering irrelevant information would turn up to no solutions or irrelevant solutions produced. 

Along with relevant data, one more aspect to be kept in mind is the time period of the data collected. For certain applications, it is unnecessary to keep very older data because suppose if any of the individuals are performing time series analysis the past 5 to 10 years of data would be more relevant rather than the complete data available over a period of time and may also lead to abrupt trends and seasonality in the series. So relevant data and the time period of data are crucial data quality parameters.

So to understand the relevance of data for business firms, very old data or historical data for business firms may not be useful to deliver business-required solutions. So relevant data with a considerable time period would help in yielding the right solutions rather than having irrelevant data and very old data as it may possibly lead to faulty trend analysis for time series analysis.s


In short, data quality and some of the metrics mentioned above are the most important factors to be considered for effective data-driven solutions. Higher the data quality better is the solutions produced by any individual firm. So quality data can be assured by adhering to the existence of the above-mentioned metrics and effective data cleansing. On a whole, data quality can be classified into two aspects of subjective and objective talks where objective talks include clean data without missing values and free of errors and subjective talk includes whether the acquired set of information is relevant for the tasks.

Data quality assessment goes hand in hand with other data governance operations such as data profiling, data analysis, and reporting. So it is very essential to evaluate the important metrics of data quality as mentioned above to deliver suitable insights.

Darshan M
Darshan is a Master's degree holder in Data Science and Machine Learning and an everyday learner of the latest trends in Data Science and Machine Learning. He is always interested to learn new things with keen interest and implementing the same and curating rich content for Data Science, Machine Learning,NLP and AI

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