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How to Prepare Data for Quality Controls

Data preparation is an essential step in data analysis. Many firms or organisations are interested in how to change the data into cleaned forms that may be utilised for high-profit objectives because there is a lot of low-quality information available in various data sources and on the web.

After deciding on a data mining technique, the next step is to access, integrate, extract, and prepare the suitable data set for data mining. Input data must be given in the amount, structure, and format required by the modelling process. In this blog, we will outline the overall framework in which we must represent our data for modelling, as well as the primary data cleaning processes that must be conducted. You will also go through how to investigate your data before modelling it and how to tidy it up. A body of data can be considered highly clean from the perspective of a database. However, from a data mining approach, we must address several issues such as missing data.

Advantages of Data Preparation
Although 76% of data scientists claim that data preparation is the most difficult aspect of their work, effective and accurate business choices can only be made with clean data. Data preparation is beneficial:

  • Quickly correct errors: Data preparation aids in the detection of mistakes prior to processing. These inaccuracies become increasingly difficult to recognise and repair when data has been removed from its original source.
  • Generate high-quality data: Data cleaning and reformatting guarantees that all data utilised in analysis is of good quality.
  • Make more informed business decisions: More timely, efficient, and high-quality business choices result from higher-quality data that can be handled and evaluated more quickly and efficiently.

Data Preparation Steps:

  • Data collection
  • Data production
  • Discovery
  • Data cleaning and validation
  • Data enrichment
  • Data storage

Data collection: We need data for every study as the world gets increasingly data-driven. Data can be pre-defined or pre-generated, or we can build data based on the business problem we are dealing with; nevertheless, keep in mind that if the project took 100 days to complete, data collection or gathering takes around 50-60% of that time. During this phase, we learn the format of data that we have gathered, as well as what kind of information we have in data and what we need to do to produce it in standard format.

This procedure is at the heart of data preparation, and in order to grasp it, we should look at Figure 1.0. As we can see, some data is missing, some is in text, some is useful, and some may be unnecessary, such as the name columns.

Enriching data does not entail adding new data; rather, it implies transforming existing data and transforming it into a relevant manner. It does not involve a format change.
Store the data: The final approach is to store the data in a file, database, or cloud platform and then use it for analysis when needed.

Quality Checking of a Dataset/ data

Business executives understand the importance of big data and are ready to examine it in order to gain actionable insights and enhance business outcomes. Unfortunately, the proliferation of data sources and the exponential rise in data quantities can make maintaining high-quality data problematic. To fully reap the benefits of big data, businesses must first establish a solid foundation for managing data quality by implementing best-of-breed data quality technologies and procedures that can scale and be utilised throughout the company.

Completeness – Where completeness is necessary, there are no missing values.

  • The number of records displayed is an adequate amount of data.
  • All required fields are present.
  • Primary keys are present, distinct, and well-formatted.
  • All foreign key fields are present and formatted properly.

Duplicates – There are no duplicate records.

  • There are no redundant fields.
  • There are no duplicate records across different databases.

Data quality concerns cost businesses millions of dollars each year due to lost revenue possibilities, inability to achieve regulatory compliance, or failing to resolve customer complaints on time. Poor data quality is frequently highlighted as a cause of important information-intensive project failure.


  • All regulations have been recognised and verified.
  • The data has been validated and adheres to data regulations.
  • All field data for the representative data type is appropriately structured.


  • Information is accessible.
  • The data is simple to comprehend.
  • The data is reflective of the desired goals

In this step, the primary job of data exploration or data surveying is to determine whether there is relevant information enfolded in the extracted data set based on the basic structure of the data (s).
The exploration is not concerned with finding a solution to the problem:

This is what Data Mining modelling approaches are for. Basic exploration entails using simple statistical approaches to discover fundamental aspects of collected data: Investigating multi-way frequency tables for nominal characteristics, while examining distributions of values for individual attributes and researching correlation matrices for numeric attributes should highlight major trends in the data.

The following steps are involved in Data Comprehension:

  • Data collection
  • Data description
  • Data exploration
  • Data quality verification

Data comprehension begins with data collection and progresses via actions to become comfortable with the data, find data quality issues, uncover intriguing subsets to create hypotheses about hidden information, or discover early insights into the data.

What do you do with your data once you’ve obtained it? What should you be on the lookout for? What instruments should you employ? Let me brief you with some suggestions for enhancing your data literacy, methods for dealing with numbers and statistics, and considerations for working with messy, imprecise, and sometimes undocumented information. We then learn how to get stories from data, data journalists’ preferred tools, and how to utilise data visualisation to provide insights into the issue at hand.

Assume you are a supervisor at a gold mine and have been requested to study the company’s statistics in relation to your regional sale. You are picking the attributes required for the analysis from the database (for e.g., item, units sold and price). However, there is no recorded value for certain of the properties. You wish to know if each item purchased was promoted as being on sale; but, because some attribute values are missing, we are unable to provide this report. In other words, the data required for data mining analysis is inadequate, incorrect (values that depart from the anticipated), and inconsistent (discrepancies in the data).

The number of occurrences or observations
A minimum of 5000 desired instances, records, or observations are required. If there are fewer observations, the results may be less trustworthy. So, What should you do? To improve the dataset, employ unique boosting algorithms.

Count of fields, characteristics, or records
Every field should have at least 10 occurrences or observations. If there are additional fields, further strategies such as feature reduction and/or feature selection are applied.

Count of target variables
A good rule of thumb is to have more than 100 for each type of data. If the situation gets extremely unbalanced, stratified sampling should be used. You can learn about data sampling in my blog of Sampling : The Simplest term of Statistics

Sumit Kumar

A Data Scientist with more than five years of experience tutoring students from IITs, NITs, IISc, IIMs, and other prestigious institutions. Google Data Studio certified and IBM certified data analyst Data Science, Machine Learning Models, Graph Databases, and Data Mining techniques for Predictive Modeling and Analytics, as well as data integration, require expertise in Machine Learning and programming languages such as Python, R, and Tableau.

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