How to Do Data Profiling: A Step-by-Step Guide

EM Admin
Aug 30, 2024
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When you observe data to analyse and review it for summarising datasets, it is called data profiling. Overall, this process is all about verifying the accuracy and relevancy of data so that insights into it can be convenient. Simply put, data profiling aims at measuring the quality of data on the basis of multiple factors like accuracy, completeness, and consistency.

Step-by-Step Guide on How to Profile Data

This guide is split into various steps, which are as follows:

Step 1: Define objectives and scope

Every task aims at achieving a goal. So, deeply consider what you expect from the providers of data profiling services. If you’re not clear about how to decide, simply try to find the answer to these questions:

Why do you want to profile your data? Is it only the quality or data preparation for its migration or integration?

What sources will you prefer for this process?

What do you want to specifically analyze? Could they be data types, models, incomplete values, or inconsistencies?

When you find answers, you discover what truly matters for you in that data. It could be cleansing databases, finding patterns for AI modelling, or mining for intelligence.

Step 2: Collect and Understand the Data

As the previous step is finished, go ahead and gather data from various sources. These sources can be databases, spreadsheets, or data warehouses. As you find, understand what your data amplifies. So, you can start understanding by following these steps:

Find the type of data, like numerical data, PDFs, textual, etc.

Discover the relationships between various data fields.

Determine metadata, such as meta descriptions, columns, and formats, to understand the voice of data.

Experian surveyed and found that 91% of companies believe that bad data quality hampers true insights. So, it is important to profile and understand what your data consists of.

Step 3: Analyse Data Structure and Content

This very next step requires the analysis of data structure and content. Many big companies like Amazon and IBM continuously store a multitude of data in tables, spreadsheets, and servers. And to interpret what that data they have, they use schema designs. This cognitive framework helps in organising and understanding information in their servers. Overall, this profiling-centric analysis emphasises the following steps:

Data Completeness: Completeness here refers to integrating missing details or records in a database. So, incomplete records are filtered to add complementary details.

Data Uniqueness: Unique data entries always feed valuable information. So, the profiling of data helps in scanning dupes or duplicate values in various columns. It prevents wrong conclusions.

Data Consistency: The uniformity in the data structure is always appreciated because it won’t delay understanding it. For example, the data in different formats can be difficult to bring within expected ranges.

Data Validity: The next significant point is validating data. Evaluate the required formats and business rules. Consider various date formats, for instance. There may be variety that can be like these-MM/DD/YYYY, DD/MM/YYYY, YYYY/MM/DD, YYYY-MM-DD, MM.YYYY, MM.DD.YYYY, Month DD, YYYY, DD Month YYYY, Month DD, YYYY, and DD Month YYYY. You can choose the best one for standardising data. This step helps in validating data effortlessly using various rules and functions.

Step 4: Use Data Profiling Tools

Data profiling tools are overwhelmingly available on the internet. Tools automate data profiling, which accelerates the speed of data processing and analysis. On the basis of popularity, these are the most sought-after tools for this purpose:

Talend Open Studio: consists of a series of tools to extract, transform, and load data or continue data mining, which requires transforming and cleaning data into a consistent format. Once profiled, this tool enables loading it into third-party databases and applications.

IBM InfoSphere Information Analyser: This is a server-based analysis facility, which allows data integration to easily understand, clean, track, and transform datasets. Its offerings integrate massively parallel processing (MPP) capabilities, which can be scaled up or down.

Microsoft SQL Server Data Profiling: This tool defines data quality by using data profiles, which are a group of aggregate statistics in rows and columns. This processing takes place within the SQL server environment.

In essence, these tools can provide automatically generated detailed reports about the quality of data, potential threats, and also recommend fixing hacks.

Step 5: Identify and resolve data quality issues

Turning to the next step, which is to analyse the data. It guides you to determine flaws for fixing them.

Missing Data: The very first error that is addressable is completing missing values. This problem in your database can be resolved by employing techniques like mean/mode imputation or machine learning algorithms so that missing values can be restored before analysis.

Duplicate Records: Dupes can infect data-based decisions. So, removing duplicate records is a necessity. SQL queries and data duplication tools can simply win this battle of cleansing.

Inconsistent Data: Standardise inconsistent data formats, such as date formats or currency symbols, to ensure consistency across the dataset.

Outliers: An outlier is an odd value, pattern, or frequency of data that does not match the expected values in a column. Its presence can misguide data analysts. So, these are treated separately.

These are some common quality issues that can infect data-driven decisions. Gartner has proven it in its research, which states that bad quality data leads to an average of $15 million per year in losses for organizations. So, considering these data issues, it is the need of the hour to profile data.

Step 6: Document the Data Profiling Results

Last but not least, the profiled results are documented in a comprehensive manner. It should be categorised as follows:

A summary with complete objectives and scope for profiling

A report in details on findings relating to data quality

A list of quality issues and their solutions

Recommendations for prospective data quality management

These points help in creating a transparent report on data profiling.

Conclusion

Data profiling can help in leveraging data-driven insights effectively. Certain steps can help in profiling data correctly, which are determining objective, understanding data, analysing its structure and content, using profiling tools, and fixing quality issues. Besides, all major steps are explained in the blog, which you can follow and profile any type of data.