Data Profiling, Process and its Tools

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Mark Twain aptly said, “The secret to getting ahead is getting started.” For a successful business, data is one resource that can help an organization that calls for data profiling, which is a technology for discovering and investigating data quality issues.

What is Data Profiling?

In Data Profiling, data assessment is done using a combination of tools, algorithms and rules to create a high-level report.

We can analyse the information that we can use in a data warehouse. Raw data from existing datasets is analysed to collect statistics and informative summaries.

It clarifies the following:

• Structure

• Content

• Relationships

• Derivation rules of the data

Organisations can access data from biometrics and sources like email and electronic medical records.

By running a diagnosis and examining the data, we can actively create a plan to fix many data problems and clean up the data warehouse before they affect the organisation.

Data profiling helps us in the following ways:

1. Understanding of anomalies
2. Assess the quality of data
3. Discover, register and assess the metadata of enterprise
4. Prediction of risks
5. Determining accuracy and validity
6. Eliminating errors such as missing values, redundant values, and those that don’t follow expected patterns

It monitors and cleanses data, improving its quality and giving it a competitive advantage.

Benefits

1. Customer desires can be figured out
2. Customer complaints can be addressed
3.  Business operations
4. Decision Making
5. Customer satisfaction can be improved
6.  Revenue and profits can be increased
7. Problem-Solving

Process

The ETL process stands for extract, transform, and load. Most importantly, It moves quality data from one system to another.

It needs a common repository for storing the results of the data and metadata. Organizations can easily identify the consistency of the data and quality issues and correct them timely, resulting in fewer errors and quality data analysis.

With data profiling in ETL, we can discover if the organisation’s data is:

• Unique

• Incomplete

• Corrupted

• Duplicated

Organisations can then identify patterns and correlations in data and start generating insights.

There are 3 types of data profiling.

• Column profiling – It counts the number of times data values appear within columns in tables.

• Cross-column profiling- Analyse data across columns in tables.

• Cross-table profiling: Analyses tables for similarities and differences in data types across tables. 

Data analysts use the collected information to interpret factors that align with business growth. They follow various steps:

• Collect descriptive statistics, including min, max, count, and sum.
• Collect data types, length, and repeatedly occurring patterns.
• Tag data with keywords, descriptions, and types.
• Carry out data quality assessment and risks of joining data.
• Discover metadata and estimate accuracy.
• Identify distributions, key candidates, functional and embedded-value dependencies, and perform inter-table analysis.

Data Profiling Tools

Tools can analyse any valuable data asset, from big data in real-time to structured and unstructured data. These tools make huge data projects feasible. For instance, company X uses DF tools to identify spelling errors and address data standardisation and geocoding attributes. This information can help them enhance customer data quality, offering a better opportunity.

 Tools are of 2 types:

1. Open source data
2. Commercial Data

Open source data tools are as follows:

Open-source data tools are software applications that are designed to assess and improve data quality.

1. Aggregate Profiler

This is a data preparation tool. It supports profiles for data in RDBMS, XML, XLS, and flat files and integrates with Teeid, MySQL, Oracle, PostgreSQL, Microsoft Access, and IBM DB2 databases.            

  Features are as follows:

1. Data Profiling, filtering, and governance
2. Similarity checks
3. Enrichment of Data
4.  Alerts for data issues or changes
5.  Analysis with bubble chart validation
6. Single Customer View
7. Dummy data Creation
8. Metadata discovery
9. Anamoly discovery and data cleansing tool
10. Hadoop Integration   

2. Quadient Data cleaner

This tool is a complete, cost-effective, plug-and-play data quality solution. It analyses, transforms, and improves the data.      

 Features are as follows:

1. Data quality, profiling, and  wrangling
2. Detect and merge duplicates
3. Boolean Analysis
4. Completeness Analysis
5. Character set distribution
6. Date gap analysis
7. Reference data matching

3. Talend Open Studio

     This tool can help in building basic data pipelines.

   Features are as follows:

1. Customisable data assessment
2. A pattern library
3. Analytics with graphical charts
4. Fraud pattern detection
5. Column set analysis
6. Advanced Matching
7. Time column correlation

Commercial data tools are as follows:

Commercial entities provide commercial data.

1. Informatica

 This tool has the ability to scan every single data record from all the data sources to identify anomalies and hidden relationships. It has the ability to work on highly complex datasets and figure out connections between multiple data sources.

 Features are as follows:

• Data stewardship console, which mimics data management overflow.

• Exception handling interface for business users
• Enterprise data governance
• Map data quality rules once and deploy on any platform
• Data standardisation, enrichment, de-duplication and consolidation.
• Metadata management

2. Oracle Enterprise Data Quality

This tool facilitates Master data management, Data Governance, Data Integration, Business Intelligence and migration initiatives and provides integrated data quality in CRM and other applications and cloud services.

  Features are as follows:

1.  Profiling, auditing, and dashboards
2. Parsing and standardization, including constructed fields, misfiled data, poorly structured data, and notes fields
3. Automated match and merge
4. Case management by human operators
5. Address verification
6. Product data verification
7. Integration with Oracle Data Master Management

3. SAS DataFlux 

This tool combines data quality, data integration, and master data management. Users can explore data profiles and design data standardisation. Businesses can efficiently use it to extract, profile, standardise, monitor and verify the data.

4. IBM Infosphere Information Analyser 

Features are as follows:

• Extracts cleanses, transforms, conforms, aggregates, loads, and manages data

• Supports batch-oriented and real-time Master data Management

• Creates real-time, reusable data integration services

• User-friendly semantic reference data layer

• Visibility into where data originated and how it was transformed

• Optional enrichment components

 This tool evaluates the content and structure of data for consistency and quality. It also helps improve the data’s accuracy by making inferences and identifying anomalies.

• a) Column analysis– each column of every source table is examined in detail

• b) Primary Key Analysis– It enables primary key validation and identifies columns that are applicants for primary keys

• c) Natural Key Analysis- Since the values in the table columns are different, then this method ascertains their uniqueness

• d) Foreign Key Analysis -This is performed in a developer tool. If the values provided in the data match the primary key values in another data set, then the column acts as a foreign key. We can use this tool on multiple objects in the developer tool

•  e) Cross-Domain Analysis -This tool is used to identify columns that have common domain values

https://youtu.be/cXf_F9eGc30?si=1po1YU-Ql2L6NHSl

CONCLUSION

Data profiling is an extremely important step in any business project. It provides accurate project timeline estimates, ensures the availability of high-quality data, and enables data-driven decisions.

Recommended Reads:

• How To Learn Data Science
• 10 Best Python for Data Science Books to Read
• What is Data Science and its Career Path?
• Scope of Data Science in India: Career, Eligibility, Jobs
• Facts About Data Science You Should Know
• What is the future of Data Science & Artificial Intelligence?

Frequently Asked Questions

1. What is Data?

Ans- Data is information gathered through observations, measurements, deep research, and analysis. Graphs, charts, or tables present it.

2. What is the ETL Process?

Ans – Extract, Transform, and Load. It moves quality data from one system to another.

3. What do data analysts do?

Ans- Data analysts use the collected information to interpret factors that can align with business growth.

4. Why do we need tools?

 Ans- Data Profiling Tools can analyse any valuable data asset. They can analyse big data in real-time to structured and unstructured data.

5. Why is Data Profiling important?

Ans- It is important as it provides an accurate project timeline estimate. It ensures the availability of high-quality data and enables data-driven decisions.

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