The value of your data depends on how well you organize and analyze it. As data gets more extensive and data sources more diverse, it becomes essential to review it for content and quality. However, only about 3% of data meets quality standards, which means companies with poorly managed data lose millions of dollars in wasted time, money, and untapped potential.
That is where Data Profiling comes in — a powerful weapon to fight against bad data. It is the act of monitoring and cleansing data to improve data quality and gain a competitive advantage in the marketplace.
In this article, we explore the process of data profiling, its definition, tools, and technologies, and look at ways how it can help businesses fix data problems.
What is Data Profiling (DF)?
It is the process of examining source data and understanding structure, content, and interrelationships between data. The method uses a set of business rules and analytical algorithms to analyze data minutely for discrepancies. Data Analysts then use that information to interpret how those factors can align with business growth and objectives.
Data profiling is increasingly vital for businesses as it helps determine data accuracy and validity, risks, and overall trends. It can eliminate costly errors that usually occur in customer databases, like missing values, redundant values, values that do not follow expected patterns, etc. Companies can use the valuable insight gained from data profiling to make critical business decisions.
Most commonly, it is used in combination with an ETL (Extract, Transform, and Load) process for data cleansing or data scrubbing and moving quality data from one system to another. An example can help you understand what is DF in ETL. Often ETL tools are used to move data to a data warehouse. Data profiling can come in handy to identify which data quality issues need to be fixed in the source and which issues can be fixed during the ETL process.
Data analysts follow these steps:
- Collection of descriptive statistics including min, max, count, sum
- Collection of data types, length, and repeatedly occurring patterns
- Tagging data with keywords, descriptions, types
- Carrying out data quality assessment and risks of joining data
- Discovering metadata and estimating accuracy
- Identifying distributions, key candidates, functional and embedded-value dependencies, and performing inter table analysis
Data Profiling Tools
With the help of DF tools, you can analyze any data asset valuable to your business – from big data real-time data to structured and unstructured data. These tools can make massive data projects feasible in no time.
Some of the best DF tools are:
IBM InfoSphere Information Analyzer
This popular DF tool enables users to assess the quality, content, and structure of data. Key features include:
- Column analysis – each column of every source table is examined in detail
- Primary Key Analysis – enables primary keys validation and identifies columns that are applicants for primary keys
- Natural Key Analysis – allows profiling the uniqueness of different values in the columns of a table
- Foreign Key Analysis
- Cross Domain Analysis
SAP Business Objects Data Services (BODS) for Data Profiling
One of the best DF tools and ETL software solutions, SAP BODS allows users to quickly identify data inconsistencies and problems before turning them into business intelligence and actionable insights. One main feature of the tool is that it combines data quality monitoring, metadata management, and DF in one package. With SAP BODS, users can perform:
- Column Profiling
- Relationship Profiling
Informatica DF and Quality Solution
One of the popular DF techniques, Informatica solutions, offers very fast DF in the repository and provides in-depth analysis. It comes with automated discovery capabilities, which help minimize specification and testing cycles for the IT team and also support data governance procedures.
DF with Talend Open Studio
A suite of open-source tools, this ETL software provides advanced data analytical tools without having to write any code. Key features include:
- Customizable Data Assessment
- Free to Download
- Fraud Pattern Detection
- Analytics with Graphic Charts
- Column set Analysis
- Time column Correlation
Oracle Enterprise Data Quality
The main features are:
- DF. Auditing and Dashboards
- Standardization of created fields, incorrectly filed data, poorly structured data, and notes fields
- Automation of match and merge
- Human Operators for Case management
- Address verification
- Product data verification
- Integration with Oracle Master Data Management
There are various other DF methods in the market. The best fit for your business depends on factors like your business goals and strategy, data quality cost, to name a few.
Data Profiling Examples
Some DF examples in use today can be to troubleshoot problems within huge datasets by first examining metadata. For instance, you can use SAS metadata and data profile tools with Hadoop to identify and resolve issues within the data to find those data types that can best contribute to innovative business ideas.
SAS data loader for Hadoop enables business users to profile Hadoop data sets using a visual interface and store the results. The profiling results in data quality metrics, graphic procedures, metadata measures, and other charts that facilitate the assessment of data and enhance data quality.
DF tools can have real-world effects. For instance, the Texas Parks and Wildlife Department used the DF features of SAS data management to improve customer experience. They used DF tools to identify spelling errors, address standardization and geocoding attributes of data. The information thus collected helped to enhance the quality of customer data, offering a better opportunity to Texans to use the vast acres of parks and waterways available to them.
Data Profiling Best Practices
There are three distinct components:
- Structure Discovery – it helps to determine if data is consistent and has been formatted correctly. It uses basic statistics for information about data validity.
- Content Discovery – data is formatted, standardized, and correctly integrated with existing data efficiently and on time. For example, if the street address is wrongly formatted, there’s the risk of delivery getting misplaced or difficulty reaching customers.
- Relationship Discovery – identifies relations between various data sets
Basic DF Practices Include:
Distinct count and percent – this technique identifies natural keys and unique values in each column that can help in case of inserts and updates. It is appropriate for tables without headers.
Percent of zero or blank or null values – users can use this practice to identify missing or unknown data. ETL architects set up default values using this approach.
Maximum, Minimum, average string length – used to select suitable data types and sizes in the target database. Column widths can be set just wide enough to hold data to boost performance.
Advanced DF Practices Include:
Key Integrity – makes sure data always contains keys, using zero/blank/null analysis. It helps classify orphan keys, which can cause a problem for ETL and future analysis.
Cardinality – used to check relationships between related data sets such as one-to-one, one-to-many and many-to-many. This enables BI tools to perform inner or outer data joins appropriately.
Pattern and Frequency distribution – this practice enables checking if data fields are correctly formatted. This is very important for data fields used for outbound communications like emails, phone numbers, and addresses.
DF in Data Warehousing
In today’s cloud-based data pipeline architecture, there’s an even higher prevalence of unstructured data. Automated data warehouses are used to tackle DF and preparation on their own. Instead of using a DF tool to analyze and for data quality management, analysts feed the data into an automated data warehouse where the data automatically gets cleaned, optimized, and prepared for analysis.
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