Data processing and analysis are not possible without data profiling, or, in other words, examining data for quality and content. With the increase in data volume, guaranteeing quality is a continuous challenge for companies seeking to use data to drive decision-making.
The amount of time and resources is limited as usual, but do you still need to achieve big data profiling? Say no more.
What is data profiling?
Data profiling is the process of examining data from an existing information source (e.g., a database or a file) and compiling statistics or helpful summaries for that data.
Data profiling has the potential to remove errors typical for databases. The errors such as inaccurate or missing values, values out of range, unusual data patterns, and so on, that often become costly in the end.
Data profiling entails the following procedures:
- Collecting descriptive statistics such as minimum and maximum values, count of values, any other attributes that can be used to define the basic data properties.
- Assessing data quality.
- Recognizing data types, recurrent patterns, etc.
- Data tagging with descriptions and keywords.
- Organizing data into categories.
- Identifying the metadata and assessing its accuracy.
- Analyzing inter-table correlations.
- Recognizing functional dependencies, key candidates, embedded value dependencies, distributions, and so on.
Examples of Data Profiling
Data profiling has a wide range of applications in businesses looking to understand and manage their data in the best way possible. Let’s take a look at the examples below.
Data Warehousing (DW)
When a company creates a data warehouse, it is attempting to collect data from different sources and store it in a standardized format appropriate for the analysis. However, if the data is of low quality, putting it all in one place does not solve the problem. Bad data just makes everything worse.
Incorporating data profiling into the data warehouse workflow can help protect from bad data. Data profiling can be used to assess the data’s integrity and compliance with data rules before or during the intake process as information is gathered.
Source system data quality
Data profiling can identify data that has potential quality issues (e.g. errors in interfaces, user inputs, data corruption).
Data migration and conversion
Data profiling is necessary for accurate data migration because data quality in legacy systems is often lower than business users assume. Once data quality issues are detected, they can then be addressed through scripts and data integration tools that replicate data from source to target. It may also reveal new needs for the target system.
The deliverables of data profiling
Here are the deliverables of data profiling according to Ralph Kimball, the pioneer of data warehouse architecture:
- A simple “Go – No Go” decision on the entire project! Data profiling may demonstrate that the data the project is based on simply does not provide the information needed to make the required decisions.
- Data quality issues originating from the source system must be resolved before the project is started. Although not always as severe as canceling the entire project, these adjustments are a significant external reliance that must be effectively handled for the data warehouse to succeed.
- Data quality issues can be addressed after the data has been extracted from the source system in the ETL processing flow. Understanding these concerns is key to designing the ETL transformation logic and methods for handling exceptions.
- Unexpected hierarchical structures, business rules, and FK-PK key relationships. Understanding the data in depth eliminates the flaws which permeate the architecture of the ETL system.
Best methods for data profiling
When it comes to profiling and analyzing data, there are basic and advanced best practices.
Basic techniques for data profiling include:
- The minimum, maximum, and average string length facilitates the selection of acceptable data types and sizes within the target database. You can configure column widths large enough that the data increases efficiency.
- Separate count and percentage – identify natural keys, distinct values in each column that can help in processing inputs and updates.
- The percentage of zero, blank, or null values identifies missing or unknown data and assists ETL architects with defining acceptable default parameters.
Advanced data profiling techniques:
- Key integrity — using zero/null/blank analysis, assures that keys are always present in the data. It also assists in the identification of orphan keys that are problematic for ETL and future analysis.
- Pattern and frequency distributions — checks if data fields are properly formed and whether emails are in a valid format. This is especially crucial for data fields used in outgoing communications (emails, addresses, telephone numbers).
- Cardinality — compares one-to-one, many-to-many, and one-to-many linkages across related data sets. This enables BI tools to accurately conduct inner or outer joins.
Types of data profiling
There are three types of data profiling: structure discovery, content discovery, and relationship discovery. Let’s go through each of these in further detail.
Structure discovery is the process of ensuring that data is consistent and correctly formatted.
Pattern matching is a common approach for structure discovery in which data engineers evaluate records against known patterns for data kinds. For example, pattern matching may scan a column of email addresses to ensure they all include “@” and end with a domain suffix.
Structure discovery also computes basic statistics such as mode, median, mean, and standard deviation based on numerical data.
Content discovery searches for apparent issues like missing values as well as more subtle concerns like inaccurate or ambiguous data.
Relationship discovery can involve references inside a database, such as a cell value populated by calculating other cell values, or references across tables and data sets, such as foreign and primary keys.
5 data profiling applications
Data profiling may be automated with tools, making large data projects more practical. Here are a few of them:
Informatica Data Quality
Informatica Data Explorer offers several profiling and data quality solutions that enable companies to perform more comprehensive and faster data analysis. This tool can quickly analyze all data records from multiple data sources for hidden anomalies and correlations. It has several preset rules that can be used to profile structured and unstructured data.
Informatica offers a free 30-day trial, after which consumers are required to start paying.
SAS DataFlux Data Management Server
SAS DataFlux Data Management Server integrates data quality, data integration, and basic data management. It has high-performance environments that allow users to construct and analyze data profiles, design data standardization schemes, and so on.
The final price is determined by the company and the data needs and can be settled by contacting the SAS commercial team.
Aggregate Profiler
Array Technology’s Aggregate Profiler is a free open-source tool.
It includes advanced data profiling techniques including metadata discovery, pattern matching, and anomaly detection. Furthermore, Aggregate Profiler covers numerous activities other than data profiling, such as masking, reporting, integration, encryption, and even the production of fake data for testing.
SAP Business Objects Data Services (BODS)
SAP Business Objects Data Services is among the most widely used Data Profiling applications on the market. It can assist businesses in doing an in-depth analysis to uncover anomalies in their data as well as other data issues. It combines capabilities like Data Quality Monitoring, Data Profiler, Metadata Management, and others into a single tool. It can quickly do pattern distribution, redundancy checks, and cross-system data dependency analysis, among other things.
The pricing of BODS is individual and is determined by the data requirements.
Melissa Data Profiler
Melissa Data Profiling allows companies to do a variety of operations such as Data Profiling, Data Verification, Data Enrichment, Data Matching, and so on. It is quite easy to use and can be used to effectively review data in various forms while doing content analysis, general formatting, etc.
The final pricing is determined by your requirements and may be settled by contacting Melissa’s Sales team.
Modern times require modern solutions
As described in this piece, traditional data profiling is a complex operation carried out by data engineers before and during data warehouse import. Before entering the pipeline, data is thoroughly evaluated and processed.
More businesses are migrating their data architecture to the cloud and learning that data import can be accomplished within a click. Hundreds of data sources are pre-integrated into cloud data warehouses, data management tools, and ETL services. But, if you can transport data into your target system with the touch of a button, what about data profiling?
An automated data warehouse that can handle data profiling and preparation on its own is required in a cloud-based data pipeline architecture. Instead of analyzing and processing the data using a data profiling tool, simply transfer it into the automated data warehouse, and it will be refined, optimized, and prepared for analysis automatically.
Sage Data provides a smart data warehouse, which employs artificial intelligence (AI) technology to automatically process incoming data, visualize it, and prepare for analysis in mere seconds.
Leverage your data to make smart decisions!
