Get a real picture of your data, its interrelationships, and current and potential issues.

Today’s world is all about connection and connecting, and this involves a tremendous amount of data. By using data profiling and its required continuous review, you can confirm that your data remains high quality and that data integrity is preserved. Plus, the knowledge gained with data profiling can be used to improve overall data quality going forward.

This is done by incorporating a collection of analysis and assessment algorithms that provide verifiable insights into potential data issues.

Data profiling is universally used for data quality processes to support information management programs, including validation, assessment, metadata management, data integration processing, and migration and modernization endeavors. If you’re not applying that same level of rigor to your marketing data, it will absolutely affect your bottom line.

What is data profiling?

Data profiling is a process that reviews source data, content interrelationships, and structural understanding to identify promising data projects.

This process plays an essential role in:

  • Data warehousing and business intelligence by revealing data quality issues within your database and external data sources and identifying needed ETL corrections.
  • Data conversion and migration projects by pinpointing quality issues that can be handled with scripts and data integration tools while replicating data from the source to the target, plus uncovering new requirements for the target system.
  • Source system data quality projects by spotlighting data that has serious quality issues, as well as the source.

The 3 major categories of data profiling

No matter which data profiling process is used, they all have the same goal: To improve quality and to acquire a better understanding of the data.

The techniques include:

  • Structure analysis, or structure discovery: This process confirms that the data is consistent and correctly formatted by using processes like pattern matching that help you understand if a field is text- or number-based. It also provides format-specific information.

    Structure analysis also looks at data fundamentals, so you can gain insight into data validity by using minimum and maximum values, means, medians, modes, and standard deviations.
  • Content discovery: This process takes an intensive look at distinct elements of the database to find areas that contain null, incorrect, or ambiguous values. Inconsistent and ambiguous entries can be fixed via the standardization process involved in content discovery. This means that problems associated with non-standard data can be caught and fixed at an early stage in the process of data management.
  • Relationship discovery gives you a better understanding of the connections between the data sets that are in use. It starts with an analysis of metadata, which shows the key relationships between the data and zeros-in on the connections between specific fields and where there are data overlaps.

    This method can help reduce issues that arise with unaligned data in data sets or in your data warehouse.

Data profiling techniques and best practices

There are both basic and advanced best practices for data profiling and analysis.

Basic techniques include:

  • Distinct count and percent: Handy for tables without headers, this identifies natural keys as well as distinct values in each column that can aid process inserts and updates.
  • Percent of zero/blank/null values: This helps ETL architects identify missing or unknown data to set appropriate default values.
  • Minimum/maximum/average string length: In addition to enabling the setting of column widths just wide enough for the data to performance improves, this technique helps select appropriate data types and sizes in the target database.

Advanced data techniques include:

  • Key integrity: In addition toidentifying orphan keys, this uses zero/blank/null analysis to make sure keys are always present in the data.
  • Cardinality: To help BI tools correctly execute inner or outer joins, this checks relationships like one-to-one, one-to-many, many-to-many, between associated data sets.
  • Pattern and frequency distributions: This is extremely important for data fields used for outgoing communication because it makes sure data fields are formatted correctly.

Why data profiling is important

Data profiling is an essential part of data handling because it aids your understanding of your data, helps you organize it, and discovers issues before they become a problem. It also helps you identify current problems and devising solutions for them.

It helps you to:

  • Discover embedded business knowledge
  • Verify information in tables matches descriptions
  • Reveal relationships between databases, applications, and tables
  • Make sure your data conforms to standard statistical measures
  • Ensure your data meets your company’s business rules
  • Uncover inconsistencies
  • Create standardization rules
  • Make proactive decisions
  • Anticipate crises
  • Organize your data

Data that’s not formatted correctly, standardized, or properly integrated with the rest of the database can create delays and problems leading to missed opportunities, confused customers, and bad company decisions.

To create data quality rules that you use to monitor and cleanse your data, data profiling is a must. It’s also critical to formulating and implementing a data strategy.

The quality of incoming data matters

Finding problems when data profiling your own customer database is one thing, but finding issues with third-party data is another. Finding a partner who provides quality data is important not only for data quality, but to your bottom line.

BDEX: Because quality data is essential

Don’t spend time cleaning up garbage data. The tools in BDEX help you get the quality data you need, right when you need it, which means increased ROI for you.

BDEX data is always clear, high-quality, and current, which isn’t easy to find in the ever-expanding world of big data.

To improve your ROI, you have to get the most out of your data. Otherwise, you’re wasting time, money, and resources by marketing to the wrong audiences at the wrong time with the wrong messages. BDEX can give you the right data you need to better connect with the person behind the data signal.

Make real human connections with BDEX. Contact the team today to get started transforming the quality and accuracy of your data.

Established in 2014, BDEX is the first ever Data Exchange Platform (DXP). Combining hundreds of data sources in real-time into one unique data infrastructure, BDEX offers the most powerful, most accurate identity graph available in the U.S. market. BDEX empowers B2C companies to use the power of data to understand consumer behaviors and intents, helping them reach the right people at the right time.

Contact Us

(917) 410 - 6616
(866) 854 - DATA
New York City
Fort Lauderdale/Miami

Copyright 2020 BDEX - All rights reserved. Privacy Policy