How to Use Data Profiling to Get an Overview of Your Organization’s Data Quality & Identify Areas of Improvement

Whether you’re aiming for migration to a new CRM, optimizing your data for marketing or implementing data governance, you need to obtain an overview of the quality of your data.

Data profiling is the process of discovering, understanding and identifying inconsistencies in your data. It is the foundation of any data initiative which if implemented rightly will prevent future roadblocks.

In this blog post you’ll get information on:

• What is Data Profiling
• Different Types of Data Profiling
• Benefits of Using Commercial Data Profiling Solutions
• Features of Data Ladder’s Data Profiling Software

Let’s get started!

What is Data Profiling?

In a hyper-connected world, businesses will continue to have data streaming in from disparate data sources. Some businesses will also struggle with legacy data that have severe data quality issues that have not been sorted or optimized for years. New data coupled with old data creates an insurmountable data quality challenge where companies waste a significant amount of resources dealing with messy data problems such as incomplete addresses, invalid information, inaccurate and inconsistent data.

Bad data is on average costing businesses 30 per cent or more of their revenue.

The amount of data is only one side of the coin – the challenge lies in identifying the actual problems within data sets and data sources itself. It’s obvious then that before any attempt is made on correcting data, businesses need to know the exact problems with their data.

Data profiling helps you identify these problems. Think of data profiling as the initial diagnosis a doctor runs on a patient to identify the cause of illness. Similarly, data profiling lets you discover the problems within your data source. Once you know the problems affecting your data, you can then proceed to find out ways to fix it.

Three Kinds of Data Profiling Discovery Processes

Data profiling helps you discover three important aspects of your data. These are:

1. • Structure Discovery: Also known as structure analysis, this process validates the structure of your data – meaning it checks to see if your data has consistent formats across different data sources. For example, does your organization follow the USPS address standards? Are country or city codes added with phone numbers? Structure discovery also makes use of patterns to identify whether a field is text-based or number-based or any other format-specific information. This discovery will give you an insight into whether the text-based field has been filled with numeric data or vice versa. Often it happens that a username may accidentally have a number with it! This minor instance may cause you problems later on when you want to run a marketing or a reach-out campaign.

1. • Content Discovery: This process is similar to structure discovery, but it focuses on the content in the data source. Using this method, you can identify fields that contain null values or values that are incorrect, ambiguous and void. For example, an address field that is left incomplete or an email field that does not have a valid .com address. This procedure is critical in helping you establish content standards and prevent you from making costly mistakes with poor or corrupt data.

1. • Relationship Discovery: Used in the metadata analysis phase where key relationships between data tables or references between cells are studied to ensure they represent one record. For example, if the date of purchase of a customer is not listed or is incorrectly listed it may affect the date of delivery of that same customer! When you’re importing or migrating data from one platform to another, it’s necessary to maintain the same level of dependency and preserve the relationship.

These discoveries are obtained through advanced data profiling techniques that include column profiling, cross-column profiling, cross-table profiling, and data rule validation to provide you with an in-depth overview of your data.

Why You Need Data Profiling – Some Key Benefits

While the primary benefit of data profiling is to discover issues in your data, there are many other benefits as well.

1. Useful in Business Intelligence Projects

Business intelligence relies on accurate data. For example, if you’re planning on running a mass marketing campaign to promote your new product or service, you will need accurate contact information including phone numbers, email addresses, physical addresses, etc. Data profiling saves you from costly mistakes that incur if you make a business decision based on faulty data.

2. Makes Data Conversion and Migration Smoother

One of the key challenges with data conversion or migration is maintaining data quality and ensuring that the new system does not bring along old, outdated data along with it. With data profiling, you can handle bad data within scripts and data sources. It can also help uncover new requirements in the target system, an insight which you can use to optimize your current data to match the new system.

3. Helps Identifies the Source of Bad Data

While you may know you have bad data, you may not specifically know what kind of problem is affecting your data. With data profiling, you can identify whether user input errors such as typos etc are the major cause of bad data or is there a flaw in your data gathering tools (web forms for example).

Benefits of Using a Commercial Data Profiling Solution

While data profiling is important, it is a hectic task to achieve if done manually. You will have to hire data experts with SQL knowledge to create and test algorithms to get results – a process that could take months of effort and hundreds of thousands of dollars in recruiting and retaining the right talent. On the technical end, the manual approach only tests a subset of attributes which means you cannot get a thorough evaluation of your data.

It is, therefore, a more feasible option to opt for commercial data profiling solutions that outweighs in-house solutions in terms of money, effort, and results.

Let’s look at some important reasons why you should consider a commercial solution.

1. Large Volumes of Data Across Multiple Data Sources

For enterprise-level businesses with a large volume of data, it makes sense to use commercial data profiling solutions due to the complexity of data sources. Moreover, if you have multiple data sources and channels, you’ll need a solution that can process all this data efficiently while ensuring accuracy. With a commercial solution, all you need to do is integrate your data source into the platform and identify errors right within your data source.

2. Save Up on Time, Money and Effort

Your IT team’s time could be utilized at other important facets of the organization – such as planning a migration, monitoring transitions, etc. Hiring resources to do something in-house which can be done at half the cost through an automated solution is a waste of time and money. With a commercial solution, you can profile and analyze data within just hours instead of days or months.

3. Helps Identify Issues Across Your Data Sources

When you plug in multiple data sources into the software, you’ll be able to identify issues with your data across the board. Furthermore, you will also be able to identify issues between all data sources – for example, in one go you can identify issues with your customer data, vendor data, product data and if they are all interrelated you can identify the problems with each of them! This is a feature that is not easily achieved through manual methods.

4. Speed, Efficiency & Timely Results

The industry standard for manual profiling is at least 3 to 5 hours per attribute – a data profiling software or tool uses just 15 – 30 minutes per attribute. A profiling project that would be on the backburner taking up days and months can now be achieved within just a few hours. Accuracy, efficiency, timely results all delivered.

5. No Requirement for Language Experts

Most data profiling tools are user-friendly visual interfaces that do not require experts with language knowledge to perform the task. You can nominate any relevant individual within the department to operate the software and perform profiling of your data.

Most companies today opt for automated solutions over manual solutions and use their data experts in other important critical analysis processes.

Going Beyond Data Profiling with Data Ladder’s DataMatch Enterprise

A commercial data profiling tool offers much more than just profiling. For example, Data Ladder’s DataMatch Enterprise tool is a fully powered data quality solution that offers data profiling as the first of many steps in correcting, optimizing and refining your data.

Watch this video to see how you can perform data profiling on DataMatch Enterprise.

Along with data profiling, the software lets you perform:

Data Matching: Once you know the errors in your data, you’ll want to move forward with removing redundancies and performing data matching to obtain an accurate view of your customers. Without data profiling, data matching will fail to deliver optimal results. In fact, you’ll have a hard time verifying your data if you don’t run your data through the profiling phase.

Data Cleansing and Standardization: You can correct data inconsistencies right within your data source with our cleansing and standardization option. What do we mean by this? If your phone numbers have punctuations or letters in them, which you identified during the profiling phase, you can use this cleansing option to correct the data.

Address Verification: Addresses remain one of the most significant challenges when it comes to dirty data. DataMatch Enterprise specifically allows for address quality correction and matching against the USPS address system.

With these core functions, you also get the ability to integrate your data source with an on-premises software that can be deployed on your server. This means your data is secured and is fully in your control. The integration ability makes it easy for you to perform ETL operations on the data source itself without having to go back and forth.

Conclusion

The whole purpose of data profiling is to help you get an idea of problems within your data before you invest in a major data initiative. While you may want to get an in-house solution, it’s important to note that data profiling is just the tip of the iceberg. You’ll need to take your data through a transformational journey that will include cleansing, optimizing, merging, standardizing and creating a reliable source of truth.

A full-fledged software like DataMatch Enterprise will ensure that you move through this journey seamlessly.

Ready To Start Profiling Data and Grow Your Business?