Good data is the backbone of most effective business decisions and strategies. If you're looking to complete a business project and don't have an existing data set that shows current performance and areas where you’re falling short, data profiling could help fill in the gaps.

Data profiling is the process of examining and reviewing the structure, interrelationships and content of current data to better understand what you have and what other purposes or areas of the business you can use that data for. Like product inventories in a retail store or warehouse, data profiling helps you create a digital inventory of your datasets.

What Is Data Profiling?

Data profiling is a process of reviewing and analysing diverse datasets across the business to inform business decisions. As your business grows and evolves, it will generate large amounts of data around customer purchase history, business spending history, accounting and finance, operating metrics and more. Without data profiling, some potentially useful and valuable data could get pushed to the back of the virtual filing cabinet, out of sight and out of mind, and its potential value is lost.

By using data profiling, your business creates a useful, searchable inventory of your business data. This can help create better and more useful business intelligence reports, improve efficiency and costs, and lead to better profits in the long term.

When data profiling, your team or systems intelligently examine existing data to understand the data structures and any potentially related data. Data profiling may also include cleansing and updating data sets to work with modern systems while removing superfluous or corrupt data that is no longer useful.

Key Takeaways

  • Data profiling is the process of evaluating and organising existing data for future use using business processes, algorithms and technology.
  • Data profiling can help you discover links between disparate datasets useful for business intelligence projects and long-term planning.
  • Growing businesses should employ data profiling and use a robust ERP to evaluate business data.

Data Profiling Explained

To get a better idea of the value of data profiling, let’s consider a telecommunications company. Telecom companies are swimming in data, and the volume of it grows daily. Like a lot of other businesses, these companies have customer data, vendor data, employee data, real estate data, regulatory data and financial data. As an industry that helps shuttle data worldwide, it also has information sets on customer usage, peering (connections to other telecom companies), storage, data centres and other industry-specific information.

That’s a lot of information to digest. Thankfully, we live in an era of machine learning, artificial intelligence and powerful platforms that can help us tie all of this data together and better evaluate what we have and how it may be useful.

How Does Data Profiling Work?

There are a few ways to approach data profiling. You may find one strategy works best for your team, or you may prefer a combination:

  • Manual Data Profiling: This involves going through databases the old-fashioned way and manually creating a listing of your data.

  • Automated Data Profiling: Automated data profiling uses systems, AI and machine learning to handle much of the work around data profiling. This often complements a manual data profiling plan, as computers may not perfectly parse and understand everything in your business data.

  • Expert Data Profiling: If your data is too much for your company to handle or this seems to be outside of your areas of expertise, you can hire experts to consult and help you with data profiling, or just do it all for you.

Why Profile Data?

Data profiling is useful for many reasons. To start, it may reveal some small cost savings with better data retention and management policies in place. But in the long run, you can use this data to build a competitive edge and boost the bottom line.

Every database likely has valuable information that can help your business. You can optimise pricing, operations, delivery, purchasing, hiring and more. Most businesses work to deliver the best customer experience, a great employee experience and maximum shareholder profits. Data profiling can help with all of those critical business goals.

Why Is Data Profiling Important?

Data profiling is important because it can help a business improve profits and cut waste. Just as grocery stores have to conduct a regular inventory count to know what and how many products are sitting on the shelves, most businesses should make an effort to understand what data is sitting on their servers: cleansing, organising and verifying it when necessary.

Why Do Companies Need Data Profiling?

You may find a database that contains an important insight that helps you beat a regional competitor — but in today’s environment, it’s table stakes. You could discover an inefficiency in your factory, costing you a small fortune, and the data points to a quick fix. You can use data to improve your marketing plan or change the geographies on which your sales force focuses. The use cases are endless, but as data and data sources grow and the need for data warehouses develops, you likely won’t get the best results without data profiling.

Types of Data Profiling in Business Analytics

There are three main types of data profiling to go through when starting your data profiling process:

Structure Discovery

Structure discovery involves evaluating the various datasets available to a business and how they are formatted. In structure discovery, you’ll find the number and type of fields and what is contained within each.

Content Discovery

Content discovery is the process of examining each database’s individual fields and elements to check the contents and quality.

Relationship Discovery

Relationship discovery is an analysis of how databases connect. You may find that data sets from completely unrelated parts of the business could share a common field and produce meaningful results.

Benefits of Data Profiling

Ultimately, the biggest benefit of data profiling should be higher profits. That comes from a combination of improved business efficiency, enhanced insights and new strategies derived from the data.

Just as your business may not have its own staff of financial planning and analysis (FP&A) experts on standby, you may not require a permanent team of data scientists. But with good data profiling in place, the rest of your team may be capable of doing quite a bit of useful analysis.

Data Profiling Techniques

Data profiling relies on several techniques and methods to catalogue, clean and validate the data you have. Popular methods include:

Column Profiling

Column profiling is a good first step in data profiling. For example, properly labelling and notating ZIP codes, phone numbers and product purchase histories enables you to match datasets with common fields using the same formatting for easier use in the future.

Cross-Column Profiling

Cross-column profiling is the next step, and it helps you look for relationships between different columns or fields in the same data table.

Cross-Table Profiling

Cross-table profiling moves up one level to look at the types of database tables you have in storage. Knowing the types of data available, the size of each data table and how the tables relate to each other expands opportunities for analysis. You might find additional commonalities you can use to drive additional insights.

Data Rule Validation

In this step, the focus is to standardise and cleanse the data. This makes machine learning and business intelligence systems even more useful, as they can better understand and evaluate information across disparate datasets.

Real-World Data Profiling Examples

Because data profiling can be complex, here’s an example based on a real-world situation. Let’s say you are the owner of a wholesale distribution company that recently acquired a sizable competitor. While your new business will be bigger, you have two big datasets to understand and merge.

In the first stages, data profiling would create an inventory of databases between the two companies. Next, the data teams would work to standardise and find overlaps. Finally, it can be cleaned up and merged into a single data source on which to base decisions moving forward.

Without good data and information, it’s impossible to make informed business decisions. Data profiling is an essential step in gathering reliable, high-quality data for your business.

Best Practices for Data Profiling

Across business of all size and industries, these best practices lead to data profiling success:

  • Follow a regular schedule. Start by picking a regular schedule. Large data profiling projects may be rare, but frequent maintenance to your data profiles helps you stay on track and avoid bigger projects in the future.

  • Employ data expertise. Where data analysis is outside your expertise, hire a firm or consultant to complete a deeper evaluation of your results and show you what you don't know about your data.

  • Utilise the best systems. Ageing servers are likely expensive and inefficient for your data needs. Upgrading to a modern ERP or data warehouse solution can help you find ways to reduce costs and improve performance.

4 Steps in Data Profiling

If you’re looking to start data profiling, these are four main steps you should take to move forward:

  1. Discovery

    Start with the discovery phase. Structure discovery, content discovery and relationship discovery helps you chart out what you have available. While everything won’t necessarily connect and work together at this point, it’s essential to know where you stand today and at the start of any data profiling endeavour.

  2. Profiling

    The profiling steps involve listing out details of what's contained in each dataset. Think of profiling as creating a database that explains all of your other databases. Smaller companies can use spreadsheets for data profiling, while enterprises rely on larger ERP systems or dedicated data management platforms. After profiling, you can note data that will be useful more often and readily accessible versus less critical data that can remain in lower-cost storage.

  3. Standardising

    Now you know what you have and how to find it. The next step is making sure similar data matches across tables and databases. For example, a United States ZIP code of 12345 could be entered as 12345-1234, or someone may have accidentally typed in 123 45 with a space in the middle or other errors. Standardising aims to bring all similar data into one format. A computer may not realise that 123 45 is the same as 12345. Fixing those errors and matching formats across all data makes human or computer analysis much more feasible.

  4. Cleansing

    The last step is cleansing. Data cleansing further fixes any formatting errors to meet your new standardisation rules. It also involves removing any bad, corrupt, or completely worthless data. Following strong data profiling policies and using backups helps avoid any additional data losses in the future.

Data Profiling Tools

As sources and methods of taking in data continue to grow, companies who cannot cleanse and organise it effectively will be at a disadvantage. But those who do practice efficient data profiling will be able to take advantage of big data and surpass their competitors.

Data profiling with an old spreadsheet program would likely be a massive waste of time and effort. Instead, you're better off with powerful, modern tools designed to analyse and profile business data. A data warehouse and business intelligence platform that can consolidate all business data into one centralised and organised system is ideal for most midsize-to-large businesses.

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Data Profiling FAQs

What is data profiling in ETL?

ETL is short for Extract, Transform and Load. Data profiling in ETL is a detailed analysis that helps businesses choose the right data for each project. Using data profiling in ETL will help you find if you have any corrupt, duplicate or incomplete data in your datasets.

What are the types of data profiling?

The main types of data profiling are structure discovery, content discovery and relationship discovery.

What is data profiling and data cleansing?

Data profiling is creating an inventory to understand your business data. Data cleansing is removing bad or corrupt data from databases and fixing data to ensure it matches a common format.

How does data profiling make big data easier?

Data profiling helps you consolidate and understand big data and turn it into something more useful and manageable.