Data Profiling with its Benefits, Best Practices & Tools

What is the importance of data to a business?

Good data is the core 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.

What Is Data Profiling?

Data profiling is a process of reviewing and analyzing diverse datasets across the business to inform business decisions. Alternatively, 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.

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. Like product inventories in a retail store or warehouse, data profiling helps you create a digital inventory of your datasets.

Ways to approach Data Profiling Work

• 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.

Data Profiling Importance.

1. It can help a business improve profits and cut waste.

2. You can use data to improve your marketing plan or change the geographies on which your sales force focuses.

Types of Data Profiling in Business Analytics

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

1. 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.

2. Content Discovery

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

3. 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

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 a business may not have its own staff of?financial planning and analysis 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 catalog, clean, and validate the data you have. Popular methods include:

1. Column Profiling

Column profiling is a good first step in data profiling. For example, properly labeling 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.

2. 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.

3. 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

The focus here is to standardize 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.

Best Practices for Data Profiling

To businesses of all sizes 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.
• Utilize the best systems.?Aging 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

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 endeavor.

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. Standardizing

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. Standardizing aims to bring all similar data into one format. A computer may not realize 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 standardization 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 organize 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 analyze and profile business data. A data warehouse and?business intelligence platform?that can consolidate all business data into one centralized and organized system are ideal for most midsize-to-large businesses.