A Brief Guide To Data Profiling

Remember Schmidt from New Girl (an American sitcom)? As an employee at a marketing firm his ideas and marketing pitches were often rejected by his boss (ouch! no hard feelings, Schmidt). It’s not like we don’t know that he is the wealthiest person in the loft AKA Apartment 4D. He is also immensely handsome and prefers everything in his apartment to be spotless. What does that have to do with this article? #Patience #YouShallSee

So, Schmidt works at the marketing firm and his ideas often fall flat on their face despite the pitches being written by his extraordinary writer friend, Nick (trust us we don’t mean to roast these guys #TinFinityRocks). It’s just that we are trying to draw the fact that many marketers and brands often struggle in coming up with the perfect pitch, terrific marketing strategies or campaigns to win customers. One of the prominent reasons behind this could be improper Data Profiling. Is it? How?

Let’s assume that Schmidt has accumulated data on his customers, this data includes what his customers want to buy, how they shop, the items they’re interested in and even the brands they prefer. Now, Data Profiling will help Schmidt to examine, analyze and create useful summaries of this data, seek critical insights and leverage this data to his advantage. Besides this Data Profiling will also allow him in examining the data to detect errors from spelling mistakes to algorithmic errors, formatting and standardization issues. Now that he has gained access to error-free data, he can use it further to frame effective strategies and target the right audience! (He’s already good at keeping things spotless so this would be easy for him!)

That was our hypothetical presentation of Data Profiling and now to know more about it, let’s head on to the next section.
 

What Is Data Profiling?

“You’re listening to the radio and writing with a pen. What decade are we in?”
 
That’s Schmidt talking to his friend Winston and this statement makes it pretty clear that Schmidt believes in staying ahead of time. So, how come we let him lag in the context of marketing? Here is a handful of things that will help Schmidt understand what Data Profiling is.

Well, Data Profiling is a process that involves organizing summarized data into categories. When it comes to business, the categories are often viewed as customer records, transaction histories and contact information. Data Profiling helps businesses understand their customers better by grouping the summarized data into similar demographics. For example, Schmidt has two customers who have purchased the same product in the past month but one of them has shopped more frequently than the other. Now, these two customers would be assigned to different groups. With the help of Data Profiling, Schmidt can detect and rectify all data errors, ensure maximum data quality and focus on what type of promotions might work best for each group. Then he might discuss this plan with his best buddy Nick and ask for his help in writing the perfect pitch.

Like every wonder of technology, Data Profiling has come a long way since its evolution. Without further ado, let’s hop on to the next section and decode the evolution of Data Profiling.
 

Evolution Of Data Profiling

Here’s a thing or two about the evolution of Data Profiling.

The roots of Data Profiling trace back to 1968 when people with extremely specialized skills were able to translate data into information. It was the time when data was stored in silos. Thanks to Sir Edgar Codd who recognized this issue and in 1970 presented the suggestion of relational databases. Later, the relational database model gained traction across the globe.

Data Profiling was first introduced in 1983 when IBM had to analyze large amounts of data that had been collected by the U.S. Census Bureau. The process of assuring data quality was tedious and required a lot of manual work. In 1986, the huge mainframe computers maintained customer data meant to be used for delivery services. These mainframes were created to correct the spelling errors in names and addresses, while also maintaining data profiles of customers who died, relocated, married, or got divorced.

It wasn't until 1988 that computers could handle more complex algorithms for data analytics. This led to the introduction of deep-parsing - Data Profiling which allowed users to get detailed information about what is stored on a computer's hard drive by performing more complex searches.

Later, during the 1990s, CEOs and businessmen heavily relied upon data analysis, data mining and data quality as they started integrating tons of data and the Internet added the cherry on the cake. By 2010, the concept of data governance was born when there was a need to combine, manipulate, store and present the data profiles. That brings us to the present when Data Profiling has become the backbone of data quality assessments.

That was all about the brief history of Data Profiling. Now it’s time to decode the process of Data Profiling.
 

The Process Involved In Data Profiling

Here we are presenting the step-by-step process of Data Profiling, read carefully because every step matters!
 

• The Right Type Of Data Profiling

  Data Profiling begins its work in form of three types of profiling, namely - manual data profiling, automated data profiling and expert data profiling:

  • Manual data profiling refers to going through the databases, data warehouses, data lakes in the same old-fashioned way (which is obviously not Schmidt’s thing.)

  • Next comes automated data profiling which implies using systems and opting for Artificial Intelligence and Machine Learning.

  • The third type is ideal for any organization that must deal with gigantic databases. In such cases, organizations overflowing with data can hire experts to handle the process of Data Profiling.

So, choose the right method, the right data profiling tool and hop on to the next step.
 

• Data Discovery

  Another important step is data discovery which can be done by structure discovery or content discovery. Structure discovery refers to checking the formatting of data whereas content discovery refers to examining each database in the context of its content and quality.

• Data Cleansing

  We bet that the header itself will impress Schmidt! Well, this step will help Schmidt meet the standardization rules and keep the data spotless (just the way he likes it!). Besides this, data profiling tools are also helpful in getting rid of any duplicate, corrupt or worthless data.

Brace yourself we are about to unfold the benefits of Data Profiling next. Come on Schmidt, you’ll love this!
 

Benefits Of Data Profiling

If you think that the benefits of Data Profiling are just limited to coming up with effective marketing campaigns, then you are slightly wrong. Here we present the comprehensive range of benefits of Data Profiling for you!
 

• No More Threat To Data Quality

  Data quality to brands is as valuable as mango-chutney to Schmidt (it’s his favorite thing ever!) as it helps them determine essential information that might impact the brand’s choices. Data Profiling helps them in identifying data quality problems that prevail in their system, data warehouses, data lakes, etc. that might affect the organization in the long run.

• On-point Decision Making

  Profiled data is further utilized to stop real-time, minor decisions from turning into mistakes and those from turning into huge problems. This is possible as it also helps in revealing the possible outcomes of certain scenarios related to customer data. Long story short, Data Profiling helps you in ensuring flawless data quality assessment, capturing a clear picture of the company’s insight and making wise decisions.

• Top-notch Crisis Management

  This point reminds us of Jessica Day AKA Jess (the main lead of New Girl) who is always there to solve the crisis for her friends. In several episodes we see her solving problems of Schmidt even before he asked #BFFGoals. In the same way, Data Profiling benefits you by identifying and addressing problems in data even before they arise.

 
Phew! That was all about how Data Profiling is benefiting brands. Now it’s time to unleash its future trends. Ready?
 

The Future Of Data Profiling

Here’s how the future is all set to surprise you with emerging trends in the area of Data Profiling.

 
As Data Profiling grows in adoption, Data-as-a-Service will make it convenient for brands and data analysts to collect business-critical information and ensure data quality in the most secure and less time-consuming manner. As a result, it will be easier for data analysts to eradicate redundancies and ensure that spotless, profiled data is being transferred to a central location leading to enhance agility and lower data errors. Businesses looking to transition to the cloud should explore Data Profiling tools that work well with Data-as-a-Service deployments.

The next trend in this arena is data fabrics which will help you get rid of traditional data integration efforts in no time at all. Besides this, data fabrics will allow you to improve the business value of existing data lakes, data warehouses and so on. While emerging technologies such as embedded machine learning, metadata management will fuel the fire of Data Profiling soon.
 

Summing It Up….

 
It’s a no-hidden fact that data is the new business currency. It unlocks insights that were previously inaccessible and gives brands the ability to be proactive, rather than reactive. Brands use data Profiling to identify their customers, monitor trends and develop strategies to keep them one step ahead. One of the most important things a business can do with its data is to stay ahead of the competition by understanding what the market needs before they know they need it. Data Profiling will open several doors for brands that want to be successful in today’s market by ensuring their data is top-notch. This article walked you through a detailed overview of how Data Profiling works as well as the benefits of using this powerful tool. We hope that you and Schmidt have understood this concept well!