How can Data Profiling help with Big Data

Businesses today rely heavily on data to make important decisions. Regardless of the company size, you have information from various platforms. This abundance of data enables businesses to stay updated and respond to market trends effectively.

However, with big data comes the chances of greater errors. It is important to ensure that your Data is free from oversights and has good quality. Data profiling curbs these faults and saves businesses from spending resources on a defective database. Keep reading to learn more about data profiling and how it can benefit your business!

What is data profiling?

Data profiling is the process of skimming through large data to analyze it. The purpose is to highlight errors, identify risks and draw conclusions based on common trends. The process helps eliminate faults and improves data quality which serves beneficial in drawing insights.

Through this process, companies can sift through large amounts of data which is essential in today’s internet-driven market. It also gives them an advantage over competitors who lack sophisticated data profiling systems. 

Due to the growing reliance on data, many companies outsource data management to agencies with the latest tools and analytics software to update databases. 

Types of data profiling

There are three types of data profiling. Each works on improving the quality of data and removing redundancies. 
 
Structure Discovery – In this data profiling, mathematical and statistical tools are used to analyze the structure of your data. Operations like mean, median, mode, standard deviation, etc., help identify data defects. For example, if the data enlists phone numbers, you will identify incomplete numbers or other inconsistencies through structural analysis. This step helps validate data and narrow down incompleteness.
 
Content Discovery – Content discovery focuses on improving individual entries in the data. It is an in-depth analysis that aims to enhance the data quality. In this profiling, the data management system identifies discrepancies and ambiguities. For example, the system will highlight numbers with incorrect area codes in data with phone numbers. Fixing this problem will help reach customers efficiently instead of losing contacts. 
 
Relationship Discovery – relationship discovery profiling is an advanced-level analysis. Here, Data is assessed in terms of correlations and insights are drawn. The data management tools aim to draw conclusions and infer meaning between tables and graphs of the data. In this stage, duplicate Data is eliminated to ensure insights are accurate. For example, understanding the relationship between columns of data with demographics.

Different techniques for profiling data
There are four basic techniques of data profiling. Each helps in analyzing big data.
 
 
Column profiling
This method focuses on counting the number of times a value appears in a column. Knowing this helps edit incomplete information and remove inconsistencies. It also helps in drawing conclusions and patterns from the data.
 
Cross-column profiling
The cross-column analysis aims at finding relationships and patterns across different columns. This profiling has two types of processes: key analysis and dependency analysis. 
 
The key analysis focuses on assessing columns to identify the primary key. On the other hand, Dependency analysis looks for interdependent variables across various columns. Both these finding help draw correlations in the data set. 
 
Cross-table profiling
In this method, the system works on identifying foreign keys. This helps in highlighting the dependency of variables in different columns. This profiling aims to identify relationships across tables and rule out redundancies. Companies can link dependent variables for policies and ignore data that is irrelevant to the audience. 
 
Data rule validation
This is an additional technique where the Data is compared against standardized data. Data analysis emphasizes this step because it shows the credibility of the data. If the data comes out to be accurate, it is ready to be used to draw insights. If the data comes out to be inaccurate, companies improve the quality instead of relying on it. Data rule validation is essential to preserve company resources because it highlights whether Data is usable or not. 
 
Why does your company need data profiling?
Data profiling has numerous benefits. For companies that rely their growth on big data, it is important to invest in data management services. This service improves the data quality and protects resources spent on bad data. Here are a few benefits of data profiling:
 
Credible and accurate data
The most obvious benefit of data profiling is getting the best quality data. Different types of profiling remove inconsistencies, fix incomplete data, and address the ambiguity. This leads you to Data of the highest quality without errors. Refined data helps in improving company policies and drawing conclusions about customer behavior. 
 
Risk management
Data profiling helps identify risks in the dataset. This information helps businesses proactively approach crises as they are aware of the problem beforehand. Knowing the root cause of the problem puts them in a better place to design plans that protect the business from downfall. 
 
Improved decision making
Data profiling provides valuable insights that show growth opportunities. When businesses rely on these data-driven insights to develop future marketing and sales plans, they are more likely to succeed. Sometimes companies try a policy on sample data and use those insights for a larger project. This preserves resources that could have been wasted otherwise. 
 
Increased privacy
With data profiling, the errors in the system are minimized. This means the chances of sending emails to the wrong people are low. Similarly, data profiling secures customer data and encrypts important information. These features protect the system from hacking or invasion of privacy. It also brings structure to the system, which helps identify external factors that could risk issues. 
 
Every B2B is flooded with big data from various sources like social media, email, online databases, etc. Its quality must be maintained to ensure that this data results in fruitful insights. Data profiling is useful for eliminating errors and providing the best quality data. If your company doesn’t have a data profiling setup, inCall systems offers a range of data management services in Singapore. You can connect with the team or browse their website to learn more about the type of data profiling that suits your business. 

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