Data Analytics

What is Data Blending? Learn the Basics

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Data Blending and Its Importance

Data blending is associating data from various sources and merging it into a functional dataset.

Data blending gives you a more processed view of data from different sources. Data blending helps you to find similarities between data, and you can draw important information from it.

Data blending enables you to create an intense analysis of data cost and time effectiveness compared to old data-warehousing processes.

This process is more useful when you are in your decision-making process. The blended data is analysed to get an overall image of the solution you are searching for.

Traditional methods like comparing multiple spreadsheets or depending on VLOOKUPs can be a laborious process. Data analysts should focus on complex problems than small SQL queries. Data blending has increased the efficiency of data analysts and leads them to a more accurate solution. 

Data blending is different from data warehousing and data integration. Data integration joins various datasets from a single database that is different from data blending. A data analyst often monitors data blending. Since the importance of Big Data is increasing day by day, data blending has become more vital.

Benefits of Data Blending

A study by Forbes shows that 80% time of a data analyst spends on works like creating, preparing, and editing datasets and only 20% of working hours are used to analyse data. Less time spent by data analysts on preparing datasets means more time for pulling out significant business insights. 

Since data blending is a fast process, it can be helpful in non-technical areas like marketing. It helps them to draw information from the CRM database and gives an idea about customer interests and more profitable areas. 

Steps for Data Blending

  1. Data preparation: This step includes identifying and gathering data relevant to the required solution from various databases. For example, A company may have 3 or 4 branches and to narrow down solutions for a problem that affects the whole brand, it should gather data from relevant databases from each unit. Data blending makes this procedure easy. But for that, we should identify the databases required for the solution. 
  2. Data combining: A vital point to note for data blending is that combining data is possible only if they have a common aspect. This combined data is obtained for easy analysis.
  3. Data Cleansing: In this step, any unnecessary or redundant data can be removed from the combined dataset, and a more functional view of the database is provided.

When to use Data Blending

Data blending is advantageous when,

  • You analyse data from several levels of granularity.
  • You join data from various sources which does not have similar dimensions.
  • You interpret or decrypt a huge amount of data in less time.

Data Blending Tools

  • The very first phase for any decision making is planning. Tools like Datawatch Monarch and Alteryx are some of the best for data planning, data preparation, etc. Using these platforms, you can extract required data from various sources.
  • Tableau and Spotfire are some visual analytics tools that have data blending abilities. These tools have Graphical User Interfaces and use extracted data for data blending.

Both types of tools mentioned above are considered as self-servicing software.

Importance practices to follow

Collecting data from various sources is not that easy. There are certain ethics you should follow even in the IT world. There are laws you should follow.

  • Consent: A lot of private information is available on the internet. Always make sure you have the consent from the person that the data belong to; if the data is not made public. But in some cases, general information does not require consent. You have to request owner along with an explanation to obtain consent.  
  • Keep a record: Even though it is nearly impossible to keep track of every data source, it is always good practice. History of links, the value of actual data, variables used to merge data, algorithms used etc. can be used for future reference.
  • Understanding algorithms: There are lots of algorithms, and each algorithm has different models. It is impractical for data analysts to know every algorithm around the globe. But since algorithms perform processes like data extraction, cleaning, etc., learning and understanding the algorithm you use is essential. 
  • Credibility of data: The Internet is a vast network and is open to almost everyone. Inaccurate data is widely available on the internet and is impossible to distinguish. Ensuring the credibility of the data you use is an important factor. Wrong information can lead to total disaster.


In this article, we have briefly learned about data blending. Data blending is an advanced method. It can save us from difficulties caused by complex formats of Excel sheets or clumsy data sources like pdf documents. By following correct steps carefully and by practising appropriate methods, data blending can be a time saver for your business.

Aarif Habeeb is the Technical Content writer at DigitalMarketingCrab. He is a technical writer and IoT writer of year 2020 by HackerNoon. His expertise includes Technical writing, Technical SEO, e-commerce, lead generation, and localized site-to-store strategies. He is the founder of Aarif Habeeb & Co. Feel Free to Follow him on Twitter and Linkedin.