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What is Data Blending? Learn the Basics

Blog: Think Data Analytics Blog

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,

Data Blending Tools

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.

Conclusion

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.

The post What is Data Blending? Learn the Basics appeared first on ThinkDataAnalytics.

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