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ToggleData and information from multiple sources hold great significance in the present critical business landscape. So it becomes important for data and business analysts to work with different data sets. Traditionally business and data analysts had to go through a time-consuming and complex process of extracting insights from different locations and then combining them together into a single dataset. Data blending is an intuitive and easy process that has been developed after technological advancement and development in the field of data science and data analytics. The guide below will illuminate what data blending is, its benefits, how it works, and how it can enable data and business analysts to work with a massive amount of data seamlessly.
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What is data blending?
The process of combining datasets collected from different sources to develop an actionable and valuable analytic data set to help make data-driven decisions for the business and drive more success into the business process is known as data blending. In other words, Data blending includes pulling data from multiple sources and developing a unique, single dataset to facilitate data analysis and visualization. For instance, if data is spread out across different and multiple spreadsheets such as sheets or excel, IoT devices, web applications, cloud systems, and business intelligence systems, then with the help of a data blending platform, you can easily blend them from distinct sources and easy and rapid process. Data blending typically helps in performing rapid data analysis and ad hoc reporting. The data blending process enables organizations to obtain meaningful insights from different sources and develop deeper analyses. Data blending is not the same as data warehousing and integration as they differ in primary use. You have a blending conducted by a data analyst or business analyst to build analytic data set so that it can answer particular questions about business organizations.
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Why is data blending important in data science?
Data blending holds great value in data science as it empowers data or business analysts to perform data analysis and incorporate the data to derive deeper and faster business insights. Combining different data sets provides valuable information that might not have been discovered if data analysts had not blended the information. Data blending also provides a new perspective on data, leading to better and improved business decisions.
In traditional methods, business and data analysts had relied on conventional approaches like scripting, Vlookups, and multiple spreadsheets to construct datasets which would have incurred a lot of time and would be clunky. Using manual processes and methods and relying on professionals for building analytical data size is also ineffective or scalable. Data Blending enables business leaders and data analysts to speed up the process of developing datasets and provide more accurate and relevant answers. To stay ahead of the competition and incorporate innovation into a business organization, business leaders have to focus on data analysis and offer accurate answers to high-level business questions instead of relying on manual SQL queries and spreadsheets. With the help of data blending, analysts can gain many advantages and expand their roles or responsibilities to make official and significant business decisions.
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The process of data blending
There are several techniques to blend data, and the complete process of data blending is divided into the following four simple steps.
Preparing Data
The first and foremost step in the process of data blending and data gathering is to collect information that can help to answer relevant questions to the business organization. Data analysts have to identify the pertinent data sets collected from different sources and use a wide spectrum of file types and structures. Every data source included in this process will share the same dimension so that they can be combined. The capacity to convert these different data types into one common structure to enable a valuable blend without changing or manipulating the main data source can be done using modern data analytics technology and data science tools. Leverage of data using data science tools and technology can be done in a repeatable and automated way.
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Blending Data
The next step is to combine data collected from different sources, customize every data based on common dimensions, and ensure a seamless data blending process. For these data, analysts have to include relevant and essential data to be answered from a business point of view. It must be noted that the resulting data set is easy to explain and comprehend to the stakeholders.
Validating Results
Combining data from different sources can arrive at several problems regarding accuracy and compatibility. Data analysis has to examine the data to validate the results and then explore the unmatched records to guarantee consistency and accuracy throughout the data sets. To perform this function, they have to structure and clean the data properly to achieve its desired outcome. Then they have to review the new data set to ensure that every type and size of data is in a perfect form to facilitate data analysis. In the final step, they have to review the desired end of the data blend properly. Understanding and exploring the data results is an amazing opportunity, especially for unmatched records.
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Outputting Data
After validating the data blending process, the next step is implementing the data into an appropriate business intelligence system and ensuring that the blended data set can help fulfill the main objective. The desired output can also be pushed back into the database, incorporated into operational business processes, analyzed using predictive spatial or statistical methods, or even pumped in Tableau or QlikView-like data visualization software.
What are the benefits of data blending?
As you know now, data blending is so crucial in data science. Let’s take a look at the benefits of data blending and why it is a crucial tool for blending data quickly and seamlessly.
Rapid analysis
Data blending enables data and business analysts to view data from different lenses and gain unique and meaningful insights to make better business decisions.
Few data silos
Data blending enables professionals to store data separately and merge them when required. Data blending avoids data blind spots and provides better flexibility.
Greater efficiency
Data joining can cause complexity when adding different tables together. Data having multiple details can be difficult to analyze and sort. With the help of data blending, the process of bringing data together becomes seamless and easier
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