If you’re in the world of data analysis or data science, you’ve probably heard of data blending. But what exactly is data blending, and what are the benefits and costs associated with it? Keep reading to dive into the basics of data blending.

What is Data Blending?

Data blending is the process of collecting data from multiple sources and merging it into one easily consumable dataset. This allows you to see correlations in the blended data and extract valuable information from it, while avoiding the hefty time and monetary investment that comes with traditional data warehouse processes. This multi-source collection method allows you to gain a more complete picture to help leaders make better informed decisions.

What is one benefit of using blended data?

Generally speaking, the main benefit of using blended data is that it saves your analysts a lot of time. According to Forbes, data analysts spend the majority of their working hours (about 80% of it) preparing, cleaning, and creating datasets. This means that only 20% of a data analysts’ time is actually spent pulling beneficial insights from a dataset. Imagine how much more insight your business could pull from these analytics if the collection/preparation process was more efficient. Data blending helps to increase the efficiency of data preparation, to an extent.

What is the difference between data blending and data joining?

While data blending and data joining are both methods of combining data for analysis, there are clear distinctions between the two approaches. Data joining is when you merge data from a single data source, with the same inherent dimensions (e.g. two tables from an Oracle database, or two spreadsheets from Excel). Data blending takes this process one step further by allowing the user to encompass multiple sources into their dataset, even if the sources don’t have the same innate measures or dimensions (e.g. combining data from an Oracle table with data from an Excel spreadsheet).

When To Use Data Blending

Usually, data blending is most beneficial when you want to:

1. Analyze data of different levels of granularity/detail
2. Combine data from different databases, without the same dimensions or measures (e.g. Oracle, SQL, Excel, etc.)
3. Compile mass amounts of data at once

Steps To Data Blending

1. Identify and gain access to data from the sources you want to use
2. Combine the acquired data for easy use and analysis by establishing common dimensions between the primary and secondary data sources
3. Clean the data, remove any bad/irrelevant pieces, and create a usable dataset to analyze going forward

Data Blending is important, but is there an even more efficient way?

Manual steps in the data preparation process such as data blending can be unnecessarily time-consuming. The good news is, there’s a more efficient option. Web data integration from Import.io works directly with your web data to identify, extract, prepare, integrate, and allow you to consume data insights in real time. This automated process eliminates the time typically spent on data cleansing and preparation, providing you with consumable data in seconds.