Etl

Data Blending Explained: What It Is and How to Do It

In today's complex business landscape, where information pours in from many different locations throughout the day, it's crucial to work with multiple data sets.

In the past, pulling insights from disparate locations and mashing them together into single datasets was complex and time-consuming, but recent advancements in data blending make it easy and intuitive.

Keep reading to learn how data blending works, why it’s useful, and how it can transform the way you work with data.

What is data blending?

Data blending involves pulling data from different sources and creating a single, unique, dataset for visualization and analysis.

To illustrate, you may have data spread out across multiple spreadsheets like Excel or Sheets, business intelligence systems, IoT devices, cloud systems, and web applications. Using a data blending platform, you can quickly mash together data from all disparate sources in a way that’s fast and easy.

Data blending is typically used for ad hoc reporting and rapid analysis.

Traditionally, teams combined data sets through a process known as extract, transform, load (ETL). With this approach, you copy (extract) data from different sources, standardize (transform) it, and migrate (load) it into a data warehouse.

The modern approach is to blend data using the newer extract, load, transfer (ELT) technique. This involves extracting data, and then storing or loading it into a data warehouse. From there, you can combine or transform data to solve business needs. 

The ELT approach is typically faster and easier than ETL, which is why it’s becoming increasingly common.

Primary vs. secondary data sources

One important distinction is that data blending requires a primary and secondary data source.

The primary data source is the original source that you pull information from. This is typically raw data like sales information for an individual or a team.

The secondary source is data that you extract from one or more primary sources for research and analysis. The secondary source operates completely independently of the primary source.

By using primary and secondary data sources, you can separate the data and avoid losing or altering information during the blending phase.

Data joining vs. data blending

It’s easy to confuse data blending with data joining because the two concepts are similar.

The key difference is that data joining only allows you to combine data from a single source.

For example, you may have data from two SQL databases that you need to combine into a single location. In this case, you could join the databases together into one set. You could also join data from different Excel sheets.

It’s not always possible to join data within a BI tool.

For example, joining only works with small data sets. On top of that, not all databases support joins, nor do joins work when there is duplicate data.

Data blending goes a step beyond data joining, making it possible to bring together data from multiple tools.

Is data blending the same as data integration?

Data integration typically refers to more comprehensive data cleansing and merging across different sources.

Data blending is typically for conducting quick, surface-level analysis of data. On the other hand, data integration typically applies to warehouse settings when you need to bring together multiple joins.

The benefits of data blending

Now that you have a better idea about what data blending is, let’s take a look at some common reasons why it pays to have a tool that helps your blend data seamlessly. 

Rapid analysis

Oftentimes, a data set only gives you part of the picture when analyzing customer data. For example, you may have two databases, one with monthly sales and another with monthly quota data.

Blending data is like using a Venn diagram. This allows you to look at the data through a new lens and generate unique insights to influence business decisions.

Fewer data silos

Despite the abundance of data today, most information is still heavily siloed in different databases.

Blending allows you to keep data in separate lanes and merge them together when you have to. This system ultimately grants better flexibility while eliminating information blind spots.

Greater efficiency 

Data joining is not always the most efficient way to bring data together.

For starters, joining can increase complexity when adding multiple tables together. And on top of that, data with multiple levels of detail can be difficult to sort and analyze.

Data blending eliminates these issues, making the process much easier and more seamless.

No need to rely on data scientists

You don’t need to be a data scientist or even have much of a technical background to engage in data blending. This strategy is useful for non-analytical professionals across departments like sales, marketing, and finance.

More revenue

More and more businesses are collecting data. Unfortunately, many are struggling to turn their analytics into insights.

Data often fails to drive results because it sits in a back-end repository instead of going through visualization and influencing business outcomes.

According to Alteryx, analysts say that not having data blending capabilities is the primary reason for not using the right data for driving key business decisions.

Lacking access to data blending capabilities often results in missing sales opportunities (37 percent), less ROI (25 percent), higher costs (18 percent), and losing out to competitors (9 percent), in addition to a variety of other negative impacts.

Data blending avoids this pitfall, giving business analysts across departments a key tool for operational analytics and empowering them to make better data-driven decisions across multiple sources.

How to blend data 

Data blending first requires setting up a blended data source.

First, acquire the data and find the information you need across different locations. This may include social media, spreadsheets, and tables, among other things.

Next, join the data by combining and loading the data to a destination for storage (e.g., a data warehouse).

The third step is to clean the data. This involves:

  • correcting data entry errors
  • deleting unnecessary data
  • and redesigning the data set into a better format

Once your data is clean and ready for action, the final step is analysis.

This is possible through tools like Tableau and Alteryx, which make data blending fast and easy.

Supercharge your data blending 

Before you can get to the stage where you clean, analyze, and blend data, you first have to get it out of a data source and into a centralized warehouse. This can be very difficult when information is spread across multiple systems.

You want to spend the majority of your time comparing and analyzing results in Tableau or Alteryx, not stringing together databases and slogging through back-end work.

Panoply streamlines the ETL and data warehousing process, making it easier (and faster) for you to work with it in a data blending tool. Learn more with a personalized demo today!

Get a free consultation with a data architect to see how to build a data warehouse in minutes.
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