Data Blending - How to Save Time and Do More With Your Data

Having and joining data into one system is helpful, but the value is exponential when you cross it and combine data from multiple sources.  Through data blending, you merge multiple data sets and look at the data through a new lens to generate unique insights and influence decisions.

This blending data enables you to unlock new connections to gain an enriched understanding that can help you figure out where to best spend your time and resources. Discover how these new data connections benefit your organization and foster data collaboration with data blending.

What is Data Blending? 

Data blending is the process or practice of making two or more data sets or sources compatible and then combining together in a standardized set. The purpose of this mixture is to derive more meaningful data insights. 

Where in the data pipeline does data blending occur? 

As simple as data blending may appear- it is not a simple process in conventional data pipelines, which require custom coding or individual components to facilitate each step of the data pipeline.

First, you must extract the raw data from its original source, store or warehouse the data and then process and transform the data so that it is compatible for data blending or enrichment. From there you can use this data to perform analysis.

‍Data Blending vs. Data Joining 

It’s easy to confused data blending and data joining. Both processes involve the homogenous mixing of data. However, data joining refers to combining data within a single source or data set, where data blending is merging data from two or more different data sources.

Save Time and Do More with Your Data

Unlock Dormant or Siloed Data

Information is scattered throughout multiple systems and various departments, and it’s not possible to gain a complete view of a situation unless you combine information from different systems to understand all the factors. The data blending process lets you understand the total scope of data you collect and extract value from this previously unused data. 

This process to unlock data can be very complex for conventional data pipelines, but with new technologies such as single data pipelines you can unlock your data in minutes.

Enriched Analysis for Better Decision Making 

After all, one dataset by itself, regardless of the level of detail, doesn’t include every piece of the puzzle needed to build a comprehensive view of a project, especially when multiple departments and programs are involved.

This is why data enrichment practices are vital to long-term goals and better decision-making. For example, there isn’t a single construction program that contains all the data needed to make decisions and complete a project. Sales data is stored in one program, modeling is done in another, pricing is stored in Excel Spreadsheets, Google Sheets, or other data banks. 

By blending data from multiple sources you can go beyond simple descriptive analytics and onto more meaningful insights and calculations. Take this example of an Embodied Energy Report. This report contains two different data sources. One set is from an Autodesk Revit File and the second data source is an Excel file containing information from EPIC energy data.

This data app shows the result of a data blending, using these two data sets we are enabled to calculate the environmental impact of this particular project. Using this data we can get a picture of the total energy and use this information to increase the efficiency and sustainability of this project.

What does the data blending process look like? 

The process of blending data depends on the tools used for data analysis. This process can be complex and time-consuming. After all, data blending occurs as a last step before data analysis because the data must first be extracted, stored, and formatted in a way that facilitates data analysis. Each of these steps may require custom coding.

To illustrate, you may have data spread out across multiple spreadsheets like Excel or Google Sheets, business intelligence systems, IoT devices, cloud systems, and web applications. Sometimes this data can be extracted using an API, or by downloading a CVS, JSON, or Excel file. If these options are not available you must work with a data engineer who understands how to handle this data.

From there you must have a way to store the data and then transform it so that the formatting is compatible for comparison. Once you've reached this point you can then blend the data and then you can perform analysis, build visualizations, and reports.

With technology advances, this procreation of data has become much more simple. Using a data blending platform like Toric's no-code data workspace you can quickly perform all of these functions in a single workspace.

How to Blend Data in Toric 

1. Connect to your data sources.

Before you can even start blending data, first, you must gain access to the data. Toric can store the data you connect to, eliminating your need for a data warehouse or data lake when performing data blending. 

With Toric, you have a few options on how to connect to your data. 

Drag and Drop Local Files 

This is as simple as dragging the connections to the independent sources to the Join Node. Drop your CSVs, Excel Files, JSONs, and more directly into your dataflow. 

Connect to an Integration 

Get real-time updates to your data by using an integration or plugin. Currently, Toric offers integrations with Salesforce, QuickBooks, Autodesk, and more.

2. Transform and clean the data 

Raw data is rarely ready for blending and analysis. First, you may need to inspect the data and clean it or format it to be compatible. Toric nodes enable you to examine your data as you clean them and perform and track your transformations directly in the dataflow. 

Classify Data Types 

A major benefit of utilizing nodes is the option of classifying data types so you can make your data compatible and more digestible for reports. Once you designate a data type you can interact with that data changing the format as needed in your analysis ( ex. quantifying time or changing the measurement type.) 

3. Join the data.

An essential step to data blending is joining the data. This is as simple as dragging connections from one node to another.

4. Build your data app as you explore your data. 

Start exploring your blended data and build your data app repots at the same time. 

Create graphs, and perform statistical analysis and track your progress within the data app.

Bonus- Use Annotations

Take notes as you build your dataflow using annotations. This feature is especially useful when collaborating with teammates to explain data transformations or analysis or ask questions about the data. 

5. Share your insights.

You’ve created one data app as you explored your data, but you can create multiple data apps with specific views for individual stakeholders. Instead of inundating them with all of the information about the data analysis, provide reports that can be synced as the data is updated. Embed these data apps where your team needs to see them, such as Sharepoint, Notion, or Wikis. 

6. Reuse your data app.

The best part about dataflows is that they live and sit on top of your data. It’s easy to create a reusable data app and replace data for consistent reports containing the same data blending process-with just a few clicks.

Key Takeaway 

Eliminate data silos and uncover rich insights by blending data. Try our no-code data workspace that facilitate this process in minutes and enables you to explore data in new ways and automate your data blending process.