What Is Data Blending?
Data blending is the process of combining data from multiple sources to create an actionable analytic dataset for
business decision-making or for driving a specific business process. This process allows organizations to obtain
value from a variety of sources and create deeper analyses.
Data blending differs from data integration and data warehousing in that its primary use is not to create a single
version of the truth that’s stored in data warehouses or other systems of record within an organization. Instead,
this process is conducted by a business or data analyst with the goal of building an analytic dataset to help answer
specific business questions.
Why Is Data Blending Important?
Data blending empowers a data analyst to incorporate data of any type or any source into their analysis for faster,
deeper business insights.
Combining two or more datasets often illuminates valuable information that might otherwise not be discovered if the
data wasn’t blended — information that provides a new perspective that might lead to better business
decisions.
Traditionally, analysts have relied on VLOOKUPs, scripting, and multiple spreadsheets for constructing datasets, but
this can be clunky and time consuming. Utilizing manual processes or relying on data scientists to build analytical
datasets is increasingly ineffective — it’s not scalable with the number of ad-hoc requests analysts receive.
Data blending building blocks speed up the process of constructing datasets and can help analysts and business
leaders get more accurate answers.
In order to live at the forefront of innovation, the focus of data analysis must focus on high-level business
questions rather than the minutiae of spreadsheets and manual SQL queries. Data blending can help analysts take full
advantage of expanding roles, as well as the expansion of data needed to make critical business decisions.
The Data Blending Process
While there are many different techniques for bringing data together, from inner and outer joins to fuzzy matching
and unions, data blending boils down to four simple steps.
Preparing Data
The first step in gathering data is to ask what information might be helpful to answer the questions being asked.
Identify pertinent datasets from various sources, a wide array of structures or file types can be used. Each
data source that is included will need to share a common dimension in order to be combined.
The ability to transform these different types into a common structure that allows for a meaningful blend,
without manipulating the original data source, is something that modern analytics technology can do in an
automated and repeatable way.
Blending Data
Combine the data from various sources and customize each join based on the common dimension to ensure the data
blending is seamless.
Think about the desired blended view and only include data that is essential to answer the questions being asked
as well as any fields that may give additional context to those answers when an analysis is stressed. The
resulting dataset should be easy to comprehend and explain to stakeholders.
Circle back to this step to include or remove data from a workflow and further build out the analysis.
Validating Results
It’s no secret that combining data from different sources can usher in a whole host of compatibility or accuracy
issues. Examine the data to validate the results, explore unmatched records, and ensure accuracy and consistency
throughout the dataset.
First, cleanse and structure the data for its desired end. Then, review the new dataset to ensure that the data
type and size are in their desired format for analysis.
Finally, review the outcome of the blend with a critical eye. This is a great opportunity to explore the results
for any unmatched records and perhaps circle back to additional data preparation tasks upstream of the blend.
Outputting Data
Once the heavy lifting of data blending is done, it’s time to implement the data into the right business
intelligence system so that the blended dataset can assist in fulfilling the objective.
This means that resulting outputs can then be pushed back into a database, incorporated into an operational
process, analyzed further using statistical, spatial, or predictive methods — or pumped into data
visualization
software such as QlikView or Tableau.
Data Blending and the Analytics Journey
Data blending is an essential step in the broader journey of analytics, though the volume of data sources that a
company might have can make data blending seem like a complex undertaking.
The Alteryx Analytics Automation Platform makes data blending less intimidating and more accessible. Analysts can deliver deeper
insights by seamlessly blending internal, third-party, and cloud data, and then analyze it using spatial and
predictive drag-and-drop building blocks. Other building blocks like Fuzzy Matching give users the ability to match
two datasets based on related but non-identical attributes — typically names and addresses.
Plus: Alteryx workflows can easily be saved and repeated for optimization, further data blending, processing,
updates, and analysis.
The Alteryx Analytics Automation Platform empowers business analysts, IT users, and data scientists alike to blend and analyze unlimited
combinations of data to produce tangible business results. This means users can democratize access to data, optimize
and automate manual processes, and upskill their insights with no-code, low-code advanced analytics.
Get Started with Data Blending
Alteryx is designed to make each step of the data blending process easy and intuitive. Check out the Data Blending Starter Kit to dive deeper into date blending and learn how to:
- Blend transactions and customers to provide visual reporting insights that help identify trends and
opportunities - Enable quick fuzzy matched blending of similar but not exact matching data and feed it into automated
workflows for real-time insights - Blend spatial data to calculate ad area distribution, increase sales, and improve ROI
To learn more about Alteryx and see first-hand how analysts and business leaders can use its data blending,
processing, analytics, and reporting capabilities to their advantage, start your free trial today.