Data Blending Speeds Time-to-Insight

Data blending is a user-friendly process that puts data-driven insights into the hands of key decision-makers at all levels of an organization. Data blending eliminates a common bottleneck that often leaves valuable data languishing: the need to rely on experts to present business data in a usable format. With data directly accessible by decision-makers, organizations can act quickly to capitalize on time-sensitive opportunities, realize efficiency gains, and increase revenue. 

What Is Data Blending? 

Data blending is the process of combining data from multiple sources to create a single data set to be used for visualization and analysis. Data blending is commonly used for ad hoc reporting or to conduct a quick analysis to answer a specific business question. By aggregating data from multiple sources, decision-makers gain a deeper, more complete understanding of the underlying factors influencing business problems and processes. 

Data Blending vs. Data Integration

Although similar on a surface level, data blending differs from data integration or data warehousing in several important ways. The primary goal of data blending isn’t to change the storage of the data, but to make all relevant data available quickly. Data blending bypasses the extract, transform, load (ETL) process. With ETL, data is extracted from the data sources, transformed in the staging area, and then loaded into a data warehouse. 

Data blending makes use of an extract, load, transform (ELT) process to arrive at data-driven insights quickly. ELT makes use of the massive data storage capabilities of today’s cloud data warehouses to load the data immediately after extraction. This sequencing makes it possible to combine or transform the data as it is needed for specific purposes, such as data blending. In general, ELT is quicker and simpler than ETL.

Data Blending Process

There are multiple techniques for data blending, each with its own unique series of steps. But no matter which technique you use, you’ll follow the same basic steps outlined below.

Preparation

The first step in the data blending process is determining what data will be needed to address the business need. Data sources may include data in multiple different formats and file types. Although data blending tools can transform differing data types into a common format, each data set must share a common dimension.

Blending

Blending data from multiple sources requires that data joins be customized based on the shared dimension. Data blending can accommodate many data sources into a single blended view, but adding data from sources that don’t directly target the business problem being addressed can create unnecessary complexity. 

Validation

Combining data in various formats from different sources isn’t always a seamless process. After cleansing and structuring the data, examine the newly blended data set to ensure the data is in the correct format for analysis. Unmatched records and issues with accuracy and consistency should be resolved before moving on to the next step.

Output

Once the data blend has been completed, the output can be loaded into a database or data warehouse and analyzed using a data analysis tool or accessed using a data visualization tool. The analysis or visualization provides valuable insights that would not be possible unless data from various sources had been combined using data blending.

What Data Blending Can Achieve

Data blending is a valuable tool that helps organizations quickly transform their data into valuable insights they can use to boost profits and improve business processes. Here are five significant benefits.

Rapidly analyze time-sensitive data

In general, data blending is a quicker, more reflexive way of analyzing data for key insights. With less complexity, data blending is less reliant on data analysts and IT professionals, relieving bottlenecks that can stifle the ability to act quickly to capitalize on time-sensitive business opportunities. 

Unite relevant data from multiple systems

As new sources of data proliferate, data silos have become an increasing problem. With data spread across disparate systems, gaining a holistic view of a specific business problem or process is very difficult or impossible. Data blending solves this issue by bringing together relevant data from a variety of sources. 

Gain deeper insights by combining related data sets

One data set rarely tells the complete story. Combining relevant data from multiple sources paints a more complete picture of why something is happening or how a specific business process could be improved. 

Make data accessible to those without a data science background

Data analysts and IT professionals are inundated with requests for data to satisfy various business intelligence initiatives. The resulting bottleneck frustrates data teams and also stifles innovation, making it difficult for decision-makers closest to the problem to gather the necessary insights needed to solve it. Data blending is less complex, providing greater access to relevant data needed to power innovation and encourage creative problem-solving within an organization. 

Improved decision-making

Today’s business landscape is too complex and change happens too quickly to rely on guesswork. Data-driven decision-making, powered by democratized data analytics methods, allows organizations to solve problems quickly and take advantage of opportunities when available. 

Snowflake for Data Blending and Integration

The Snowflake Data Cloud powers data sharing across your entire business ecosystem and effortlessly facilitates data analytics processes such as data blending, enabling quick access to data from multiple sources. With Snowflake, you can achieve seamless data collaboration while reducing costs and revealing new business insights. The Data Cloud supports ELT processes and Snowflake’s comprehensive data integration tools list includes leading vendors such as Informatica, SnapLogic, Stitch, and Talend, to help you take your capabilities further. 

Snowflake Data Collaboration eliminates the long ETL, FTP, and electronic data interchange (EDI) integration cycles often required by traditional data marts. Reduce costs by replacing traditional data sharing methods and eliminate the need for copying, transforming, and moving data. Unlock insights with access to live, granular data from across your organization and your business ecosystem.

See Snowflake’s capabilities for yourself. To give it a test drive, sign up for a free trial.