Data Blending
- Data Blending defined
- What is Data Blending good for?
- Data Integration or Data Blending
- Are there any drawbacks?
- Better Data Blending
What is Data Blending?
Data blending is combining multiple data sources to create a single, new dataset, which can be presented visually in a dashboard or other visualization, and can then be processed or analyzed.
Enterprises get their data from a variety of sources, and users may want to temporarily bring together different datasets to compare data relationships or answer a specific question.
Data blending tools let them “mash up” data from spreadsheets, web analytics, business systems, and cloud applications, among others.
What is Data Blending good for?
The data created by or available to organizations is always growing. Data blending can speed up the consumption of that data without involving data scientists or other specialists. When you blend data from multiple sources, you can get a deeper view and reveal important insights.
Data blending tools can give non-technical users rapid results in areas like sales, marketing or finance. For example, users in the marketing department might blend data from a CRM system and a spreadsheet with product profitability information. They could then quickly see which products not only make the most money but also attract the most customer purchasing interest.
Data blending tools will lead to faster, more data-driven decision-making by senior management. When you go beyond standard reporting by connecting (blending) your data sources (a platform like Sisense works great for this), you can start using visualizations to view your data, like a reporting dashboard that automatically populates with real-time data. And that information can be used to reveal all kinds of insights, like churn calculations, close rates, or sales funnels.
Basic steps for blending data
Setting up your data blending operation can be complex. Let’s break it down into manageable chunks.
First, collect your data. You may have to collect the information you need from diverse sources like Excel spreadsheets, cloud and non-cloud databases, Google analytics, or social media tracking apps. Plan ahead, because it may take time to get the relevant access and permissions for everything.
Then, it’s time to join the data. Here’s where a BI platform like Sisense really comes in handy. Combine your sources and load it into a destination, like a data warehouse, where it will be accessible by everyone who needs to look at it.
Finally, clean and refine your data. Remove what is incomplete or incorrect, and modify the rest so it is properly formatted, and optimized for the best and most accurate analysis.
Ways to combine data
If you want to create charts, reports, and visualizations based on two or more sources, you’ll have to blend your data. You have a few different options of how to do this.
The most common data combining method is via relationships. Using relationships, you can join data across multiple tables, making it the most flexible and dynamic way to combine data sources. This is also the easiest and most intuitive method.
Joins are another available option for combining your data. Joining tables means physically defining the data and merging it into one single table. You can use a Venn diagram to display your data joins. This method is best suited for data combinations that can use a single table, like row-level security and extract filters.
If you want to analyze data from already published sources, the best method to use is the data blend. Blends individually query each data source separately and present the results in a visualization. The data sources are never physically joined. This makes sense if your tables have different level of detail, or if the data needs cleaning.
Data Integration or Data Blending?
Use of these terms may vary. We use data integration to mean more in-depth data cleansing, refinement, and combination from different sources. For instance, an IT department may use database joins to integrate data into a single dataset.
This new dataset may then be made available in a data warehouse for general use. By comparison, data blending is often done on the spur of the moment to answer a specific question.
Once the question has been answered, the blended version of the data may be discarded, leaving the original, separate data files intact.
Differences between primary and secondary data sources
Data blending necessitates having one primary and one or multiple secondary data sources. Your primary and secondary data sources must first be defined, before you start combing your data. You’ll need one primary source (original data that was directly collected from your users), which will be combined with one or more secondary sources (previously collected data).
Your primary data will generally live on the left side of your data blend. This standard allows for data analysis that contains different levels of granularity. It also makes it easy to compare data from different databases that measure disparate fields, which could save you a lot of time in data preparation and creating new datasets.
Are there any drawbacks?
Some data blending tools may not preserve all the data detail when combining datasets. For example, data visualization software may do blending by simply aggregating data. In this case, users will get rapid views and summary information from the combined data.
However, more in-depth data exploration may not be possible. Users may not be able to ask ad-hoc questions, which in turn could limit creativity and innovation.
Better Data Blending
More capable data blending tools can make it easy to combine data, while retaining the details of each data element. Logical connections between the elements can also be maintained.
Users can then explore data on the fly and make new calculations for more insights. They can ask new questions of their data as they want, without depending on their IT department to do more complex database integrations.
