Data blending is essential, let’s face it. Businesses across sectors are making a large portion of their decisions using data-driven marketing. Despite the fact that data volume and complexity are still increasing, agencies and their customers cannot afford to lag behind in the fight for ever-increasing accuracy.
However, both data storytelling and analytics are developing. Decision-makers no longer need to rely on specialized data professionals or expensive, time-consuming data integrations to obtain accurate, useful data. This is where data blending can be particularly useful.
In this guide, we’ll take a look at what data blending is, some examples of it in use, how it can affect data analytics, and why one would want to invest time in using data blending.
What is Data Blending?
Data blending is the process of merging many data sources to form a single, brand-new dataset that may then be analyzed and shown visually in a dashboard or other visualization.
Businesses obtain their data from a number of sources, and users might wish to temporarily combine various datasets to examine data linkages or respond to a particular query. They may "mix up" data using technologies for data blending, which includes data from cloud apps, online analytics, corporate systems, and spreadsheets.
Basically, you're taking data from two or more disparate data sources and blending the data together to build a report or calculation.
The following are a few common methods for data blending:
- Joining Data - This is done when there is a common field between two or more data sources.
- Unioning Data - This is done when the data has the same column structure (a.k.a. the same "header" row) but the developer wants to append or add two or more data sources together in that header row.
To better understand these concepts, let's consider some examples.
When joining data, imagine that you have sales data coming in from an eCommerce platform (as an example) with a "customerID" record. Let's say that you have a CRM in Salesforce with that same "customerID" record. You can data blend and combine those two via the join function so that you can see both the CRM and Sales data in the same report.
Note: There are many types of "joins" as well. Only matched rows from the left and right tables are examined during an "inner join." "Left outer join" examines rows from the right table that match and rows from the left table that don't match. “Right outer join” examines both matching and non-matching data from the right table in addition to matching rows from the left table. “Full outer join” examines every row that matches in either the left or right table. Last but not least, "cross join" examines each and every combination of rows from the left and right tables.
Let's consider unioning data. In a union, imagine that you have a project status coming from a project tracking platform like Jira. The project status data is coming from a homegrown spreadsheet, but the status follows the same structure: "Project #, Task, Assignee, Status". You might want to combine all the records into a single report. You can do so by unioning the data or merging all the rows under the same header row.
How Does Data Blending Affect Data Analytics?
As we noted before, data blending is the process of mixing data from many sources to produce an analytical dataset that may be used to make business decisions or to power a particular business activity. This approach is highly helpful for data analysis since it enables firms to extract value from a range of sources and conduct deeper studies.
Data blending is different from data integration and data warehousing in the context of data analysis in that its main purpose is not to produce a single version of the truth that is kept in data warehouses or other systems of record inside an organization.
Instead, a business or data analyst performs this procedure with the intention of creating an analytical dataset to support the resolution of certain business queries. Data blending enables a data analyst to include data from any source or kind into their analysis for quicker, more insightful business decisions.
Why Would You Want to Data Blend?
So why exactly would one want to use data blending? One common reason for data blending in the context of data analytics is to see the big picture and have everything you need in one report. However, data blending is mostly useful when you want to generate a specific formula that requires pieces from both (or more) data sources.
Let’s consider an actual use case. Just before your client is expected to decide whether to renew your agency's contract, let's say you are a strategist or project manager overseeing a multifaceted campaign that has started to lag in conversions relative to campaign KPIs.
You still have money and time to fix the performance slump, but you're not sure how best to use those resources on updated assets. You are operating a variety of lead-generating campaigns, therefore you must clearly examine a cross-section of the data to determine the cause of the drop-off.
You'd be in a difficult situation without the rapid and effective uses of data blending available today. To swiftly obtain and analyze the data, you would either need to enlist the aid of your agency's data staff or struggle on your own. Both options don't scream optimization, particularly when you have a ton of other tasks to complete. Thankfully, you can use data blending easily.
Just as well, organizations constantly produce more data and have access to more data as well. Without using data scientists or other experts, data blending might hasten the consumption of such data. When you combine data from many sources, you may gain a more comprehensive understanding and uncover crucial insights.
How DashboardFox Can Help in Data Blending
Data blending is indeed very helpful whenever you need something in your report, and you want to have a glimpse of what the bigger picture is all about. It helps you a lot in determining what steps to take and what things to do to thrive and prosper in your business in terms of data analytics.
However, you need a tool that would help you execute data blending seamlessly to avoid any delays in the process that can affect how you run your business. You need a business intelligence tool that can help you provide the best in data blending without any hassles at all.
That’s why DashboardFox aims to be the right tool for your data blending needs.
DashboardFox allows data blending via our no code report builder (we call it the composer). And once the data is blended, we allow you to treat that combined dataset as a virtual data source, so you can then start to build reports and dashboards from the blended data set, just like you could from a data warehouse.
Of course, there are caveats and scenarios where making views or blending data at the database level makes more sense. For example, if the amount of data is so large, it's going to make for a very long-running report and possibly require a lot of server memory to bring huge amounts of data from multiple sources and blend them in memory.
Once data is joined or unioned in DashboardFox, you can then create custom formulas, visualizations, and all the other things you can normally do against a single data source, including filtering, drill down, and more.
Add to that their pleasing pricing arrangement (one-time payment scheme without any subscriptions to maintain) and a dedicated team to handle all your concerns 24/7, you have the best tool in the market in the bag.
How was our guide to data blending? Tell us what you think about data blending and data analytics in the comments.