Data Blending: Manage Your Data Efficiently and Cost-Effectively

Why use data blending?

Nowadays, data sources are extremely abundant, so many businesses have access to virtually unlimited datasets. Data blending allows you to speed up the consumption of this information without building an in-house team of data specialists. When you blend data from several sources into one dataset, you save time on data preparation and have more time available for identifying patterns and revealing crucial insights. 

Data blending further allows non-technical individuals obtain quick insights into marketing, sales, and finance, among others. For example, you can blend data showing product profitability with data from your CRM system. This shows you which products or services are most profitable while highlighting customer purchasing interest.

Perhaps one of the main advantages of using data blending is that it allows you to identify connections between different datasets once you combine them. Alternatively, analyzing several datasets separately could make it nearly impossible to corroborate your findings. However, businesses have several other reasons for investing in data blending tools, such as:

• Tying together data from different sources allows for deeper data intelligence; 
• Making smarter choices by using the insights from different datasets mashed together; 
• Saving time on analyzing one dataset instead of several different ones. 

How do you blend data?

Brief overview

Undoubtedly, many businesses may be reluctant to spend time on data blending given the numerous formats, labels, and systems that could collect different types of data. Yet, when broken down into specific steps, blending data is more manageable. Essentially, it is a four-step, straightforward process, as outlined below. 

However, some businesses may find it cumbersome to access data in the first place. Even more importantly, effective data blending requires two types of data: internal and external. Primary, or internal, sources refer to data sourced by your business. For instance, this may include sales information collected by your sales department. 

The second type is external, or public web, data. This is independent of your internal data, but it helps you enrich your analysis and obtain actionable research. You can use a data provider to source relevant information or establish an in-house team to collect data by web scraping, for instance. However, an in-house team may require more time and resources compared to data providers, which now provide access to many types of data relevant to any industry or business. 

Depending on your business and type of data, there could be different techniques or methods to blend data. The general steps, however, are data preparation, blending, validation, and outputting. 

Preparing data

Unlike data integration, blending data is not about collecting all the information your business has access to. Instead, it is about creating a unique dataset of carefully selected data that helps answer a specific business question. 

Consequently, the first step is to identify what information may help you answer your question or attain your business goal. In order to combine data from different sources, the information must have a common dimension. 

A wide range of file types or data structures may be available to you, but these must be able to combine into a common structure. Although this could seem complicated, modern analytics technology and programs can do it easily, automatically, and repeatedly. 

Blending data

A quick and easy guide to data blending consists of four main steps:

• Identify the data you need;
• Combine it and customize each entry according to the common dimension of your information;
• Review your included data to make sure each entry is essential to answer your original business question;
• Make sure you include data that could give you additional context.

The final dataset should be seamless and easy to understand, so you can share it with interested stakeholders. 

Validating results

Intuitively, working with multiple datasets can easily lead to accuracy problems, bias, or compatibility issues. This step consists of examining the final dataset to validate it and ensure its accuracy and consistency. At this stage, you should make sure that the dataset is present in the best format for your type of analysis. 

Outputting data

Finally, once your data blending process is done, it is time to use the dataset. Depending on your initial objective, you need to integrate it within the right business intelligence system. For instance, you could add your data into data visualization software, pump it back into a database, add it to an operational process, or perform advanced analysis, such as predictive analysis.  

As briefly stated above, data blending is ideal for non-technical users, because there are numerous data blending tools available on the market. Some examples include Tableau or Alteryx, two solutions for analytics and data preparation. They automate the process outlined above, allowing industry professionals access blended data for quick decision-making.

What is the difference between data blending and data joining?

Data joining and data blending are types of combining data for analysis. Data joining refers to using data from the same source; for instance, you combine data from three different spreadsheets in Excel in order to conduct your analysis. On the other hand, data blending is broader as it allows you to take a step further – you can use multiple sources to collect and blend data. 

In short, data joining is about merging data coming from one source. Data blending, on the other hand, uses different sources, so the insights obtained from data blending are more complex (and, thus, more powerful) than data joining. If you have access to data collected in an Excel spreadsheet, Oracle table, a SQL database, and your data provider, the only way to combine them all is by implementing data blending. 

Benefits of Data Blending

One of the main advantages of data blending is that you have virtually unlimited possibilities to obtain insights since you can potentially blend any number of datasets into a single one. This could include user-generated data from your website, your HR department, CRM system, or data sourced from a DaaS provider, such as social media data, and web analytics. In general, data blending is used with both structured and unstructured data. 

One of the main advantages of data blending is that it allows non-technical individuals to quickly obtain data insights. For example, if you are familiar with Venn diagrams, data blending has the same effect: looking at only monthly sales may not bring you much insight. However, if you combine this with monthly quota data, you will be able to generate a unique insight. 

Next, data blending is quick when it comes to eliminating blind spots. Having multiple data silos is a challenge for many business professionals. Data blending allows you to combine several sources when you need to make business decisions. 

Yet, data blending is not without challenges. Obtaining access to all of this information can be time-consuming unless you purchase your data, and the entire data blending process can also be resource-intensive. Also, some data sources or types may be more difficult to combine than others; data administration and data maintenance are not without hurdles either. 

Conclusion

The emerging importance of data for business decisions means that you also need to reinvent the way you manage and analyze it. A centralized system, data silos, and other data management solutions may not be universally valid. A business is a dynamic entity that needs to stay up to date with market trends, competition, and the changing business landscape, so data consumption needs can be extremely varied. 

Data blending is a process that allows non-technical users to combine data from different sources using software. This provides easy and quick access to the information you need to make business decisions and achieve corporate objectives. As business users require more and more sophisticated access to data-driven insights, organizations must consider how to address these needs sooner rather than later.

This article was originally published on insideBIGDATA.