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Enterprises are always looking for the best ways to derive the most value from their data across various applications and business contexts. To uncover an organization's most relevant data trends and use them to inform strategy and decision-making, it's often necessary to unite data from different sources. Data blending is one of the best methods for accomplishing this goal.
In this article, we'll explore how data blending works and how it differs from other processes that serve a similar purpose. We’ll also look at situations in which blending data is particularly effective for reporting and preliminary analysis.
What is data blending?
Data blending is the practice of combining data from multiple sources to turn it into a single data set that's easy to examine for meaningful insights.
Primary vs. secondary data
Blended data is the sum of information from primary and secondary data. Primary data is the main focus of the data blending process—for example, it could be something like sales information for every product or service in an organization's catalog.
The secondary data source represents the aggregate of one or more separate sources, all of which have relevance to the primary data. Meanwhile, secondary data includes sub-groups of relevant information, such as records of current clients from a customer relationship management (CRM) system or details from a marketing department spreadsheet listing prospects who have expressed interest but haven't made a purchase.
Rapid analysis
Blending data from these sources into one data set quickly reveals relationships and preliminary data analytics trends that can be immensely useful to business users. In this case, it might be something as simple as demographic details that leads and existing customers have in common.
Because blending takes place as part of—or sometimes before—data preparation, it's somewhat similar to data exploration, also called exploratory data analysis (EDA). The main difference between data blending and EDA is that blending doesn't require significant technical expertise or comprehensive knowledge of data science, whereas a data analyst and or data scientist will usually take the lead on EDA.
Data blending vs. data integration vs. data joining
Because the basic definition of data blending sounds very similar to that of data integration—i.e., the unification of data from different sources—it's possible that someone new to these concepts might confuse one for the other, or think they're interchangeable. Data blending and data joining are also often mixed up.
To avoid confusion, let's look at these three concepts differ and how they may sometimes overlap.
Data blending vs. data integration
Both data blending and data integration begin with the extraction of information from multiple data sources, with the end goal of combining the disparate data for more effective analysis. The key difference is that data blending happens on the spot. It's used when multiple data sets need to be aggregated for a specific use case at a specific time, often to answer a particular business question.
By contrast, data integration takes place over a longer period of time than blending. It's also more comprehensive, as a number of secondary steps must take place before data can be brought to the data warehouse and made available as a unified view. These include data cleansing, refinement, deduplication, and virtualization, among others—the specifics vary based on the type of integration and its ultimate goal. Blended data is often cleansed after sets have been combined, but it's accessible before cleansing.
The other key differentiator between data blending and data integration is in their use of extract, transform, and load (ETL) and extract, load, and transform (ELT) processes.
- ETL: Data is extracted from various sources, such as databases, before being transformed into a uniform file format and loaded into a data warehouse. This is often a key element of data integration.
- ELT: Extraction is basically the same in ELT as it is with ETL. But instead of being converted into a single format after extraction, the data gets loaded into the data warehouse in its raw form. Afterward, the data can be transformed, if necessary, but it's also directly accessible for blending or other operations. It's often faster than ETL, which has helped drive its popularity in recent years.
The spur-of-the-moment nature of data blending means that data must be accessible at virtually any time. As such, the faster ELT process is ideal for blending.
Data blending vs. data joining
Functionally, data joining is very similar to data blending—more so than integration—so it's not surprising that the two sometimes get mixed up.
When an analyst joins data, they take two data sets from the same format—such as sets from separate SQL databases—and present them side by side to combine them. Data joining requires at least one column in each set to be identical. This isn't a requirement of data blending.
Although joining is useful for immediate, direct data comparisons within two sets, the inability to go beyond two sources of data is a serious limitation. Also, joins won't work well with large data sets, and some databases don't support them. Furthermore, blending is better than joining for determining if data needs to be cleansed or adjusted—e.g., have null values filled in and errors corrected—prior to integration.
Benefits and use cases for data blending
4 Key benefits
1. Spontaneous reporting
Ad hoc reporting is perhaps the most common use for blended data. When analysts or other business users need specific data for a particular purpose, they can use data blending tools—in conjunction with database management system (DBMS) platforms—to pull up an aggregated blend. This is especially useful when users need to pull up insights outside of batch schedules or other predetermined reporting parameters.
2. Effective visualization
Data blending tools can present their findings as visualizations, such as infographics and charts, after querying data sources and aggregating relevant information. This is a dynamic way to make data in sets stand out and gain greater real-world context for users.
3. Better collaboration
The spontaneity of data blending, coupled with visualization, makes it an ideal process to help lead non-technical business users to greater insights. For example, using a data blend to highlight a critical pattern during a presentation to senior management will hammer home that point and potentially accelerate finding solutions to business problems.
4. Reduced siloing
It's still quite common for data to be siloed in enterprise databases. Data blending makes it easy to aggregate data whenever necessary, mitigating the disadvantages of siloing.
3 Use cases
1. Marketing
In an era of multichannel marketing, it's crucial to understand how campaigns perform across different mediums. Data blending is ideal for looking at marketing spend for multiple advertising formats—paid search, social media, and so on—as well as click generation, followers, and conversions.
2. Retail
Data blending is useful for sales data in general. But because retail involves wider ranges of products than B2B sales, the benefits of aggregating different retail data sources arguably stand out more. For example, blending makes it simple to compare sales targets against actual sales for many product categories with a single visualized data set.
3. Banking
Major banks with millions of customers ingest massive amounts of data every day from a wide variety of sources. Data blending helps these institutions quickly look at customer details ranging from investment portfolio performance to fintech transactions, so the banks can better direct offerings to their clients and improve customer experiences.
Overcoming potential data blending challenges
Data blending does have certain limitations. While they are far from insurmountable, understanding and acknowledging them is vital.
Limitations of data blending
- Laser focus on visualization: Data visualization is one of the main reasons why data blending is so effective. But in some cases it's the only real feature data blending tools offer aside from the blending itself, giving users few or no data preparation capabilities.
- Compatibility and format issues: Some of the best-known platforms for data blending don't support certain types of aggregation. There may also be restrictions on formats. Unstructured data is the biggest limit, but some blending tools also won't blend some structured data sources, such as logical tables.
- No storage: Blended data is never stored in the data warehouse after it's been presented or visualized on the reporting layer of a blending tool as a single data set. As a result, the blend isn't reusable across different reports. It can only be recycled in copies of the report that originally featured the blend.
A combined solution
The key to success with data blending is not to view it as a cure-all. Use it in conjunction with other platforms to conduct complementary or subsequent operations including data preparation, cleansing, warehousing, integration, and analytics.
Making data blending a key part of a data team's toolbox alongside Teradata solutions—including Vantage, the dynamic, cloud-ready data analytics and warehousing platform—ensures that insights derived from blended sets get used to their fullest potential.
To learn more about how Teradata supports effective data blending, read our case study about how an energy sector client used our Refinery Optimizer solution to effectively leverage data from many sources alongside key financial metrics.
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