As consumers generate vast amounts of data and businesses gather it, a crucial question arises: What becomes of this data? If not properly stored, shared, and analyzed, the data might languish on a server, destined for eventual deletion. A 2023 study revealed that only 22% of business leaders believe their companies share data well. This means actionable insights about customer preference may not go to departments that can use the information to grow your business.
Data blending is a popular technique for taking data from different sources and combining it to create more data points and insights for your business. You don’t need a data science degree to do it—just a baseline understanding of what it is (and isn’t) and how the process works. Here’s what you need to know about data blending.
What is data blending?
Data blending is combining data from multiple sources or datasets, often with differing structures and formats, to gain a more comprehensive view. The process enables collective analysis and insights not achievable with individual datasets. Data blending is a practical approach to threading data together into a cohesive whole.
In data blending, primary sources are the core datasets containing the main information under analysis, typically crucial data points. Secondary sources, on the other hand, complement or enrich the primary data source, providing supplementary information or context and enhancing the analysis with dimensions, attributes, or perspectives not thoroughly covered by the primary sources. The combination of primary and secondary sources yields new actionable insights.
For example, an ecommerce electronics retailer that wants to analyze its product offerings might merge a primary source, which contains product sales data, quantities, prices, and customer reviews, with a secondary data source, external customer sentiment data from social media. Combining product ratings from its database with sentiment scores from social media might yield a more holistic view of customer perceptions across channels.
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Data blending vs. data joining, warehousing, and integration
To better understand data blending, it helps to differentiate it from related but distinct concepts:
Data joining refers to combining data based on common attributes within a single database to retrieve specific, predefined insights. For example, you might combine customer information from a “sales” table and a “customer” table in a database based on a shared customer ID to analyze purchasing patterns. On the other hand, data blending integrates data from multiple data sources with diverse structures for analysis and insight.
Data warehousing is collecting, storing, and managing data from various sources in a central repository. Unlike data blending, which often involves combining data for immediate analysis, data warehousing provides a structured environment for long-term storage and retrieval of integrated data.
Data integration is the overarching process of combining data from diverse sources to provide a unified view. Unlike data blending, which can involve merging data at different levels of granularity, data integration focuses on creating a cohesive and coherent dataset by addressing issues like data consistency, format harmonization, and duplicate data.
Benefits of data blending
- A better understanding of customers
- Improved marketing strategies
- Personalized customer experiences
- Identifiable trends
Data blending yokes streams of information that may not otherwise be in conjunction, creating new datasets in the process. This process can benefit your ecommerce operation, including the following:
A better understanding of customers
Combining and analyzing diverse data sources can uncover valuable customer habits that would otherwise remain hidden. For example, combining sales data with customer data (like demographics) can help identify new customer segments, enabling insights into which are over- or underperforming. This can lead to smarter demand forecasting.
Improved marketing strategies
Blending data from different sources can help determine campaign efficacy across channels. For example, you can compare conversion rates from email, paid advertising, and organic campaigns in one place. This can lead to better budget allocation for future spending.
Personalized customer experiences
Data blending can uncover insights that allow you to tailor customer interactions to individual preferences and behaviors. Merging customer purchase history with browsing behavior might allow you to personalize recommendations. If customers frequently browse a product category without purchasing, you might offer targeted discounts to encourage conversion.
Data blending helps spotlight recurring patterns across datasets that may otherwise be hidden. By blending sentiment analysis data from social media with sales records, an ecommerce business can track positive sentiment about an individual product to see if it corresponds to higher sales.
How to blend data
- Identify relevant data sources
- Prepare data for blending
- Choose the right data blending tools
- Implement data blending techniques
- Ensure quality
The data blending process varies depending on your data sets, tools, and goals. Regardless, it typically involves the following five steps:
1. Identify relevant data sources
Determine the data sources you want to blend. They could be in internal databases, spreadsheets, inventory data, social media databases, CSV exports from analytics platforms, or published data sources.
Here’s where you can get creative with data blending. Ask yourself questions you want answers for: How might external weather patterns influence regional sales data? How could customer service records shed light on purchase behavior?
2. Prepare data for blending
After you gather your data, get familiar with its structure, format, and quality. Some data can be messy, containing unvalidated open-text fields, for example. It's crucial to clean and transform data properly before processing: Rectify missing values, standardize formats, and address compatibility issues, such as variations in date (YYYY-MM-DD vs. DD/MM/YYYY) or in units of measurement (miles vs. kilometers).
For instance, if your primary data source uses two-letter state abbreviations (e.g., OH), while your secondary source employs full-text state names (e.g., Ohio), ensure they align during this phase.
3. Choose the right data blending tools
Make sure you have the right tool for the job. Popular options include:
Tableau is a data visualization tool with data blending features, enabling users to combine data from different sources and create interactive visualizations without complex coding. Thanks to its user-friendly interface, data blending in Tableau is suitable for anyone, from business analysts to less data-centric users.
Qlik Sense is a data discovery tool with data integration capabilities that allows users to explore and blend data from multiple sources through interactive dashboards. Its associative data model automatically establishes relationships between data elements as you add or combine data.
Monarch is a data preparation tool that automates data extraction, transformation, and blending from structured and semi-structured sources, such as PDFs and reports. Monarch automates data extraction, cleansing, and blending processes, particularly from data sources that might not be easily accessible through traditional methods.
4. Implement data blending techniques
Identify common fields to serve as match criteria for the blend. Match criteria are the conditions used to combine the data, pairing common attributes. For example, you can use email addresses to match user logins with customer support tickets, generating information about frequent returns.
Having established the datasets and match criteria for the blend, consider the precise data blending techniques. Think of the datasets as squares of paper: Should they align side by side, stack atop one another, or be intricately woven together in strips? Depending on the datasets you’re using, you may want to use any of these more specific data blending techniques:
A left join includes all records from the left (first) data set and matching records from the right (second) dataset. A right join is the inverse: It includes all records from the right dataset and only the matching records from the left dataset.
Combines only the matching records from two datasets, retaining rows where the key values match in both sets.
Retains all records from both datasets and fills in missing values with null or default values for nonmatching records.
Appends rows from one dataset to another, creating a larger combined dataset. Useful for adding data collected over time.
Stacks datasets vertically, combining rows to create a taller dataset with similar columns.
Combines datasets horizontally, adding columns from one dataset to another to create a more comprehensive dataset.
Summarizes data by grouping it based on specific attributes, then calculating aggregate data metrics like sums, averages, or counts.
5. Ensure quality
While you’ve already undertaken quality assurance during the data cleaning and transformation phase, the blending process may have introduced new challenges, such as duplicate entries or inconsistent values.
Ensure the accuracy of results by cross-referencing various entries with their sources. Perform gut-check tests to validate the logical coherence of the new figures. If raw sales data appears anomalous, investigate potential issues with the blending process or incomplete cleaning of specific entries. Rectify these issues to ensure the new dataset is robust, valid, and actionable.
Once you finish this step, you have finalized your data blend, and the prepared dataset is ready for analysis.
Data blending FAQ
What challenges should I consider when data blending?
Challenges when blending data include data quality, compatibility issues, handling duplicates, and ensuring accurate mappings.
Is data blending the same as data integration?
No, data blending involves combining data temporarily from various sources, while data integration creates a unified and often permanent dataset.
What are some popular data blending tools?
Popular data blending tools include Tableau, Qlik Sense, and Monarch.
Are there any resources or courses available for learning data blending?
Yes, online platforms like Coursera and Udemy offer courses on data blending and data analytics techniques.