Data blending: unlocking the power of multiple data sources

Einblick Content Team - December 20th, 2022

As data continues to proliferate in every part of our personal and working lives, it is increasingly important to understand how to bring data from disparate sources together. Data blending helps solve this precise issue. This article will explore the definition, importance, and process of data blending, as well as its benefits, challenges, and potential future developments.

What is data blending?

Data blending is an essential process in modern business that allows organizations to combine data from multiple sources and create actionable insights that can help drive a specific business decision or process. As the name suggests, data blending is more fluid and ad hoc than data integration or data warehousing. While the ultimate goal of data integration or data warehousing is one unwavering source of truth for the organization, data blending allows data analysts to be agile in their problem-solving. Read on to learn more about the data blending process.

The data blending process

The data blending process typically consists of five steps. In this section, we’ll review each step in more detail so that you have a clear understanding of the whole data blending process.

  1. Identify the business case
  2. Find and prepare the data
  3. Blend the data
  4. Validate the results
  5. Share insights

Critical to all five steps is understanding and exploring your data thoroughly, as well as communicating with stakeholders.

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1. Identify the business case

Like any other process, you need to understand your “why.” As stated earlier, one of the defining characteristics of data blending (versus data warehousing or data integration) is that data analysts use data blending for a particular, unique purpose. Data blending is then part of a larger data and analytics strategy. If you’re working with data, you likely already have a problem in mind. Perhaps you’re interested in understanding customer churn rate, or annual revenue, or predicting student outcomes. Within those cases, you may first need to consolidate your data from multiple sources to gain initial insights that can further your work. But without knowing the use-case, you cannot implement a technique. This is true of data blending too.

2. Find and prepare the data

After you have identified the purpose of the data blend, the analyst can identify pertinent datasets from various sources and blend them together for quick analysis. Note that data blending, ultimately does not change the underlying source data. The data blending is an intermediary step towards sharing insights.

3. Blend the data

This involves combining the different datasets and customizing each join based on a common dimension to ensure that the data blending is seamless and produces a cohesive and actionable dataset. In this step, the analyst must think carefully about what data is needed to answer the relevant business question, the desired view, and any fields that may provide additional context or insight. The resulting dataset should be easy to understand and explain to stakeholders. This step is where the analyst's expertise and creativity come into play, as it requires combining and manipulating the data in a way that is both effective and accurate.

4. Validate the results

Once the data is blended, you have to examine the blended dataset to ensure that it is accurate, consistent, and free of any compatibility or accuracy issues. The ability to validate the results of data blending is a crucial skill for any data analyst. In this step, an analyst must cleanse and structure the data for its desired end, review the new dataset to ensure that the data types and sizes are in the desired format for analysis, and carefully review the outcome of the blend with a critical eye. This step is essential for ensuring the quality and reliability of the blended dataset, which will in turn bolster the quality of any insights or analysis gleaned from the dataset.

5. Share insights

The final step in the data blending process is sharing any key takeaways from the blended dataset with stakeholders. This involves presenting the results of the analysis in a clear and concise manner, highlighting important findings and their implications for the business. In this step, the analyst must effectively communicate with a wide range of audiences, from technical experts to business leaders. This step is essential for ensuring that the insights are understood and used by stakeholders to make informed decisions and drive business processes intentionally.

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Benefits of data blending: faster, deeper business insights

One of the key benefits of data blending is that it allows analysts to gain faster, deeper insights into the business. By combining data from multiple sources, analysts can create a more comprehensive and holistic dataset that can provide points of clarity that might otherwise be overlooked. This allows analysts to make more informed and strategic decisions, and to gain a more complete understanding of what’s going on for customers, students, users, or other relevant parties. By combining data, analysts can identify trends and patterns in the data that were not evident from a single source, providing new avenues to follow in pursuit of achieving company goals and initiatives.

New perspectives lead to better business decisions

Sourcing data from multiple places is like asking multiple experts for their opinions. More is not always better as conclusions can get muddled. But different sources of data can help you gain a new perspective, which can lead to better decisions. Data blending can help analysts to uncover hidden opportunities and potential threats, providing a more accurate and comprehensive picture of the business and its environment.

Expanded roles and access to data

Data blending can also open up roles and responsibilities within an organization to analysts. As a pathway for analysts to access a wider range of data, including data from external sources and unstructured data, data blending can provide more opportunities for analysts to start data-driven initiatives as well.

Data blending challenges: management & data quality

Although data blending provides many advantages to analysts, there are some challenges. First and foremost, data quality and security are at the root of any process involving data. When combining data from multiple sources, there is a risk that the data quality may be insufficient or in accessing multiple data sources, there could be an increased chance of security breaches or other risks. In order to overcome these challenges, analysts must carefully assess the quality and security of the data before blending it, and take steps to ensure that the blended dataset is stored securely. This can involve using automated tools, as well as manual processes, to validate the data and protect it from unauthorized access or other threats.

Compatibility and accuracy issues

Whenever you’re working with multiple data sources, whether it’s during extraction or transforming for ETL pipelines, SQL joins, or data blending, you have to keep in mind the compatibility of your data. When combining data from multiple sources, there is a risk that the data may have inconsistent formatting, variables may be stored differently, or there may be missing or incomplete data. This can lead to inaccurate or unreliable results, and can undermine the value and usefulness of the blended dataset. As a result, you have to carefully assess each data source before blending them together. This can involve using data preparation and integration tools or specific programming languages to transform and standardize the data so that it can be blended effectively and accurately.

Managing and maintaining blended datasets

Once the data has been blended, it must be managed and maintained in a way that ensures its accuracy, reliability, and security. This can involve a range of tasks, from updating the dataset with new data to managing the access and use of the dataset by different stakeholders. This can be a complex and time-consuming process, and it requires careful planning and coordination to ensure that the dataset is managed effectively and efficiently. As long as you approach the task methodically, you’ll be well prepared to harness the benefits of data blending, while mitigating the risks.

Future developments in data blending

Since the volume and variety of data continue to increase, data blending will play an increasingly important role in helping organizations understand their data quickly when necessary. As the data blending process continues to evolve and become more sophisticated, there are a number of potential future developments and applications that could emerge. Data blending will also continue to evolve and become more sophisticated, as new technologies and tools emerge that enable analysts to blend data more easily and effectively, and to explore the data and metadata in different ways. These technologies and tools range from data preparation and integration tools, which help analysts to cleanse, structure, and transform data from multiple sources, to data visualization and analysis tools. Some of these new platforms could include the use of artificial intelligence and machine learning to automate the data blending process, the integration of data blending with other business processes and systems, and the expansion of data blending to support new types of data and new business scenarios. Beyond new platforms, existing platforms, like Tableau and Alteryx, are including data blending in their suite of offerings. These developments and applications could have a significant impact on the way organizations use data blending to gain insights and make decisions.

About

Einblick is an agile data science platform that provides data scientists with a collaborative workflow to swiftly explore data, build predictive models, and deploy data apps. Founded in 2020, Einblick was developed based on six years of research at MIT and Brown University. Einblick customers include Cisco, DARPA, Fuji, NetApp and USDA. Einblick is funded by Amplify Partners, Flybridge, Samsung Next, Dell Technologies Capital, and Intel Capital. For more information, please visit www.einblick.ai and follow us on LinkedIn and Twitter.

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