Harmonizing Data: The Key to Effective Data Blending
With current technological innovations, companies are experiencing increased data traffic, and managing it can be a problem. Data blending is one method to unlock the true potential of the various databases a company uses. Adopting data blending not only increases data accuracy but also enables businesses to stay competitive.
What is data blending?
Data blending is the process of combining data from different data sources into a single data set while maintaining their separate identities. This creates a comprehensive, unified view for analysis.
Benefits of data blending
The following are the benefits of data blending.
Flexibility and comprehensive insights
You can blend data from various systems and databases. This flexibility enables you to adapt and incorporate new data sources as your business needs evolve, ensuring that you can continue to derive value from your data. You can discover trends and patterns that may have been hidden while going through individual data sets in isolation.
Enhanced data quality
Data blending allows you to improve the quality of your data by harmonizing information from multiple sources. By combining data from different systems, you can identify and correct irregularities.
Increased data granularity
Data blending enables you to expand the level of information or granularity in your research. By combining databases with different levels of granularity, such as customer-level data with transactional data, you can do analyses at a more granular level and find connections that may not be apparent when using only compiled data.
Improved decision-making
The perspective acquired from analyzing blended data can lead to better-informed decision-making. You can make choices that are based on a complete set of information.
Time and cost savings
Data blending helps streamline data preparation procedures. Instead of manually combining and reconciling data from various sources, you can automate the blending process using tools and techniques specifically designed for this purpose.
Better collaboration
By merging data from multiple teams and departments, team leaders across the organization can get a more complete view of how their work affects and is affected by other teams. Teams can work with a shared database, fostering collaboration, alignment, and cross-departmental planning.
Techniques of data blending
Data blending techniques involve the following:
- Joining and merging: This approach involves merging databases based on similar domains. Joining enables you to combine rows from multiple tables based on matching criteria to create a unified data set.
- Unite and append: Uniting or appending requires stacking the database vertically, where the structure and fields remain the same.
- Aggregation and grouping: Data blending often requires totaling data from different sources to obtain summary statistics. Aggregation methods such as sum, average, count, min, max, etc., can be applied to blended data sets based on specific criteria or grouping variables.
- Data transformation and cleaning: Before blending, data sets may require cleaning and transformation to ensure data uniformity and compatibility. This can be achieved by removing duplicates, handling missing values, etc.
- Statistical modeling and integration: Advanced data blending strategies require applying statistical models and algorithms to integrate data from various sources.
Tools and technologies for data blending
Numerous tools and technologies can help in data blending tasks. Here are some frequently used ones:
- Extract, transform, load (ETL) tools: ETL tools smooth data extraction from multiple sources, transformation to a consistent form, and loading into a target system or data warehouse.
- Data integration platforms: These platforms offer comprehensive data integration capabilities, including data blending.
- Data preparation tools: Data preparation tools help clean, transform, and blend data from diverse sources.
- Data virtualization tools: Data virtualization enables data blending by providing a unified view of disparate data sources without physically moving or replicating the data.
- Business intelligence (BI) tools: These tools enable you to connect to multiple data sources, combine them, and perform visual investigation and reporting.
Challenges and limitations of data blending
Five common challenges associated with data blending in business include:
- Data quality: Data blending assumes that the underlying detail from multiple sources is accurate, reliable, and consistent. However, the data quality varies across sources, leading to inconsistencies, or anomalies.
- Data governance: Blending data from various origins can raise concerns related to data governance. Different sources may have different privacy policies or ownership rights. This, therefore, calls for more work to be compliant with data protection regulations and maintaining data security.
- Scalability and performance: Data blending can involve processing and merging such large amounts of data that data blending solutions may find it difficult to manage in terms of scalability and performance.
- Versioning and timeliness: Accounting for different versions of databases and ensuring they are up to date can be complex when blending data from multiple sources.
- Limited data integrations available: Not every source of data has direct integration with every other data source. Therefore, it is not always easy to connect the data sources.
Key tips for effective data blending
Here are some best practices for data blending:
- Understand your data sources: To begin your data blending, you must understand your sources first so that you know with what sources you have to work.
- Plan how to combine data: Determine all the necessary sources, designs or formats, and technologies that are needed to help achieve data blending.
- Make sure data is consistent and clean: Successful data blending requires the combining of accurate data into a cohesive and meaningful whole.
- Use unique identifiers to link related data: Using unique identifiers allows easy integration and consolidation of various data sets, helping with a comprehensive and accurate analysis.
- Use common data formats for easier blending: Applying common data formats simplifies data blending by ensuring smooth integration and compatibility among various data sets.
- Consider data volume and performance: Large data volumes can negatively influence blending efficiency by slowing processing speed.
- Test the blended data for accuracy: Testing ensures that the method of data blending produced reliable and trustworthy outcomes, verifying its effectiveness.
- Automate the blending process when possible: Automation enables organizations to streamline data blending tasks and reduce human error.
- Document your blending steps: Documenting your process helps in transparency and facilitates error identification and correction.
- Monitor and test the blending regularly: Determine the accuracy, consistency, and up-to-date nature of the integrated data on a scheduled basis.
Real-world applications of data blending
Here are a few examples of how data blending can be used in different industries.
Patients by specialization
This metric provides valuable insights into the distribution of patients across different medical specialties. By combining data from multiple facilities, healthcare administrators can gain a comprehensive understanding of patient needs, regional healthcare demands, and the effectiveness of specialized medical services to optimize patient care and the overall healthcare system’s efficiency.
Revenue trend
This metric gives an organization a view of their financial success over time by integrating revenue data from multiple streams. This enables the business to determine patterns and correlations that may not be apparent when examining data sets in isolation. By blending revenue data from several ventures, executives can expose valuable patterns and make data-driven decisions to optimize their revenue growth strategy.
NCR reports by project
This metric shows combined data from multiple construction company projects. It shows company executives which projects had and still have the most issues. Executives and project managers can then compare the projects and issues to find commonalities in the projects with both the highest and lowest rates of noncompliance. Plans can be made to reduce the most common issues in future projects, and the highest-conforming projects can serve as models.
Monthly room performance overview
This metric is from the hospitality industry, providing a consistent and regular stream of data. Hotel owners input their own data and then data from various sources in their industry as a whole to compare them. This gives owners and managers a more comprehensive view of how their businesses are doing relative to hotels as a whole. Looking at only their own data would not give them as much insight into market trends and how their hotels are affected by them.
Future trends in data blending
Some of the likely future trends in data blending are:
- Automation technologies, like artificial intelligence, are likely to play a vital role as automating data blending tasks becomes more common.
- The integration of advanced analytics techniques directly into business software will enable more accurate predictions and insights.
- Advancements in integration approaches will happen, with more sophisticated algorithms and tools being developed to combine data from different sources.
- With the increasing application of cloud computing, data blending tools and platforms will increasingly maximize cloud infrastructure to control and process large-scale data integration tasks.
How Bold BI works with data blending
Bold BI offers drag-and-drop functionality and a user-friendly platform. Users can connect to multiple data sources and blend their contents seamlessly, irrespective of their format or location. With Bold BI, users can prepare and create mashups, identifying relationships among data sets and executing transformations with just a few clicks. Moreover, Bold BI provides data cleansing features, such as data profiling and validation, ensuring the quality and accuracy of blended data. With data you can trust, you can focus on analyzing and visualizing your metrics.
In conclusion, data blending is a powerful tool for companies to use to uncover insights, improve decision-making, and drive innovation.
Originally published at https://www.boldbi.com on August 11, 2023.
