It is believed that data blending combines large data from different sources and creates a unique data set that provides high speed and visibility when analyzing data. The combination of data – understand this as a set of data – solves a complex problem faced by many companies. Because companies have been collecting data or more for years, they now have dozens of databases, from Excel spreadsheets to Tableau worksheets.
By combining different data into one data packet, data blending can create a single source for the simultaneous transition to big data software. Importantly, it can help find compelling links between conflicting data sets.
Data – Blending – Explore!
Data blending allows you to view the interrelationships in combined data and gain valuable insights from them while avoiding the time and significant financial investment that goes into traditional data storage processes. This multi-resource collection method allows you to get a more complete picture that helps leaders make informed decisions. It is the process of reducing data from multiple sources and adding them to a single data packet. The real benefit is typically a fast data processing course that can be used by sales representatives and business professionals who own big data certification – for specific queries without the need for IT professionals.
According to the study, experts spend most of their time (about 82.4%) preparing, cleaning, and creating data sets. It means that only 22.5% of data researchers’ time is spent processing useful information from the database. Imagine how much more information your company would get from these analyzes if the collection/preparation process were more efficient. Blending data helps, to some extent, increase the efficiency of data preparation. Here, the workflow of the “data blending” included:
- Identifying the data source you want to link
- Describing some contact metadata corresponding to the device
- After that standard deletion and results.
It has led to interesting results, as the user community has found itself in some blocks in working with the joint efforts of servers and clients. In the data merge scenario, the BI tool performs a connection to multiple systems, and they all ignore (probably) the entire data space of the plug.
Advantages of Data Blending
The number of databases that can be combined into one set is almost exhaustive. It can include traditional databases, CRM systems, human resources, and user data from forms, social networks, marketing activities, and web analytics and usually includes a free mix of structured and disorganized data. Of course, data composition is not without costs. Employees must spend time collecting and disseminating data from a variety of sources. Data blending requires investment in staff time. Also, connecting multiple data lakes to one pool can be more difficult than in others. It can cause complex administrative problems when processing data.
However, in a world hungry for information, data blending in data processing offers the next major competitive advantage.
- Provides faster and more accurate access to critical data and enables the company to collect data faster.
- Makes the application of all types of data more efficient, from direct data mining to more accurate forecasting.
- Provides better information to managers and accountants who work with them.
- Finally, it enables much better decision-making, because the data that guide the decision-making process are better organized and more logical.
Data Blending Steps
Some companies are reluctant to deal with complex data merging. After all, every business department has its system for managing and storing data – both formats and tags. Depending on the job, even a certain combination of project materials can be a hard and time-consuming process. Thus, although there may be many factors (acceptance problems, data retrieval), data blending is generally a three-step process.
- Data Gathering: View, label, and expand all required sets of data sets. The deeper the data collection, the more ideas can be extracted from the resulting data package.
- Data Collection: After collecting a large amount of data, merge these different databases into one central data set, a data pond, or, for large operations, a database.
- Clearing/Deleting Data: In some cases, the data must be transferred in a format that allows storage in a single memory. After all corners of the data have been examined, some data will probably need to be deleted; it is simply not usable or relevant to the company’s larger projects, so it only slows down the entire processing process.
Although data blending is a data collection method for analysis – it takes the process a step further, allowing the user to consider multiple sources in their data set, even if the sources do not have the same scale or inherent dimensions (by combining data from different sources).
When to Use Data Blending
Combining data is usually more useful if you want to:
- Analyze data from different detail levels
- Collect a lot of data at once
- Identify and access the source data used
- Creating data, removing invalid or irrelevant objects, and creating data packages that can be analyzed in the future
Limitations of Data Blending
All the same, data blending limitations are usually in the ETL solution of your choice. An excellent platform recognizes damaged and copied data. It also helps you organize your data so you can use it more efficiently before and after uploading to your destination. Because some data blending tools work better than others, you need to explore your capabilities.
ETL solutions enable data blending – because they allow users to connect to multiple databases for search. After processing, the data can pass through data rows that convert the data. Transformations may include redesigning data to facilitate data processing and understanding.
Looking at the practical experience of the article that presented the idea of data blending, we can see that the idea originated in the computer science community in 2014 or late 2013. At the time, software packages offered a “data blending method” designed to improve the productivity and experience of the main user group of the data interface through a workgroup alone (unlike large users who were able to combine multiple data streams on their own and did not often need this convenience). The principle of this method works as follows: Suppose we are interested in combining spreadsheet data with data stored in a management system. Typically, business exchange experts with a data engineering or ETL team would be needed for this workflow.