Data blending is a process that is gaining attention among analysts and analytic companies due to the fact that it is a quick and straightforward method used to extract value from multiple data sources. This process can also help to discover correlations between the different data sets without the time and expense of traditional data warehouse processes.
The process of data blending begins with the gathering of data from various different sources. Once these valid sources are located and loaded, the next step is to combine all of the useful data in to a functioning data set while leaving out the polluted noise of unnecessary data. The process itself is what is referred to as data blending. The graphic below shows how three separate data tables can be combined into one standardized table.
In the modern business world data blending can be broken down into three simple and fundamental steps:
Data Acquisition: Identify and obtain access to the data within your sources
Joining Data : Combine the edited data for further use and analysis
Data Cleansing: Redesign the data into a usable/functional format and correct/remove any bad data
This three step process can be repeated as many times as necessary to add or subtract data sources that seem fit. With this in mind, there are three essential purposes to this process. The first, is to reveal a “deeper intelligence” within your data. This means that more knowledge and credibility can be obtained from multiple sources as opposed to one. The second purpose of data blending is the capability to put accurate actionable data in the hands of business analysts in a timely matter. The third and final is to drive better decision making skills by senior leaders in an organization. This ensures that these individuals guide the business towards promising opportunities and benefits.
The most prominent environments where data blending is utilized are within sales/marketing, finance operations, and site/merchandising operations. In today’s world it is critical for these business sectors to adopt and integrate tools that will allow their users to visually mash their data so that they will have a competitive edge when it comes to responding to big data flows.
