Anyone with an Internet connection produces large amounts of data every day. This data is uploaded and downloaded by millions of people everywhere in the world. It’s very hard to comprehend just how much data is travelling back and forth. That’s where the term Big Data comes in. Although individual predictions will differ, the amount of information we generate and store is multiplying at an incomprehensible rate.
There have been many technological advancements that make information gathering and storing easy. From websites, server, networks, applications, sensors, mobile devices and social networks – all of them have been used in information gathering and storage. Companies and enterprises have no choice but to develop and implement strategies that will help in managing the ever increasing volume of data and better yet, make a profit from it. There are a number of people who doubt the opportunities brought to us by Big Data.
As data volume and data complexity grow, it will become harder for any organisation to extract meaning from their collected data. One emerging solution that’s gaining attention is Data Blending. Data Blending provides a new real-time, analytical-oriented take on the batch-oriented ETL data integration process. Although Data Blending has a huge potential, it can be hard to adapt to traditional structures of information because of EDW or enterprise data warehouses as a central information hub. Some experts find it hard because they say that it doesn’t make sense to move Big Data into the EDW and then analyse it in the same way as relational data. It would be impractical and time consuming because of the structural variety and the volume.
But for many experts, it’s a reliable solution because it can provide a quick and straight-forward extraction from multiple data sources. It can also find patterns between them without wasting time or expense, as compared to traditional data warehousing processes.
Data Blending Defined
Data Blending is defined as the process of combining data from multiple sources to create an actionable analytic dataset for enterprise decision making or for driving a specific business process. It can be applied in retail site selection or to multichannel profiling and packaging data for sale by data aggregators. This process is required when a business organisation’s infrastructure and data management are not sufficient enough for bringing together specific datasets required by business groups. Data Blending can bring together any disparate data just like customer information from a cloud sales automation system, segmentation models and click stream web data.
Data Blending is different from data warehousing and data integration because the primary function and use of it is not to create a single unified version of the truth that is stored in data warehouses or other record systems within the organisation. Data Blending is a process conducted by a business or data analyst with the goal of creating an analytic dataset in assisting answers to business questions and queries.
Uses Of Data Blending
Data Blending implementation into the business line can give many benefits and many insights within a few hours. It can make processes significantly faster than traditional IT approaches. Below are some examples of Data Blending advantages in business.
1. Sales And Marketing
The good thing about Data Blending is that an analyst can access data directly from the environment where it is located. An analyst will be only need to have the right credentials to access the data needed, pull out the data from the right systems and then combine the data in common fields, blending the specific information that they are looking for. For example, you can combine data on customer identification, or you will be able to understand what services or products are having the biggest impact on sales and pique the interest of potential customers and loyal followers.
Data Blending minimises the time to insight from weeks into just hours. This allows analysts to work with the data directly to improve quality, plus it can be combined to produce a usable format to be fed directly into any existing model. Analysts within a financial environment understand how crucial is it to acquire the right information and deliver the correct reports.
3. Site And Merchandising Optimisation
One way to maintain a successful store or business is to understand your potential customers and prospects. You can do this by looking at your customers spending levels, purchase history as well as other metrics. You can also use data for insight targeting, media planning and other multi-channel initiatives. One way to do this effectively is to analyse data from 3rd party providers like Bradstreet or Experian. Combine this with internal data in identifying the factors that indicate the highest probability to purchase or buy. This can include age, ethnicity and consumer spending on goods and services.
In conclusion, while traditional data analysts use IT tools to create reports from historic data, present day analysts should extend that capability with their business insight. With improvements and enhancements in information technology and Big Data, new opportunities for businesses have appeared to make processes easier, and this is where Data Blending comes in.