What if we give you a bunch of books and ask you to retrieve a single piece of information from it. It is no doubt difficult and time-consuming to do so. But, what if we take you to a library where books are arranged in a systematic manner and ask you to do the same thing? It will be easier for you to do the same. Therefore it doesn’t matter if we have the data, it matters how we integrate and classify it and make it apt to use.
What is Data Integration?
Data Integration is the process in which we combine data from different sources and providing users with a unified view of them. Trusted data such as documents, data sets, and tables from various sources are accumulated and merged for personal or business related issues. This process is significant because we are not talking about few megabytes or gigabytes of data but about tons and tons of terabytes of data which may need integration when two similar companies try to merge their databases or any other reason which may need data integration. It has presently become the focus of lots of theoretical work and a number of open problems are still unsolved.
Data Integration supports the analytical processing of large data sets by combining, aligning and presenting data sets from organized departments and external sources. There are various types of Data Integration, such as –
CDI which stands for Customer Data Integration – Customer Data Integration is regarded as one of the earliest application. It is the process of amalgamating and managing customer information from all the sources available which includes contact details, customer valuation data, and other information which is gathered through direct marketing. CDI software ensures that the operators and departments dealing in personal information can access the most recently reviewed and complete customer information.
Sensor fusion integration – Sensor fusion amalgamates data from multiple sensors to add context and achieve a more and detailed view of the sensors’ subject or environment. Sensors detect some type of input from the physical environment and respond to it which in turn may start a chain of events which provide information or guide to another system.
It is generally implemented in DWs or Data Warehouses through specialized software that hosts large data from internal and external resources. Data is then extracted, integrated and then presented in a unified manner. As the IoT (Internet of Things) develops and grows further than the amount of data collected is also expected to grow exponentially. However, the challenge is in realizing the potential value of the data. Therefore Data Integration and analysis are essential to achieving that result.
The History of Data Integration can be dated back to a few years back. Combining heterogeneous data and compiling them for ease and future use has existed for some time. In the 1980s, computer scientists began designing systems and software for interlinking random data and forming databases. The University of Minnesota in 1991 designed the first data integration system which was driven by structured metadata for the Integrated Public Use Microdata Series (IPUMS). IPUMS used data integration for hosting a lot of data which at will can be extracted, transformed and load data from a heterogeneous source into a single compiled manner where data from various sources is compatible. IPUMS demonstrated the feasibility of large-scale data integration by making lots of databases interlinked and interoperable. With the passage of time, this process grew old and time-consuming so we moved to a more easy data integration method.
Since 2011, Data hub approaches have been put into use but after 2013 Data lake approaches competed with the earlier process and proved to be a better method.
Need For Data Integration
There are various reasons as to why the need for Data Integration arrived, Let us analyze a few of the reasons–
- Every Data types have their own purpose and benefits. Each Data format represents data in a unique way in which no other data formats can represent the same. Integrating data from different formats of data adds clarity to the data which can be used and understood easily. For example- The Google Map integrates road congestion real-time data with the normal mapping system. This is also a kind of Data integration.
- Every software functions in a specialized manner. And believe it or not, each of them works differently though they may look like they are doing the same work. Each of them represents, analyzes and transforms information in a specialized manner.
- It helps to reduce data complexity. Just as we have discussed earlier integrating data is a lot easier to navigate around than an un-integrated and a “hotch-potch” of the given data.
- It increases the value of the data. Having a detailed information about any given topic or having the complete databases of customers rather than having arbitrary information of customers or non-compiled data about any topic is more convenient and user-friendly.
- It helps in making data more easily available and easy data collaboration.
- Taking smarter decisions in the field of business when you have clearer data and having a chance at predicting the possible future from earlier cases.
So you see there’s a lot of benefits of Data Integration. We have only discussed the ones which people consider to be the main ones.
Bottom Line -Integrating Data is a huge process which includes and involves correct and near precise Analysis of Data, Process Flow Modelling, Exploring the alternative methods, Evaluation, and Selection of Data. After which there comes the Database Designing and other specifications. And then, finally Development, Testing, and Implementation of the Data Base. With the invention of data integration techniques, our life has become sorted. Companies do not need to employ extra people for the management of that big data as software tools are there for proper and effective data integration. This can help large business to cut down those unnecessary heavy costs and help them get the best results.