There is actually no universally accepted one single approach of Data Integration but there are some elements which define it, that are, a network of Data Sources, a Master Server, and the clients who must be accessing that data from the Master Server.
What is Data Integration?
Data Integration is, in fact, a process of gathering relatively random information from different sources and combining in such a pattern that it becomes imperative and significant and provides a unified version of data to the clients. IBM states a strong definition of data integration in the following words.
“Data integration is a combination of technical and business processes used to combine data from disparate sources into meaningful and valuable information.”
The thing to ponder is converting the randomly available data into something useful, it is not just about moving and copying data from one website to other but the real meaning and purpose are to make the unified version of it which will be constructive and functional for the client. The technical and the business processes being mentioned in the definition refers to the methods which help to bring the data together into an integrated view, and there are many techniques for bringing the data together physically for an integration process.
The course of action starts from ingestion, moving forward to cleansing, mapping the data, then transferring it to a useful sink and the final step is to make the data into something valuable and understandable for those who are accessing it. Data integration is a must for today’s businesses in order to improve their decision-making strategies and to enhance their competitive edge over their competitors.
Data Integration for enterprise:
Enterprise of these days is paying extra attention in data integration in order to analyze their data more efficiently so that they can act accordingly especially when there is an explosion of data in the world of big technologies with new clouds of data available.
Breaking the whole procedure down into non-technical terms for common people, In a typical Data Integration Process, a client sends request to the head server about his needs and head server collects the required data from internal and external sources, after collection, the data is being unified into one single form, that is being sent back to the client in cohesive and exploitable form.
Why Data Integration Is Important?
Data integration is a procedure being required when the company decides to implement.
Let’s say a company is already receiving all the data which is requisite for proper functioning but the data must be departed into different segments and separate data sources. For example, the data must be from web traffic, Customer-Related Applications, CRM System, customer and sale related system, just naming few of them, all the data must be available in different sources and what if you need to analyze the data and take some operational actions accordingly, do you think that would be an easy task for the engineers and the developers to bring all the data in an amalgamated form on your table? No, it is not that easy.
Without integrated data, the whole procedure will contain logging into millions of accounts to get the desired information, multiple sites visit, copying data from different sources, reformatting of data and cleansing to have what you need, and many other steps before analysis can happen. Is it even practical? Who got that much time in the 21st century?
Unification of system:
Employees from every department of an organization are creating and producing some sort of data which is needed by the company, so having it in the unified form will make it easy to have access to it and getting the use out of it whenever is required.
As it the time of internet, every employee is working on the internet and they need to have access to other’s work, in order to perform their task, the work is being done in collaboration now, so IT has developed the secure solution for individual’s and company’s needs.
When the company employees used to integrate the data manually, that used to take more time than analyzing and preparing the data, but now with the application of data integration the process takes few minutes and the employees now do not have to build everything from scratch every time the management has to analyze the data. Now the precious time can be invested in the betterment of the organization through productive and competitive activities.
Manually collecting the data does not only take more time but there is always a chance of messing up. The collectors must know every location, every site and the accounts the management might need and they also need to keep their selves updated with any new addition in the data they are working on, which is not an easy task, but when the whole work is being done through a software, it keeps everything updated and the work is error-free as well.
Focus on voluble data only:
Value data is required for proper analysis and through quality issues being identified and the solution being enforced, that eventually results in more precise data. Data integration has actually improved the quality of data and even the business itself over the period of time.
When is Data Integration required?
Data integration is a procedure being required when the company decides to implement a new application and wants to transfer the data from your old servers to the new application. For this purpose, a completely unified form of data is essential to make the procedure go flawlessly.
And its need becomes even more crucial when one company decides to merge with another company and they come to a decision to work in collaboration. In that case, the whole data of both of the companies are required to be a synchronized and coordinated so that employees of both the organizations can take benefit of available data and can produce something new out of it. Building a data warehouse for an enterprise also desperately requires the data integration which will enable them to have a unified view of their own data for analysis or business intelligence needs.
Data integration Approaches:
These are some commonly known approaches to data integration:
Extract, transfer, and load which is called ETL Technology, it is being used for data consolidation, which will help to clean, filter and transfer data before implementing any business rules or formulas on them. Bringing of data physically from different data sources and creating a version of consolidated data in one data store is the main priority of data consolidation. The goal is to reduce the number of data storage location.
Virtualization interprets and retrieves data; provides its users with a nearly real-time view of unified data from different data models but unlike the other process, the complete set of data is not available in the single location although the client will have the access to view all of it simultaneously. Single point of access is not being required by data virtualization.
It is the process of copying data from one location to another. There are two ways of data propagation, the first is synchronously and the second is asynchronous. Two-Way data exchange between the source and client happen in synchronously data propagation. Enterprise Application Integration (EAI) and Enterprise Data Replication (EDR) can work for data propagation as well.
Enterprise Application Integration is often used during real-time business transaction processing and exchange of messages. Integration platform as a service is the updated version of enterprise application integration.
To transfer a large amount of data between databases, enterprise data replication is being used instead of applications. Logs and base triggers are used to keep a check on the exchange of data between source and database.
Data Federation works like Data Virtualization. The main technology used by it is Enterprise Information Integration (EII) and it uses a Virtual Database that helps to create a heterogeneous data from different systems and then data is brought together and is being shown from a single point of access. It uses data abstraction to provide the unified version from one source and then data can be presented and analyzed in many new ways through applications.
Data federation and data virtualization are two approaches which comes really handy when data consolidation is out if range or would cause too much security/ compliance issues.
Warehousing is the only commonly known term in these approaches but it is added here because it is more generic than other procedures mentioned above. Actually, data warehouses are used as storage repositories for data but the term “Data Warehousing” is commonly used for cleansing, reformatting, and storage of data which is in short data integration.
Data integration for Modern businesses:
Even though the procedure of data integration is being to take the businesses to the next heights but it is not a “fit-all” formula, specific solutions are available for every problem of every kind of business.
Creating data warehouses:
Large businesses often use data integration procedure in order to produce warehouses which will combine multiple data sources into a rational database for their use and data warehouses will also allow the users to run quires, reports formation, generation of analysis and retrieving of data into a relatively simple tasks and the time being invested on all of these tasks would be drastically less as compare to the whole manual system used to take before.
Leveraging Big Data:
There is an expulsion of data these days, in big organizations like Instagram, new data is coming every second, there are billions of users out there and coming up with all the information, keeping the system up to date is a hard job to do, for that propose data integration works for such highly complex and massive volume of data and tries to integrate the data as soon as possible for organization because as more data crop up, the more data is available for business to leverage and the nowadays organizations need sophisticated data integration, that’s central to operations for many of them.
Simplifying business intelligence (BI):
Business Intelligence is being simplified through data integration as it provides a unified view of data from various sources and compiles it on one single source. That makes the high volumes of data easy to evaluate and the organizations can easily view and comprehend the available data in order to bring the required changes and focus completely in production and bringing the best for the company.
Business analysis can help to predict the future of organizations by looking closely at the facts and figures along with the strategies the company is working on but business intelligence does not work on that same formula. It can focus on describing the present and the past to aid in the decision-making process or to change the strategy.
ETL and Data Integration:
ETL is a process where data is taken from the source system and is delivered to the warehouse. That’s an ongoing process. Data warehousing compile multiple data sources into useful, understandable and consistent information for BI and analytical efforts to make changes in the policy or the strategy to maximize the output for the organization.
Data integration techniques:
ETL is not the only way for data integration. There are many refined ways through which unified data can be created. There is a new level of complexity in the field of data integration now and many new techniques are being introduced every now and then.
Middle-ware Data Integration:
Middle-ware is the source of connectivity between two disparate systems and allows them to communicate in terms of the transformation of data, holding multiple legacy applications, and without requiring the two applications to communicate directly.
Data virtualization integration:
Data virtualization allows users to access the unified view of disparate data from different source systems and create a new set of unified view for them across the whole enterprise. A lot of companies are inclined towards this approach because this eliminates the need for a separate data source for consolidated unified data and the main benefit of data virtualization is “near real-time view” of data from sources.
That’s not the best technique for data integration, as it is short term but it includes the limited possibility of a history of data’s availability. Or extra load which on the source systems which may have an undesirable effect on the performance of source systems later on.
Data warehouse technique of data integration:
Creation of new data warehouses which will store a unified version of data extracted from all the different sources comes in this technique. Even though it is the most commonly used technique, it has its pros and cons. Benefits might include the ease of managing history, combining data and to store them in the central repository of data but it requires the formation of the whole new setup for the process of data integration to happen, that can count as its drawback.
Data integration problem and solution:
Data integration is the process of finding the relevant data from multiple sources and combining it in one particular manner to make it look understandable for clients and useable at the same time at compiling it at one single source for easy access purposes.
Does this sound simple and everybody’s cup of tea? Well, that’s not true. It’s a quite complex job because it requires technical knowledge. And there are hundreds of problems related to data integration in the practical field when it is providing you services to save your money and time. Few data integration problems are solution are mentioned below:
Have you ever been in a situation where your boss assigns you a very important task but you have zero knowledge about it? Yes, that’s how some companies work with data integration. They know what want from it; the solution to one particular problem but what is the right route to get to that point? They have no information of it, Well there’s where the technical team play its role and try to find the right path, right kind of data, right sources, right systems that will use the data, a right type of analysis, a right frequency for an update. All these tasks are being taken care of before reaching the final line successfully.
Knowledge of data:
Not everybody can understand the nature of data required for every single client, it requires a team of skilled people who has a grip over data assets of the enterprise and the source systems along with the capability to lead the discussion about long term integration to make it booming and consistent.
Source system and extraction:
Frequency of extract, the extent of data extraction, quality of data, all this is not common knowledge, but it affects the timeline and direction of the project so having someone with an understanding of options of extracting data from the source system is significant for the success of the project overall.
Legacy system and external data:
Modern systems include the makers like time and dates of activities which were not present in legacy systems, integration data may need to include data from them, so that creates a problem at that stage. But with the advancement of technology, this issue is being taken care of.
Data which is being taken from external sources is not detailed and reliable as internal sources which make it difficult to examine and when an external vendor is involved, sharing data with the organization becomes a little difficult.
Once the integration system is up, that’s not the end of the job. Keeping it consistent is as important as installing it, keeping up with demands from organizations and updating their data time to time is also essential.
Selecting the right tool for your project is very important because the success of the project depends on the tool and techniques which are going to be followed by the users. For small new businesses, it might be an issue to select the right tool but the knowledge and applications available online is of great help, you just need to do the research to find the right applications which will be suitable for you. Even though the old companies are also in need of updating their tools, as ETL is the only technique they are familiar with, but now there are hundreds of new and update ones in the market with advanced applications, so organizations need to keep themselves modernized in order to take advantage of technology thoroughly and to maximize their outcomes.