Introduction to Data Integration
Data Integration could be defined as the collection and combination of data from various resources to provide a unified structure or view of the combined data on which we can manipulate operations, perform analytics and build statistics.
As a whole strategy from the business processes perspective, integration is the initial step towards transforming data into more descriptive and critical data.
There are mainly two types of Data Integration :
- Enterprise Data Integration
- Customer Data Integration
Enterprise Data Integration (EDI)
It can be defined as a properly defined set of technological instructions that help us to manipulate data over two or more data sets.
As the name itself suggests that it typically involves the acquisition of data from diverse business systems and crunching them in a manner that can perform various management activities and business intelligence reports.
Customer Data Integration (CDI)
For a business organization to be successful, their main motive must be satisfying customers, understanding their needs and preferences.
With the humongous amount of data already available, it is quite obvious to assume that there is marginal difficulty in accessing and operating on the data at a much faster pace.
So, CDI is nothing but the process of collection and manipulation of the customer data among numerous multiple sources and framing data in a unified way so that it would be easy to share among every member of that business organization that deals with customers.
Predictive Insight, Improved Customer Service, Loyal Customers are some of the benefits to name under CDI.
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Need for Data Integration
With the increasing volume of data collected through a variety of sources and at a much faster velocity every day, it is very much clear that Data is and has been the most valuable possession.
The businesses are very much keen on implementing various strategies on utilizing the data to complete applications as possible. Still, the real question is how efficiently can that be done. So, let’s understand what it means-
The problem with such an immense quantity of Data is also quite extensive. According to a survey conducted online by Experian, thewhir.com and others nearly 60% of companies today lack a properly functioning business strategy which ends up in resulting catastrophic effects.
Data Integration tends to solve this issue quite effectively by doing a real-time view and analysis of the Data, thus collected from various targets.
- Helps in reducing Complexity in Data
- Increases the value of data crunched through unified systems
- Centralizing the data, i.e. making it more valuable and easy to use
- Collaborations are made easier among various business systems
- Smarter Business decisions are taken due to future predictions
- Improves the communication between different departments under the hood
- Secures your data live by keeping information timely up-to-date
- Better customer experience
Tools for Data Integration
No doubt, the demand for data integration arises from complex data centre environments where various multiple systems are creating large volumes of data. This Data must be understood in accumulation, rather than in isolation. Data integration is nothing more than a technique and technology for providing a unified and consistent view of enterprise-wide data.
There are numerous tools available in the market that would help us query out the Data effectively since our Data is not going to integrate itself. To name a few, we have some Open Source Data Integration Tools, Cloud-based Data Integration Tools and also the On-premises data integration tools.
Again the question is that how to and which one to choose among those various tools available in the market depends on the requirements, platform, type of data that particular business organizations are likely to use it for.
Specific Features of Data Integration tools are
- Connectors – the more the connectors, the more your team will save
- Open Source – open-source contents tend to provide more flexibility
- Portability – build once and run multiple times at distributed environments
- Easy to use – tools you make to integrate should be interactive and easy to use with better visualization
- Cloud Compatibility – integrated data should be open to working natively in multiple cloud environments
Data Integration Architecture
Most of the people, the ones from the industry background believe that there is no architecture for data integration, and that is the reason why most of them termed data integration architecture as like some rhetoric hyperbole.
The biggest reason why data integration needs architecture is Complexity. Since there are a various wide variety of data being collected from multiple business sources in which again are different data models present where it gets crunched into numerous small pieces that are yet equally distinct which doesn’t flow in any particular manner, so chances are it needs proper staging areas. So, if we have to sum it up in a short and precise way, it is a lot of complex and diverse tasks to operate into a data integration bin.
Hub-and-spoke is the most preferred architecture style for mostly all the integration solutions. In this architecture, inter-server communicates and performs data manipulations which are passed through a central hub, where another integration server manages the same task of transformation. Since these are built under the provider’s tool, the server used is the provider’s integration server.
Challenges to Data Integration
- Working with Timely Data – integrating real-time data, without lagging behind the systems
- Remove all data siloes – extracting from and delivering to a wide variety of systems
- Build a smart architecture – processing and enhancing the streaming data to the enterprise platform.
- Data security issues – lack of data security is one of the critical priorities of an integration solution, making sure it is secure and confidential.
- How to get to the finish line – using specific integration tools and principles of architecting the data to accomplish desired targets at a constant pace limiting within weeks or days.
- Keeping up with the industry trends is one common challenge for every emerging and established business organization to match the targets needed to win.
- Data from newer business demands – the integrated data should be readily adaptable to the latest technologies of industry such as IoT, ML, Cloud to excel in the market.
- Leveraging Big Data means using the collected data to its maximum bound, which is highly complex and massive quantity, more the Data, more the leveraging of business.
Data Integration in Data Mining
Data Mining is that process where we operate on the existing data from the database and trying to bring out all the necessary information from that raw data.
Data integration is the pre-processor to fetch data from multiple distributed sources. Then these are stored in a structured manner in the database and using that database; valuable information is coupled.
There are two approaches for data integration, namely a) Tight Coupling b) Loose coupling.
Tight Coupling – In this, the data warehouse is assumed as an interface that retrieves information using the ETL(Extract, Transform and Load) operations from multiple targets into a single centralized location.
Loose Coupling – An predefined interface is provided that manipulates and transforms query in a manner that the root storage can understand and is made sure that no temporary storage is done. Everything acts in the source database only.
Data Integration in Data Warehousing
Data integration is one of the significant aspects of Data Warehousing.
At the highest level, if we talk about Data Warehousing, it is nothing but the innovation, manipulation and mapping practices to match the correct set of requested data with the data to be forwarded as a response to the end-user.
ETL(Extract, Transform and Load) is one of the significant components of data integration in data warehousing. The most well-known implementation of data warehousing is building a data warehouse for the enterprise side.
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As we understand that data warehouse is all about internal operations, but the constraint is that all the integration operations and management are done externally to the organization so to bring them as a collective unit without any redundancy we can use data integration as a local-as-a-view approach where each table in the database is used as a source which is defined globally to a corporate view.
Data Integration in Business Intelligence(BI)
Business Intelligence is the set of operations done to bring out useful information from the raw data available and use it take better business decisions, predictive analysis, tools for identifying data clusters, managing business processes and also the development of better communication to collaborate effectively and support decision-making pointers for better outcomes.
First, the Data is collected and integrated with the data warehouse where it goes under various manipulations. Then the useful Data that is obtained is held under multiple BI tools to support analysis of the data and is stored at a specific location.
BI Tools are often considered as Decision support systems (DSS) tools as they allow the business members to make effective use of it to analyze and extract useful information.
Sometimes it gets complicated as one really would feel that everything is the same, and there’s no key difference among the impact of data integration in mining, warehouse, and business intelligence. The critical link among these is that for everything to work out efficiently, the top priority is the integration of data.
Benefits of Data Integration
The whole data management system is surrounded by a nucleus called data integration which is essential to carry out any expected result. If any system goes through the discussed methodologies, they are expected to taste numerous fruitful benefits of Data Integration.
- Better Collaboration and deployment
- Availability of real-time integrated data
- Data from multiple distributed sources
- Data Integration helps in achieving better partnerships and customer relationships
- Saves Time, Boosts Efficiency and Reduces Errors
- Making Excellent Business Decisions
- Adaptability, Reliability, and Reusability can also be considered as one of the key benefits.
Everything in an organization from business processes to analytics, warehouses and anything that is either way directly or indirectly dependant on Data is nothing without data integration. So, the business organizations and its members should have full knowledge and access to every Data set from every Source to grow as a collective unit.