Tuesday, 22 August 2017

Data Integration


Data Integration tools work to draw information from multiple source systems into a consolidated central data store (or groups of data stores) to provide “a single view of the business”. The growing size of data stored in key environments such as data warehouses is driving the need for organizations to deploy more comprehensive integration strategies.

As data volume becomes even greater and data sources multiply, organizations are increasingly turning towards data integration solutions that are capable of consolidating all of this raw data quickly and converting it into useful information for the knowledge worker to query and analyze in a business environment. Nowadays, change is the norm and the business challenges are mounting.

Data integration becomes increasingly important in cases of merging systems of the two companies like, consolidating applications within one company to provide a unified view of the company's data assets. The most well-known implementation of data integration is building an enterprise data warehouse. Data integration is a term covering several distinct sub-areas such as Data warehousing, Data migration, Enterprise application/information integration and MDM. The basic requirement of data integration is for accuracy and adjustability to any type of requirement.

Challenges of Data Integration

At first glance, the biggest challenge is the technical implementation of integrating data from disparate and often incompatible sources. However, a much bigger challenge lies in the entirety of data integration.

Integrating multiple information system creates a unified virtual view to the user’s imperative to the number of system or location of the actual stored data.

Data integration is hard. Every company, we’ve talked to about their data has data integration problem. It’s not just the IT people that moan about it either, it’s IT users too and the company executives. Everywhere datas are almost in a constant mess throughout. Today we have a dedicated sector in industry for data integration solution; it generates about $3 billion in revenue and its growing space. Aside from that there are probably billions more spent on in house data integration efforts.Some of the most significant technical challenges in designing an integration environment involves identifying the technical needs for your solution and determining the combination of protocols and services.

While the goal is always to provide a homogeneous and unified view on data from different sources, the particular integration task may depend on any of the these factors Architectural view of information system, Content and functionality of the component systems, Source Wrapping, Semantic data, Streaming data, Large scale automatic schema matching, Construction of a global schema, Understand Data Needs, Understand Business Timing Needs, Integrate Master Data and Governance Rules, tracking data, fault detection, etc,.

Data Integration Techniques

There are several organizational levels on which the integration can be performed. As we go down the level of automated integration increases. The Data Integration techniques are Manual integration or common user interface, application based integration, middleware data integration, uniform data access or virtual integration and common data storage or physical data integration. There may be some kind of differences in techniques based on Hardware and operating systems, Data management software, Data models, data semantics and middleware.

Data Integration: Key Functional Capabilities

Although data integration functionality can differ somewhat depending on the business and IT problems addressed, there is general agreement that a comprehensive data integration solution includes the core functional capabilities like data movement or transformation, data replication, data quality and data services.


Till today there is no one step solution available in the market which can solve the entire range of problems. Each issues of data integration is unique and needs different and unique approach to solve it. But if we understand the problem properly and know how to tackle it, we can work on the development of a combining algorithm which can at least be able to solve most of the major issues.

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