Why does one need to integrate data since data is designed for a specific function and format? Can we not maintain them as individual blocks of information and infer on a need basis? With large volumes of data to maintain and manage, does data integration offer the perfect solution? IBM defines data integration as the discovery, cleansing, monitoring, transforming and delivery of data from a variety of sources. By bringing disparate sets of data together, the strength of the information increases manifold since you now have meaningful information and insights that can be derived from this block. Another key aspect of data integration is that it opens the door to innovation, collaboration and transformation through knowledge sharing. However, while there are a number of positives to the data and application integration process, it also poses a number of challenges within the organisation.
Data Integration Process: The Challenge
The dynamic business environment in which we live today has made data integration an absolute necessity today. The value of data increases manifold when it is brought together to enable you to see the big picture. However, there are challenges that data integration brings with it.
Data integration is often confused or used interchangeably with systems integration and business integration. According to REMEDI, “Data integration is the collection and integration of electronic transactions, messages, and data from internal and external systems and devices to a separate data structure for purposes of cleansing, organizing, and analyzing the joined data.”
Top challenges of Data Integration
One of the most basic issues that follow the integration of a vast amount of data from different sources is to understand the behaviour of the target and source systems. Next, comes the most cumbersome and critical task of actually mapping the data structure between both systems. Now that the structure has been mapped, the team now has to fit the data in the new system with customizations or modifications of business rules if required. In legacy systems, data is maintained in different formats. Segregating large volumes of data and making sense of it and its association is a time driven process that requires in-depth analysis. The final challenge of data integration is to see if this data is performing accurately. So the quality of data is important. What you feed is what you will get as output.
Last, but not the least, data security has to be in place to safeguard it from malware or other threats that can compromise sensitive information. The consequence of incorrect data integration has an enormous impact on the organisation. It can cause huge business loss and impact customer relationship.
To avoid such pitfalls, data integration should be planned well ahead. The responsibility does not stop with bringing the data together; the post maintenance and monitoring activities should also be considered as part of the long term plan. So the entire process including preparation, integration, verification, validation, and monitoring effort, as well as investment in additional infrastructure must be accounted for thoroughly. The process should also allow for continuous improvement and must be elastic enough to accommodate any unforeseen risks.