Data Integration is one of significant steps in bridging the silos, not just data silo, but also functional silos, process silos and cultural silos as well.
With the overwhelming business data growth and the reality of data silos existing within enterprise, Data integration may become one of the crucial IT/data management priorities now. But more specifically, what are those top data integration challenges?
- Getting business buy in is not easy, but starting small and demonstrating value. Companies are typically scared of starting large initiatives for fear of failure, so starting small works, but scaling up from there becomes a challenge. If there is a top down mandate, then that makes it easy, the issue there can be if the organization is not ready, and the processes are not mature enough to be streamlined for data integration, then it may not work.
- Streamlining and standardizing data management processes is an essential prerequisite to providing effective integration solutions, and this is where the challenges lie in getting business buy in. Faster time to delivery is a challenge due to the very same reasons.
- Ensuring Data Quality earlier vs. later. Inconsistent / broken business processes often result in significant data inconsistencies. And Data Quality is multiple dimensional concepts, data expert provides a better definition of data quality as “the extent to which the data actually represents what it purports to represent.”. It has Objective Data Quality Dimensions such as : Integrity, Accuracy, Validity, Completeness, Consistency, Existence Or Subjective Data Quality Dimensions such as: Understandability, objectivity, timeliness, relevance, interpretability, trust
- The lack of and low reuse of integration logic depends on the architecture. If integration is done as a point to point exercise for every new interface requirement or new customer, then it is a problem. And a high degree of reuse can be accomplished using both services which abstract endpoint as well as an event-driven approach using a canonical model.
- Lack of strong information/data governance. At strategic level, the goal of strong information governance is to well balance the long term focus with some quick wins. Broad scope and long term focus are great planning tools and overall targets. But you have to have smaller goals that are achievable in a reasonable time within the current business and IT environment. Nonetheless, to keep up with the competition, businesses cannot afford to complete an enterprise model first, before starting to harvest. They need to follow an incremental approach that ensures a return-on-investment within a reasonable period of time.
Like many other things, people are the weakest link in managing data, so empowering people, strengthening process and taking advantage of the effective tool are all critical to overcome such data integration challenges.