The amount of data that enterprises are generating is mind-boggling. It is estimated that 45,788 Uber trips, 600 Wiki page edits, and 51,892 peer-to-peer Venmo transactions are being completed just in a single minute – spurring huge volumes of data. Enterprises will require extreme computational force to process this high-dimensional, semi-structured and heterogeneous data coming from wide sources. We’ll look at the ways that how self-service integration helps companies to scale-out fast and productize an intensifying barrage of data without costly IT support.
In the last few years, we have seen enterprises moving away from traditional data warehouses to cloud based data warehouses. The reason behind this global exodus is lower operational cost, agility, and improved performance. Most of the technologies that organizations are referring generate a colossal amount of data. This data generated by machines, sensors, and other social technologies is not always structured. It arrives from diverse streams in unstructured, high dimensional or semi-structured formats. It becomes really challenging for even the IT to store, collect, prepare or visualise this data. Moreover, increased sources of data overburden the data processing engine and slow down the data exchange operations. All this can be simplified, if integration becomes code free and manageable by business users.
Self-service B2B data integration helps operational users, i.e., business analysts and data scientists to build reusable integrations without lengthy rough IT code marathons. Business users don’t have to rely on IT for getting the integrations developed. They can build integrations in few minutes rather than days or months. In this way, they can bring data from any source into the production line, become faster to work with, and accelerate time to revenue.
Self-service integration suites pack simplified user interfaces that are interactive and easy to use. Business users get predictive interaction features that use past traffic or meta-data to make custom recommendations for data transformations. Auto profiling ranks data transformation options in an order as per a business users’ interest and preference. This transformed data can be cleansed and executed across all layers. Business Users can now roll back or undo transformations in simple steps.
Visual discovery tools allow business users to blend data from different source systems for specific B2B transactions. In this way, business users can clean and integrate data from fragmented sources in simple ways.
Self-service gives business users the speed required to manage, refine, analyse and provision data. They can now select a range of options for information management including data loading, validation, standardisation, data integration, data enrichment, data masking, data encryption, etc. Both business and IT teams can define B2B processes, onboard customers, and analyze data in a seamless way.
If business users are building their own integrations, what will be the impact on enterprise data governance? Many enterprises are struck at the threshold of this question. They fear that business users creating integrations can lead to data chaos. The reason behind this fear is hesitancy to change and heavy reliance on traditional API led integration approaches.
In many organizations, integration initiatives are driven by centralized IT teams and part time data stewards who are scattered at different places. In such a fragmented environment, API led integrations lead to inconsistencies and silos. Teams develop something with less clarity to what other team members are building. However, this problem doesn’t persist with modern self-service integration tools. Modern-day self-service data integration tools maintain a metadata lineage in the repository which ensures consistency and coordination between teams working in different places. They also get access to standards for metadata import/export to re-use integrations and transformations across the B2B network. IT teams can use this platform to assume governance role and monitor data movement in different dimensions.
Enterprises today are generating data at a breakneck pace and managing this data can be an over drive. Gartner recommends organizations to implement self-service integration capabilities to ensure quality and consistency of services. This approach makes integration between internal teams, customers, and suppliers faster and reliable. Seamless connectivity optimizes data utilization, lowers operational cost, and improves time-to-market for competitive advantage.