Data integration is one of the greatest challenges for enterprises during new tech adoption. While adopting new technologies, the conventional controls to integrate data are often stretched to the limit as they don’t support the diverse and complex nature of data. If Gartner is believed, traditional integration models of 2012 will become obsolete in 2018 and won’t accommodate emerging data usage patterns. Organizations will need nexus-enabled integration architecture to support continuum of data velocities created by new and old technologies.
Applications & Data Convergence: The faster you bring data and applications together, the faster you execute your business goals. This becomes possible with a layered approach for application and data convergence. A modern integration platform addresses this overwhelming need by strongly integrating processes and application and data in a uniform manner. On the contrary, it is hard to align disciplines and practices with conventional approaches.
Self Service Data Integration: In the digital era integration should be easily accessible to business users like a service. The service should be available at web-scale and support growing integration needs of enterprises. Traditional integration is tedious, lengthy, complex and requires expert intervention. Businesses can accelerate speed and improve productivity if business users can themselves create workflows for seamlessly sharing data with stakeholders without any IT intervention. It also ensures development, execution and governance of data.
New Challenges Need Modern Approaches: In conventional ETL servers data flowing through a single channel is blocked because of network congestion. Advanced integration tools feature specifically designed connectors to bridge the great divide between new and old technologies.
Big Data Benefits: Entities without modern data integration tools are failing to leverage the advantages of big data. Without a framework for integration, organizations are struggling to deal with a wide variety, velocity, and volume of data. Advanced data integration tools set up a single source of truth and ensure proper usage of data.
Organizations are making a shift from data warehouses to compute frameworks, i.e.., Hadoop, MapReduce, Microsoft Azure etc. for moving huge clusters of data. The Data integration tooling should have an understanding of these newer storage frameworks for efficient data management.
Cloud Migration: Organizations confront several technological challenges while moving applications to cloud. Cloud migration can be simplified if applications & data are prioritized and managed from a centralized interface. A modern data integration solution can simplify data migration by decoupling data from applications, and safely moving it to target source without any data loss. It can also help in creating backup for data.
Social-Mobile-Analytics-Cloud (SMAC) Takes a Big Leap: Enterprises are increasingly using social media for promoting their brand and getting connected to customers. By measuring the social data companies can build better relations with the target audience. However, traditional integration tools doesn't provide right controls for deriving insights from the social media channels. A modern data integration tool can address this need by helping companies in understanding the target audience to improve the products and services.
Continuum of Data Velocities: Organizations should replace the legacy data integration framework if it requires teams to deploy separate change engines to support data velocities. Next-gen data integration tools deliver the cadence required to stream large pools of data and develop responses for discrete business events.
The data is not limited to traditional databases and it travels across object-based and cloud-based systems. Using brute coding force to create data connections and transform data is resource consuming and also not feasible anymore. Organizations should have a credible integration framework in place to ensure continuous sharing of data between different systems and processes. In this way, all classes of data users and analysts can access data stored in any on-premise and cloud-based environment.
Internet of Things (IoT): IoT presupposes effective management of data sets. However, It is impossible to manage Data of wearables and devices with conventional approaches. An advanced data integration tool can ensure real-time availability, visibility, and delivery of data. It can further help in making the most out of IoT investment by aggregating, transporting, and maintaining data.
To siege competitive advantage organizations are deploying different kinds of business systems within the organization. However, they are less sure about managing the data and getting it out of silos. An advanced integration tool can enable business leaders in leveraging data to make impactful decisions and foster new ways of working.
Both businesses, as well as technologies, are being impacted by frequent innovations and changing digital trends. Differences between nature of technologies & data is creating mouse trap for many organizations and hampering their business performance. Staying ahead of the curve requires foundational and end-to-end integration support for near real-time bulk data conversion and data transformation. Organizations must replace their data integration approach if it is barricading the transformation initiatives and failing to move data between enterprise systems.