Data is streaming through businesses at increasingly faster rates, creating time-to-insight and time-to-action data integration challenges which can be crucial to rising above competitors. As fast access to information becomes a greater demand for businesses, a more apparent challenge is in condensing data into useful information for developing insights. Many companies are spending more time and resources on increasing data and preparing it than they are on analysis. Agile, efficient, flexible data integration strategies are crucial to expanding big data analysis. If you're looking for easier ways to handle data integration projects, you may want to try looking into introducing artificial intelligence (AI) capabilities to your data integration strategies toolkit.

Industry experts estimate that data volumes are doubling in size every two years. Managing all of this is a challenge for any enterprise, but it's not just the volume of data as much as the variety of data that presents a problems. With SaaS and on-premises applications, machine data, and mobile apps all proliferating, we are seeing the rise of an increasingly complicated value-chain ecosystem. IT leaders need to incorporate a portfolio-based approach and combine cloud and on-premises deployment models to sustain competitive advantage. Improving the scale and flexibility of data integration across both environments to deliver a hybrid offering is necessary to provide the right data to the right people at the right time.