For most organizations today, the complexity around data has increased rapidly. In the area of analytics, we have gone way beyond the on-premises data warehouse and data marts of old and into big data Hadoop systems, NoSQL databases, streaming data platforms, cloud storage, cloud data warehouses, and IoT-generated data at the edge. In operational systems, we now have data in applications residing in data stores on multiple different clouds as well in on-premises systems. Also, the number of data sources is exploding as companies ingest more and more external data such as weather and open government data. Some would say we are awash with it. Silos have also appeared everywhere as business users are buying in self-service data preparation tools without consideration for how these tools integrate with what IT is using to integrate data. At the same time, new regulations are demanding that we do a better job of governing data, and business executives are demanding more agility to remain competitive in a digital economy where the customers can churn at the touch of a mobile phone screen.    

So how can companies remain agile and reduce the time-to-value when data complexity is on an upward trajectory? In this keynote session, Mike Ferguson will look at this problem and discuss how companies can organize themselves and use artificially intelligent data management software to automate data discovery, speed up data processing and use analytics to reduce time-to-value while regaining control of their data. The session will also show how a common approach to smart data management and analytics allows data and analytics to become the foundation for the 'always on’ smart business.