For many companies, the number of new data sources that a business now wants to analyse is rapidly increasing. In addition, data integration is now happening almost everywhere in the organisation, whether it be for master data management, data warehousing, building data marts, data science projects, real-time analytics or self-service BI. The result of all this activity is that the cost of data integration is rising rapidly, silos are emerging and the complexity in terms of managing a governing data has the potential to spiral out of control. Therefore many are saying to create a ‘data lake’. Put all the data in one place where you can clean and integrate it for any purpose. But data is being collected in many different locations across the enterprise and in the cloud with much of it too big to move. So how do you manage and govern this environment? How do you govern it and accelerate delivery of trusted data that is ready for business use? This session looks at this problem and proposes a new collaborative information architecture to organise, govern, rapidly process and manage distributed big and small data to provision it to wherever it is needed.

• Data integration complexity
• The siloed approach to managing and governing data
• A new inclusive approach to governing and managing data
• Introducing the distributed data reservoir and data refinery
• Goals of a data reservoir
• How does a data reservoir and data refinery work?
• Tasks and services to manage and prepare data
• The mission critical importance of an information catalog in a distributed data landscape
• Managing multiple data integration tools in a distributed data reservoir and data refinery
• The publish and subscribe model for readying information
• Mapping new data and insights into your shared business vocabulary
• Enabling the dynamic data map: managing metadata in a graph database
• Creating an Amazon for Data: ordering trusted data as a service