For many companies today, there is a real desire is to become data-driven. A key first step in achieving this, is to create a data strategy that sets out a roadmap on how to get there.  When defining a data strategy, most companies are faced with two broad challenges. The first is how to reduce risk to get data under control, govern it and build trusted data assets. This needs to happen in an environment where regulation is increasing, data complexity is growing with thousands of data sources and many different types of data store both on-premises and in one or more clouds. The second is how to maximise the use of data and analytics for competitive advantage to disrupt the market(s) you compete in.  It seems that data strategy needs to be defensive as well as offensive.  So how do you do this?  What should be in a defensive data strategy and what should be an offensive one? This session looks at this problem and seeks to find a way to bring defensive and offensive data programmes together to deliver competitive advantage.

• The vision – Becoming a data-driven enterprise
• The data challenge – data complexity, thousands of data sources, multiple data stores, multiple clouds and on-premises systems, ungoverned self-service data preparation and data flows everywhere
• Managing expectations – the promise of building analytical assets using rapid data integration and machine learning
• Key Requirements in a data strategy
  • The need to reduce risk and improve competitive advantage
• Business strategy alignment – a critical success factor
• Defining a defensive data strategy to establish trusted data
• Creating an offensive data strategy to drive competitive edge
• Creating a data platform and changing your data architecture to support both
• Orchestrating data and analytical pipeline components for rapid development