Keynote Panel: Succeeding in the Cognitive Age – Exploiting the Potential of Artificial Intelligence & Beyond
• What does a ‘Cognitive Enterprise’ look like in the real world, and what are the steps to becoming one? • How mature are Artificial Intelligence solutions currently, what can they achieve within the business context, and what should we prepare for in the coming years? • Where to begin – Approaches to acquiring the… Read more.Speaking
Discussion Group Session 1
• Exploring eco-systems – building an effective environment for your enterprise’s data • Ensuring that your infrastructure is ready for the future – preparing for AI workloads and more • Using cognitive solutions to accelerate your architecture – automating roles, policies, and consent
• Beyond Volume, Variety, Velocity, and Veracity – Reaching the ‘Fifth V’; Value • Data as a source of innovation – utilizing your information to out-compete your rivals • How can we move the data function from a perceived cost center to a respected profit center?
• What steps can we take to best enable disruptive innovation through the use of Machine Learning? • How do you collate and prepare datasets to maximize success of machine learning algorithms? • Will the rise of machine learning lead to a decreased demand for Data Scientists as they are replaced by self-learning algorithms?
• Delivering insights to wow the customer and improve retention & profitability • Using customer data and cognitive systems to deliver true personalization • Avoiding the ‘creepy factor’ – how far are the public ready for us to go?
Discussion Group Session 2
• Establishing a strong governance framework to ensure high quality, consistent, and actionable data • Embedding a culture of responsibility amongst all data users so that governance remains a priority in coming years • Future proofing – what steps can we take now, to maximize the likelihood that today’s procedures will be ready for tomorrows… Read more.
• Immediate impact – what are the common ‘low hanging fruit’ and where should we turn once these have been utilized? • Proving strong return on investment to earn trust and demonstrate value. • Avoiding becoming a ‘flash in the pan’ – how can we ensure a long term, continued willingness to invest in data?
• Exploring the benefits and limitations of utilizing Cloud infrastructure • Private, Public or Hybrid – which route suits which circumstance? • Have security concerns been sufficiently addressed within Cloud technologies for them to be developed in an enterprise setting?
• Building trust – How can we become vital partners, rather than rivals, for the Marketing function? • Measuring ROI – providing data and developing metrics • Using predictive analytics to estimate churn and improve planning
Discussion Group Session 3
• Eliminating inefficiencies through data – where can the big cost reductions often be found? • How can the application of analytics increase revenues across the whole business? • How can analytics improve assessments of financial performance, to optimize budgeting and forecasting?
• How should potential projects be identified and prioritized? • Earning the trust of potential internal customers in lines of business • Communicating how the data & analytics function can aid value, without over-promising and under-delivering
• What opportunities does Blockchain present, and how can we ensure we are best placed to exploit them? • How might we need to adjust our data governance and management practices to order to accommodate Blockchain usage? • Overcoming volatility and developing standards – When should we expect to see Blockchain in production use cases?
• Building a strong relationship with Product Development teams, based on mutual trust • Discovering gaps in the market through analytics and sparking creative solutions to them • Using data to refine and optimize existing products and services
Discussion Group Session 4
• Do we need dedicated ‘data translator’ roles, or should we expect all team members to perform this duty? • Unlocking the secrets of communicating with non-data colleagues • What role can visualization play in communication our findings, what how can we ensure it is understood by all?
• How far can enterprises adopt a startup approach? • Which advantages are innate to startups, and which can larger enterprises replicate? • What methods can be used to increase collaboration between siloed departments within large organizations?
• How can your data program support wider transformation within your organization? • Should digital transformation be a cross functional responsibility, and what is the CDAOs place in this? • Positioning the CDO as a vital partner for success in digital
• Be the disruptor, not the disrupted! Utilizing data & analytics to devise novel business models • Testing and validating original revenue generating ideas through predictive modeling • What are the best examples of innovative business models created through analytics, and what can we learn from them?
The Innovation Huddle provides you the chance to take a deep dive into one of 5 different use cases which explore the potential applications of Artificial Intelligence within your enterprise, through a dedicated 30 minute group discussion moderated by a subject matter expert in the area. You will share real practical experiences within your group…. Read more.
Keynote Panel: Data Science Versus Data Engineering – Striking the Right Skills Balance within your Team
• What is the difference between Data Engineers and Data Scientists, and what should they each be doing within your function? • Stop hunting for Unicorns that don’t exist! How to successfully split the workload between Engineers & Scientists and reap the rewards of utilizing complementary skills sets. • Don’t feed your models garbage –… Read more.Speaking