Business and technology experts believe AI will be the most significant technological revolution that businesses have ever experienced. Despite this excitement, some executives are still asking if this is all hype, others are asking if it is even relevant to their industry! The reality is, if you want your business to succeed in the coming decade (and who doesn’t?), you need to pay attention to AI.

Your very existence will depend on it. This presentation will help you:

• See through the hype;
• Understand how AI applies to your business; and
• Understand how you can move it from an aspiration to a business reality.

• Examining the reasons for implementing AI:
  • Enhance your customer experience;
  • Empower your employees;
  • Optimise your operations; and
  • Transform your products.

• Making the business case for AI – linking proposed solutions to specific business benefits.
• AI in the BI environment: How do you upskill your current BI/analytics team and train them to implement and use AI tools?
• Looking at current AI implementations, eg, chatbots. How do you ensure the performance of your bot and increase engagement?
• Moving beyond chatbots to cognitive computing – the trend towards natural language processing.

• What is augmented analytics and how can it benefit your business?
• Understanding how data is prepared, shared and operationalised using augmented analytics to enable faster access to insights and more accurate business predictions.
• What are the advantages of augmented analytics for your data and analytics team?

• How can you use the data that you have to create a true single view of your customer?
• To what extent does customer analytics contribute value to companies’ performance?
• What are the key success factors for reaping the benefits of customer analytics?
• What practical steps can you take to start applying customer analytics in your organisation?

• Examining the challenges of integrating data from multiple systems and channels to achieve a single view of customer in a traditional business
• Understanding how Naked’s digital business model allows it to store millions of data points from every micro interaction a client has on its app, thus achieving a complete view of that client
• Identifying the benefits of the single view of the customer with Naked’s model:
  • Using AI’s superior ability to classify risk and to predict behaviour, thereby reducing fraud and lowering premiums
  • Making customer interactions quicker and less intrusive (eg more than 98% of claims are fast-tracked, removing the frustration and time delays associated with old-school insurance)

• Examining how to migrate from a relational database to a Hadoop data warehouse: what have the results been? What other value can be derived from the Hadoop ecosystem?
• Looking at the benefits of incorporating Apache Kafka into the data warehouse environment
• What are the implications of moving forward to the cloud for data warehousing? Can the two systems operate in parallel?
• Understanding the potential of data streaming technology – could it eventually replace traditional databases?
• Analysing the importance of team culture and what is required from a people perspective for an organisation to make such a transition

• Examining the changing role of the data model and metadata, and the challenges this presents for today’s organisations.
• What are the elemental characteristics of data that provide a common denominator for modelling for all types of data?
• How is this common denominator used to map various kinds of databases, including relational, dimensional, NoSQL, NewSQL, graph and document?
• What are the critical success factors for a creating a business-centric data model?

• What does a modern data architecture look like? What are the critical success factors?
• Examining how to plan and document a modernised data architecture.
• Identifying techniques to map standards and technology to architecture layers.
• How do non-relational data platforms fit into new data architectures?
• What is the role of virtualisation and cloud storage in your architecture?
• How can data architectures be adjusted to support self-service BI and analytics?

• How do evolving information demands and modern data pipelines create data integration challenges?
• How can you pursue data integration as a strategic capability?
• Examining modern integration technologies such as data virtualisation, data preparation, data engineering and stream data integration
• How can you complement traditional integration practices such as ETL/ESB with modern integration technologies?

• Identifying the benefits of data science – how does it integrate with your existing data and analytics functions?
• How to set objectives before going ahead with your data science projects.
• How to start simply and why doing so is the key to success.
• Creating the right organisational and delivery framework in which data science can succeed.
• How do you measure the success of your data science projects?

• Encouraging cross-functional collaboration
• Tying your AI project to a measurable ROI
• Reskilling and redeploying workforce: identifying the skills gaps and potential loss of jobs

Digital transformation is not just about developing new products, processes or tools, it’s about redefining what efficiency means for your business. In an era of rapidly increasing competition brought about by the exponential adoption of cloud solutions and AI, companies need to innovate now more than ever in order to maintain that critical edge. But can these applications work in the context of your own business and be put to work on your unique challenges? Absolutely. In this presentation Hendri will explain how.