Wednesday 6 March 2019, Science Museum - London

Discussion 2019 Roundtable Topics Previous Attendees Register



Roundtable Discussion Themes

As an attendee of the DataIQ Discussion delegates will have the opportunity to attend three different themed roundtables over the course of the day to learn, share and discuss that theme with their industry peers. Many data professionals face similar challenges but find different ways to overcome them - this is an opportunity to exchange those experiences in a conversational environment.

The roundtables operate under Chatham House Rules - nothing that is discussed at DataIQ Discussion will be attributed to any brand or individual - so delegates can feel comfortable and relaxed about their conversations.

Table 1 - Strategy: Making the business case for data and analytics

  • Core principles for winning investment

  • Risks to sustaining investment

  • Success factors and key metrics


Table 2 - Strategy: Aligning the data strategy with the business strategy

  • Creating sustainable data sources and flows

  • Mapping and maintaining the data estate as the business grows

  • Using data and analytics for business innovation


Table 3 - Data management: Building a single source of the truth

  • How to achieve a unified view of the business and the customer in data

  • What creates value for the business from integrated data

  • Deploying integrated customer data into customer-facing processes


Table 4 - Data management: Maintaining the relationship with IT

  • Why data management needs IT - but doesn’t belong under it

  • What the key focus for data management needs to be

  • Where data management technology diverges from the corporate stack


Table 5 - Analytics: Ensuring analytics creates business benefits

  • Aligning the analytics team with lines of business

  • Translating business challenges into analytical tasks

  • Ensuring analytics gets the credit it deserves when the business benefits


Table 6 - Analytics: Managing analytical pipeline demand and expectations

  • Setting realistic service levels and turnaround times

  • Arbitrating between competing demands

  • Automating and offloading repetitive tasks to focus on value drivers


Table 7 - Data science: Creating a true data science function

  • Understanding the scope, scale and resourcing required

  • Managing your unicorns

  • Recognising when to push projects - and when to stop them


Table 8 - Data science: Preparing for AI and ML

  • What the data foundations need to be

  • How to pilot and evaluate AI and ML projects

  • How to ensure AI and ML projects can be deployed


Table 9 - Data governance: Building privacy into your organisations' culture

  • Recognising the building blocks of trust

  • Deploying governance as a business enabler

  • Communicating with customers and being transparent in data use


Table 10 - Data governance: Compliance and ethics

  • Aligning data strategies with privacy and regulations

  • Creating a culture of data respect and privacy-first processes

  • Ensuring ethical overlays are considered against compliance 


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