Day 2 - 18 March 2020
Big Data Business Solutions: Chair’s Welcome and Opening Remarks
Keynote: Delivering business value through Data Analytics
- Accessing the power of unstructured data through machine learning
- Creating business value and actionable insights from unstructured data
- How images, video, speech and text can all be better recognised and processed
- Key use cases and data sets that can benefit most from a ML approach
Head of Data & Analytics
absa Bank Mozambique
10:30AM - Day 1
10:20AM - Day 2
Panel: Creating a data-driven culture
- Establishing evidence-based decision making as a core part of the digital workplace
- Defining a clear strategy to leverage data whenever and wherever possible to enhance business efficiency and effectiveness
- Putting big data at the heart of your business
- Closing the data literacy gap, hiring talent and getting buy in from the top.
Senior Data Science Consultant
11:30AM - Day 2
Beating the odds: How to make your data project or team part of the 15% success story.
Making data science a success is really hard with up to 85% of projects and initiatives around big data and data science failing, according to Gartner. The reasons are complex but often misunderstood. As project management begins to grapple with the opportunities presented by data science those responsible for implementation need to proceed with care.
What is so different about data that it needs new approaches? This talk will focus on the requirements for data science success and looks at a future after the hype:
- Motivation: Vanity project or aligned business strategy with senior leadership buy-in?
- Requirements and preparations: Solid foundations or duct taped data silos and constant fire fighting bad data?
- Hiring: Unicorns with the right skill sets to be a commercial data scientist or expensive mis-hires?
- Delivery: Models in production serving business needs or undocumented proof of concepts on laptops?
- Retention: Roadmap of game changing projects or abandoned team and expensive write-offs?
Implementing a successful data monetization strategy
- Leveraging data to improve your company’s operations, productivity and products
- Identifying new revenue opportunities
- Making data available to customers / partners
Afternoon Keynote: Making Big Data the centre of your business model
- What problems is Big Data helping to solve?
- What data will be used? How will it be generated and analysed?
- How to encourage Big Data Innovation and stay ahead of the pack.
Technical Evangelism Director
02:30PM - Day 2
Clear and Presentation Danger: how to share your data-driven insights
You know the feeling. You’re in a meeting and the presenter is sharing data-driven insights. But they make no sense! It’s not the data that’s the problem, it’s the way they are presented. This is the Last Mile of Data Analytics. All your investment in people and technology means nothing if you do not effectively share the insight with others, in order to drive better decisions. Designing charts for meetings, presentations or even keynotes is a learnable skill. Andy Cotgreave, author of the Big Book of Dashboards, and Computing’s Analytics Professional of the Year, has sat through thousands of meetings, and seen many crimes committed against data. He’s even committed many himself. He’s also seen how effectively it can be done, with a little bit of effort. In this keynote, Andy will share his tips on presenting data effectively. You will leave with a bunch of easy-to-apply tricks to make your charts punch home their message in every meeting or presentation you lead.
Víctor Morón Tejero
Data Scientist Lead
03:00PM - Day 2
From data analytics to data science driven
This talk will cover the challenges we found to start implementing a data science team both from the technical and human point of view, by going through at least one example of a model implementation at nectar.
Main points will include:
- Technology needed (and problems/limitations with old/new technology)
- Skills involved further than Data Science
- Improvements regarding previous implementation
Panel: Improving Business performance with data science
- Understanding what data science can and can’t do
- Translating data science capability into business results
- Securing support from stakeholders
- Building Data Science capabilities from the ground up