INTRODUCING OUR 2021

SPEAKERS

Introducing our first round of incredible speakers, with more to come! This carefully-selected group of innovative global and South African BI specialists will zone in on the technology and ideas that are transforming the BI space.

John Kamara Founder Ada Lab Limited (KEN)
Dr Alex Antic Strategic Data Science Expert Independent (AUS)
Jordan Goldmeier CEO Anarchy Data (USA)
Dino Bernicchi Head of Data Science HomeChoice International PLC
Jason Foster Founder & Chief Executive Cynozure Group (UK)
Kate Carruthers Chief Data & Insights Officer UNSW Sydney (AUS)
Chris Turner CEO Sports & Wellbeing Analytics (UK)
Sray Agarwal Associate Director: Data Science and Analytics Publicis Sapient (UK)
Shashin Mishra Director: Data Science and Analytics Publicis Sapient (UK)
Emmanuel Lubowa CX-Head Business Analytics Airtel Uganda (UG)
John Kamara Founder Ada Lab Limited (KEN)
Dr Alex Antic Strategic Data Science Expert Independent (AUS)
Jordan Goldmeier CEO Anarchy Data (USA)
Dino Bernicchi Head of Data Science HomeChoice International PLC
Jason Foster Founder & Chief Executive Cynozure Group (UK)
Kate Carruthers Chief Data & Insights Officer UNSW Sydney (AUS)
Chris Turner CEO Sports & Wellbeing Analytics (UK)
Sray Agarwal Associate Director: Data Science and Analytics Publicis Sapient (UK)
Shashin Mishra Director: Data Science and Analytics Publicis Sapient (UK)
Emmanuel Lubowa CX-Head Business Analytics Airtel Uganda (UG)

Entrepreneurship in Tech; AI, Blockchain, Machine learning and IoT

Learn how businesses can leverage key technology trends, transform organisations and drive competitive advantage for impact in industries including finance, agriculture, health, education, gaming, and startup enterprises. Understand how technology plays a critical role in catapulting Africa into the Fourth Industrial Revolution while creating critical mass impact.

Key takeaways:

  1. How new technologies can be leveraged in solving some of the most prominent and pertinent socio-economic challenges in Africa.
  2. How various players play different roles through the empowerment of existing structures and systems to build holistic sustainable ecosystems in problem-solving.
  3. Share knowledge, experiences, and network with up and coming entrepreneurs in tech and beyond fueled by my passion to see more successful tech-driven companies launch out of Africa.

How to Build a Successful, Scalable and Sustainable Data Science Capability

In this keynote, Human-Centred Data Science Expert, Dr Alex Antic, will share insight into:

1. How to establish a successful and sustainable Data Science practice, with the right culture
2. Effective ways to ensure success with Data Science projects
3. Tips, insights, and how to avoid common pitfalls

Embracing Data Disasters

We live in a time where data grows faster than our ability to even articulate the challenges and opportunities it creates. We can choose how to respond—we can turn our backs on past failures, or we can what historical data disasters say about today’s data workers. In this engaging keynote speech, celebrated Data Scientist Jordan Goldmeier provides a clear insight into how companies can gain continuous value from their data teams and the actions steps required to begin generating value immediately.

Key takeaways:
1. Reduce bottlenecks on data teams
2. Enabling data workers to generate value immediately
3. Creating data teams that endure through the Pandemic and beyond

Infuse and Scale AI in Your Organisation: Developing and Leveraging AI Platforms

So, your organisation has taken a few AI models live and is reaping the benefits. But now what happens? How do you quickly develop and push your next 10… 20… 100 models into production and manage retraining, degradation, and data drift? AI Platforms are helping organisations with these problems. Join this talk to uncover the details.

Key takeaways:

  1. How are AI Platforms changing the AI lifecycle?
  2. Unpacking the key features of an AI Platform
  3. Examples; build or buy?

Connecting the Dots Between Data & Your Business Strategy

Too often organisations make investments in building capabilities without having the right alignment to what they are trying to achieve. This can often lead to missteps, wasted investment, increased silo’s and missed opportunity. Creating an organisation that is guided by data is about connecting what you do back to your business strategy. It’s about having clarity on what you are trying to solve for and the size of the prize associated with getting this right. Taking lessons from across Public, Private, and the 3rd sector, this opening keynote will help set the tone for how you cut through the noise and focus on the things that really matter to your organisation.

Key takeaways:

  1. Get clarity on what to prioritise
  2. The journey to take that is right for your organisation and stakeholders
  3. What good looks like and outcomes you are trying to achieve

How to Build a High Performing BI & Analytics Team That is Future Ready

This talk will cover the key resource that is needed to build the future of BI and analytics – your team. It will discuss how to build a culture of high performance, how you can create a team that is ready and willing to learn new skills and technologies. It will include a case study of UNSW’s migration away from its legacy BI & data warehouse platform to a modern data platform, and how the team has evolved.

Performance through Player Welfare – How Data Analytics is Transforming Contact Sport

This presentation will cover the journey of how Sports & Wellbeing Analytics use sensor-based real-time information to look at the particular complexities of contact sport and turn that into actionable insights which enable athletes, coaches, and medics to make proactive decisions on their performance and welfare. It will include a discussion on a new lexicon for contact that is transferrable across sport and how the insights from this make it possible to compare what is happening on a rugby pitch to a boxing ring. Additionally, it will cover how machine learning and AI can interpret this data to tell what is happening to an athlete without the need for video.

Key takeaways:
1. How internet of things based data can be harnessed to transform a sport
2. The importance of real-time data
3. How the use of machine learning can challenge the art of the previously thought impossible

How to Define a Machine Learning Problem

Most of the software product teams planning to use machine learning to make their products smarter, make a common mistake – poorly defined problem statement – leading to a machine learning model that does not bring the results in real life that they had hoped it would. Even though most of the times business understands the goals they want to achieve by utilising machine learning (or AI as some like to call it), but the task of converting that goal into a set of requirements that can be understood by the data scientists often takes a back seat.

The interpretation of goals into technical requirements is left to the data scientists, without going through a rigorous process to convert business goals into requirements that are measurable and convertible to technical requirements for the model(s). The desire, or impatience, to get the benefits from the data as soon as possible leads to an outcome that does not meet the expectations.

In this session, we will talk about how the different roles within the team should work together to define the problem statement that can then be converted by the data scientists into custom objective or loss functions. These custom functions are then optimised in the train/test/validation stages to create a model that ultimately achieves the business goals much better.

Key takeaways:

  1. What are the most important steps in a data science solution’s lifecycle?
  2. Who is responsible for defining a machine learning problem?
  3. How can different roles come together to define a machine learning problem?

How to Define a Machine Learning Problem

Most of the software product teams planning to use machine learning to make their products smarter, make a common mistake – poorly defined problem statement – leading to a machine learning model that does not bring the results in real life that they had hoped it would. Even though most of the times business understands the goals they want to achieve by utilising machine learning (or AI as some like to call it), but the task of converting that goal into a set of requirements that can be understood by the data scientists often takes a back seat.

The interpretation of goals into technical requirements is left to the data scientists, without going through a rigorous process to convert business goals into requirements that are measurable and convertible to technical requirements for the model(s). The desire, or impatience, to get the benefits from the data as soon as possible leads to an outcome that does not meet the expectations.

In this session, we will talk about how the different roles within the team should work together to define the problem statement that can then be converted by the data scientists into custom objective or loss functions. These custom functions are then optimised in the train/test/validation stages to create a model that ultimately achieves the business goals much better.

Key takeaways:

  1. What are the most important steps in a data science solution’s lifecycle?
  2. Who is responsible for defining a machine learning problem?
  3. How can different roles come together to define a machine learning problem?

Business Intelligence Tools & Strategies

Businesses are advancing from a static, passive report of things to proactive analytics with dashboards that help them see what is happening every second and give regular alerts. Features such as AI algorithms based on advanced neural networks provide a high level of accuracy in detecting anomalies as it learns from historical patterns and trends. This will help immediately register and notify the user of any unexpected events. Another feature that AI has to offer in BI solutions is the upscaled insights capability that automatically analyses the dataset without needing human intervention.

There is an increasing demand for real-time online data analysis tools. The arrival of IoT is bringing an invaluable amount of data that will promote statistical analysis and management at the top of the priorities list. Tech giants use AI in various ways that will enhance the machine learning process, and businesses worldwide should keep an eye on this process in 2021.

In 2020, organisations witnessed the crucial value of real-time data and accurate updates for business analytics, enabling the necessary development of strategies to respond to a situation as it arises. Up-to-date data has become more critical than ever before, and since the world has changed, businesses need to adapt as well. in 2021 and beyond, organisations and governments will use real-time data with live dashboards for quicker reactions

Key takeaways:
1. Businesses are advancing from a static, passive report of things to proactive analytics.
2. Increasing demand for real-time online data analysis tools.
3. AI in various ways that will enhance the machine learning process, and businesses worldwide.