Programme @


Big Data Analytics

16 June 2022


Programme @ BDA

Session One

moving forward in a data-driven way with your users, customers and business working together

  • Data in motion: moving your data at the speed of business
  • From the edge to the cloud and everywhere in between
  • What’s out there? Marrying your disparate data sources
  • From vendor to in-house – How to reimagine your analytics landscape
  • Graph data science: what can it do for you?
  • Getting to know your customers


Conference Chair's Opening Address


Data in motion: moving your data at the speed of business

Your business is in constant motion, and so too should your data be.

With the right platform, you can easily connect your apps, systems, and your entire organisation with real-time data flow and processing.

We address, the value of real-time data no matter the industry, how data in motion beats data at rest, and the business-wide value it delivers.


From the edge to the cloud and everywhere in between

Organisations are forever searching for new and innovative ways to share and collaborate on data in order to maximise the value it has to the business.

Such a preoccupation has only served to become an obsession thanks to the enforced workforce distribution of the pandemic.

One such transformative way in which to do so is with a cutting-edge data cloud centre.

We address:

  • Why data cloud over other platforms?
  • Its architecture, structure and performance value
  • How it can match workload size and scalability challenges


What’s out there? Marrying your disparate data sources

Harnessing the power of external data is vital if you are to take full advantage of data generated outside of your traditional parameters.

A well-managed plan for using external data can provide a competitive edge, create greater linkage between data sources, and ensure you limit the possibility of missing out on data-led opportunities.

Join us as we map out how best to harness your external data ecosystem and successfully integrate a broad spectrum of external data into your operations.


From vendor to in-house - How to reimagine your analytics landscape

Transitioning from a vendor-based data warehouse to a homegrown open-source-based platform has become a noticeable trend amongst end-user organisations.

No small undertaking, its scale, and complexity is one which many fail to navigate successfully.

However, with a well-thought-out strategy, breakthrough technological innovations, and a well-drilled team, it can be achieved.

We highlight one such organisation which took the path of establishing its own, bespoke data analytics platform, and the key lesson learned along the way.


Graph data science: what can it do for you?

Today’s hyper-data-driven business environment has left many in search of a smarter, cleaner, and quicker way to make sense of their data in order to solve complex challenges and turn them into opportunities.

With an enterprise-grade graph framework, data scientists are able to adopt a more flexible and intelligent approach to improve predictability, drive better decision making and generate innovative practices.

By integrating the power of relationships with network structures in existing data you can discover the solution to previously unsolvable questions and at the same time increase prediction accuracy.

We address:

  • Scalable graph algorithms and analytics workspace
  • Native graph creation and persistence
  • Visual graph exploration and prototyping


Questions to the Panel of Speakers


Refreshment Break Served in the Exhibition Area


Panel Discussion and Audience Q&A

Getting to know your customers

From a nakedly revenue generating perspective, customer personalisation matters above all else.

Product recommendations, messaging, timing, discounts, promotions, offers and home pages, all play a role in not just retaining your customer base, but also in upselling, cross selling and repeat business.

With an AI-driven personalised product platform you can make your business more relevant, noticeable, and successful.

Join us as we highlight how you can reorganise your business around AI and machine learning.


Questions to the Panel of Speakers & Delegates move to the Seminar Rooms


Seminar Sessions


Networking Lunch Served in the Exhibition Area

Session Two

Solving your biggest issues, identifying best practice and realising your greatest ambitions

  • Why the democratisation of data accessibility matters?
  • Modernise your IA
  • Building assurance into your ML production pipeline
  • Predict and optimise your business outcomes
  • Fighting misinformation with big data and AI
  • Data governance as a business enabler
  • Mitigate risk and accelerate insights


Conference Chair’s Afternoon Address


Why the democratisation of data accessibility matters?

Many organisations are still yet to expand the accessibility of ML, and by extension AI, beyond their data science teams.

This is due in large part to most enterprises have not put in place the right machine learning platform for their business, which in turn prevents this truly transformational tool from being democratised and adopted across the business.

We address, why it is never too late to bake into your business the competencies required to maximise the value of ML and AI, why ML is the primary driver of enterprise innovation, and the strategy you need to secure your ML breakthroughs.


Modernise your IA

All successful AI stories begin with the effective sourcing, storing, interrogation and utilisation of data.

Quite simply, AI cannot exist without a reliable, robust, and well-structured information architecture.

Still, being able to gain business value and insights from data is not always easy. From legacy infrastructure to data silos, failure to secure a holistic view of all your sources severely limits the value of AI.

We address:

  • What makes the best data foundation for AI?
  • How to integrate and infuse across the business
  • Ensure such a foundation is open and flexible
  • Establish streamlined access to data no matter the type or where it resides


Building assurance into your ML production pipeline

Chester Enslin
Head of IT Enterprise Integration and Software Quality Assurance, JSE
view profile
Kevin McKerr
Sales Leader, BITanium Consulting
view profile

By automating the monitoring of all your ML models to gain insights and scale with confidence you not only empower AI stakeholders with visibility and control into the health of your models in production, but you also ensure accuracy and efficiency in your data science models.

Join us as we address how to go beyond mere observability, know when your models behave badly, and get the desired insights to produce better models.


Questions to the Panel of Speakers


Afternoon Networking and Refreshments served in the Exhibition Area


Predict and optimise your business outcomes

Data is essential, but true optimisation comes from improving performance and competitive advantage from the production of analytics models which allow for democratic access to the insights gained.

Many believe that the best approach to producing an effective model starts with the identification of a business opportunity that allows you to determine how the model can improve performance, rather than starting with the data.

We address how such an approach to modelling can generate faster outcomes and embed models in practical data relationships that are more accessible and how this approach avoids the potential of designing overly complex models which exhaust organisational capabilities.


Afternoon Networking and Refreshments served in the Exhibition Area


Fighting misinformation with big data and AI

In the age of information and data overload, misinformation has become an increasing tendency, almost the “new normal”, with far-reaching consequences including impacting the results of the 2016 United States election and, more recently, the spreading of false information around the pandemic.

The consequences of inaction are serious. From the fragmentation of society into competing, and often violent factions, to the irretrievable decline in societal trust in democratic systems of governance.

Join us as we discuss the role that big data analytics and AI can play in the fight back against this assault on truth and trust.


Data governance as a business enabler

Data governance is not some monolith to circumvent for fear of it acting as an innovation disabler but should instead be seen as the engine to scale the use and distribution of trusted data pipelines throughout your company.

Just as in society, so too in business, good governance leads to the proper functioning of the people, processes and technologies designed to ensure the delivery of desired outcomes.

We address, how to develop and deliver trusted data, direct it to the right users in the right format and right time to harness business value, and confidently ensure data privacy, proactive compliance with regulations, and easy collaboration with data professionals in every function.


Mitigate risk and accelerate insights

Analysing enterprise data to identify unexplored growth areas and operational pain points enables the C-suite to make business-critical decisions. We can enhance and centralise your global reporting through intuitive and interactive visualisation dashboards.

We not only automate existing reports to reduce reliance on IT but empower executives to explore insights and test hypotheses on their own.

We can assign probabilities to scenarios and create risk scores so that business stakeholders and compliance officials can see the potential outcomes of their decisions before acting.

We address:

  • Better understand how you mitigate the data-driven decision-making risk
  • Detect conflicts of interest in third party relationships
  • Control risk and drive regulatory compliance


Questions to the Panel of Speakers


Closing Remarks from the Conference Chair


Conference Closes

Please note:
Whitehall Media reserve the right to change the programme without prior notice.

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