Programme @


Big Data Analytics

9 November 2022


Programme @ BDA

Session One

turning D&A trends into business assets

  • Building reliability in your data cloud storage
  • The growth of data fabric technology
  • Mastering ethical customer data collection
  • Data analytics + AI/ML = optimised performance
  • The evolution of vector similarity search

09:00 (GMT)

Conference Chair's Opening Address

09:05 (GMT)

Building reliability in your data cloud storage

Big data comes into organizations from many different directions, and with the growth of tech, such as streaming data, observational data, or data unrelated to transactions, and increased knowledge of how disparate data types can be used strategically, big data storage capacity is an issue.

In most businesses, traditional on-premises data storage no longer suffices for the terabytes and petabytes of data flowing into the organization. Cloud and hybrid cloud solutions are increasingly being chosen for their simplified storage infrastructure and scalability.

We address:

  • Why cloud solutions are now an enterprise data and analytics must-have
  • The flexibility and scalability that hybrid provides
  • How the cloud can help establish a new database or application, spin up a server, or build new clusters in a split second

09:20 (GMT)

The growth of data fabric technology

An important development that focuses on expanding the space available for digital transformation in an enterprise, data fabrics are progressively developing in the cloud and being adopted by organizations that need additional real estate and increased accessibility for their growing pools of big data.

With a data fabric architecture, they can easily store and retrieve needed data sets across distributed on-premises, cloud, and hybrid network infrastructure.

 We address, why organisations of all sizes are turning to smart data fabrics to provide the capabilities needed to discover, connect, integrate, transform, analyse, manage, utilise, and store data assets to enable the business to meet its goals.

09:35 (GMT)

Mastering ethical customer data collection

Much of the increase in big data over the years has come in the form of consumer data or data that is constantly connected to consumers while they use tech such as streaming devices, IoT devices, and social media.

Data regulations like GDPR require organizations to handle this personal data with care and compliance, but compliance becomes incredibly complicated when companies don’t know where their data is coming from or what sensitive data is stored in their systems.

Join us as we stress the value of first-party data sourcing in not only ensuring compliance with data laws and maintaining data quality but also delivering cost savings.

09:50 (GMT)

Data analytics + AI/ML = optimised performance

 The marriage of big data analytics and AI/ML automation, both for consumer-facing needs and internal operations, is a happy one.

Without the depth and breadth of big data, these automated tools would not have the training data necessary to replace human actions at an enterprise.

We address, how AI by itself is very powerful, but AI plus automation presents an opportunity to create smart systems that react automatically to the technology in a seamless way to reach a higher level of intelligence and complete end-to-end services.

10:05 (GMT)

The evolution of vector similarity search

Perhaps the least known and most interesting trend for the future of big data comes with vector similarity search, a new approach to finding and retrieving data through deep learning and other smart data practices.

We explore, why Machine Learning teams are starting to use vector search to drastically improve results for semantic text search, image/audio search, recommendation systems, feed ranking, abuse/fraud detection, deduplication, and other applications.

10:20 (GMT)

Questions to the Panel of Speakers

10:35 (GMT)

Refreshment Break Served in the Exhibition Area

11:05 (GMT)

Panel discussion and Q&A

11:35 (GMT)

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

11:50 (GMT)

Seminar Sessions

12:30 (GMT)

Networking Lunch Served in the Exhibition Area

Session Two

capitalizing on new and emerging tools, technologies and processes

  • How to enable self-service analytics to ensure D&A success
  • Edge computing environments: Operating at the parameters
  • Data and analytics governance as a growing challenge – and what you can do about it
  • How to embed a culture of data-driven differentiation?
  • Managing and connecting your data: become an analytics master
  • Embracing a combined approach to business efficiency enhancement

13:30 (GMT)

Conference Chair’s Afternoon Address

13:35 (GMT)

How to enable self-service analytics to ensure D&A success

Mastering self-service analytics at scale continues to evade many D&A leaders, which in turn leads to limited business value.

Data and analytics leaders must deliver self-service value, nurture collaborative development between IT and business, and practice lightweight management and supervision.

We address how you can –

  • Get the insights you need without having to rely on analysts or technical teams to access data and reports
  • Enable non-technical staff to get access to data to generate reports so they can make data-driven business decisions
  • Embrace self-service analytics to democratize analytics capabilities among all end-users

13:50 (GMT)

Edge computing environments: Operating at the parameters

Data, analytics and the technologies supporting them increasingly reside in edge computing environments, closer to assets in the physical world and outside IT’s purview.

However, even those companies that understand the potential benefits of processing data generated at the edge can struggle to generate value from it.

Given the multitude of different devices that can be considered ‘the edge’ – as well their respective performance constraints – identifying where, and why, to perform data processing is not a simple task.

We address, the reasons for adopting edge computing, the value it brings and how to best manage and overcome common pain points.

14:05 (GMT)

Data and analytics governance as a growing challenge – and what you can do about it

Data and analytics governance has become more challenging. Progress is hindered by new requirements, new technology capabilities and a lack of maturity in the discipline.

As a result, data and analytics leaders need to reimagine how best to implement data and analytics governance for business value.

We address:

  • How data governance will save you time, money, and resources
  • How to choose the best governance model for your needs
  • 3 steps to delivering data you can trust at any scale
  • Tips for building a data team and a data-driven culture

14:20 (GMT)

Questions to the Panel of Speakers

14:35 (GMT)

Afternoon Networking and Refreshments served in the Exhibition Area

15:05 (GMT)

Panel discussion and Q&A

15:35 (GMT)

Afternoon Networking and Refreshments served in the Exhibition Area

16:05 (GMT)

How to embed a culture of data-driven differentiation?

True differentiation from your competition cannot simply be store-bought; it has to be earned through a coming together of the people, processes and technologies that make your organisation successful.

By using the only truly original asset you as a business have, your data, you can provide yourself with the opportunity to act first on many vital data-inspired points of business intelligence.

We address:

  • Create and sustain competitive advantage
  • Development agility as a valued factor
  • Respond and capitalise on market trends
  • Empower your developers
  • Prevent legacy infrastructure from slowing you down

16:20 (GMT)

Managing and connecting your data: become an analytics master

Data only has as much value as can be derived from it by connecting and managing it cohesively.

It is only when you enable digital feedback within your organisation can you use the data and the intelligence it creates to provide the foundation for a successful digital transformation.

By harnessing the constant streams of data being created, tying this to your existing data sets and then applying AI to it, you enable better decisions and transformative processes.

We address, how to empower your workforce, optimise operations, engage customers and transform your product offerings.

16:35 (GMT)

Embracing a combined approach to business efficiency enhancement

As predictive decision-making continues to gain widespread acceptance as the standard, companies are implementing AI solutions which have caused further shifts in the industry.

Although AI, data analytics and cloud computing are leveraged in separate ways, their work chains are interestingly linked.

If the information generated is big data, then the cloud is the media to extract that information.

Along this chain, AI has the potential to add innovation while making the data received meaningful.

We address how, with the effective use of such data, an optimised enterprise knowledge repository can be developed with AI, which will enable more accurate predictions & insights related to consumer activity.

16:50 (GMT)

Questions to the Panel of Speakers

17:00 (GMT)

Closing Remarks from the Conference Chair

17:05 (GMT)

Conference Closes

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

Follow us on social

Keep up to date with what's going on by following us on social media.

Featured blogs

Read the latest news and views from key industry figures and thought leaders.

Big Data Analytics Tops the Most Wanted Role
The significant increase in demand in tech-enabled sectors such as IT and BFSI has seen 96% of companies plan on hiring new staff with skills in big data analytics for related roles in 2022. This looks to top the charts as the most in-demand role for the year ahead, with rapid tech adoption being implemented...
Data Analytics Surges Ahead of Ability to Collect its Information
Over the last two years, 90 per cent of international government agencies have improved their use in regards to data analytics, with a reported widening in the gap of how much data is collected and how much is used for meaningful analytics. Maturity Levels In a report conducted recently, 89 per cent of respondents were...
Machine Learning: The Why, When and How
by Kumar Dipanshu Why machine learning? At its simplest, machine learning (ML) uses mathematical models to analyze large volumes of data, identify patterns and make decisions. ML models can imitate human behavior to predict outcomes, such as those used for language translation, chatbot automation and predictive text. They’re also the power behind an Amazon product...