Programme @ BDA europe

Do you understand the practicalities involved in delivering the ambition of perfecting the customer experience?

Is your digital transformation being powered by data in isolation or is it being driven by the convergence of data, technology, and talent?

Those who are able to answer these questions with a vast reserve of retained knowledge and understanding of the digital economy are those who are successfully turning raw information into a tailored competitive advantage.

We address, why it takes more than just technology to succeed, how it requires as much of a cultural as a structural shift, and the new roles and change agents you need to bring it about.

One key trend brought on by the pandemic has been the reprioritisation of enterprise AI initiatives away from the C-suite and towards the rest of the organisation, leading to greater operationalisation.

However, as investment in AI solutions increases substantially, failure-to-complete rates remain stubbornly high for many.

We address:

• How to better understand the pillars of a successful AI project
• Why you should start from the problem and not the solution
• The value in following an agile approach with end-user feedback

From receiving an alert from the bank on the suspicion of fraudulent activity on your account to high street retailers perfectly calibrating your online experience to provide deep personalisation, having a comprehensive understanding of the relationships involved in your data is a powerful asset.

We address, how with a purpose-built graph database, you can better manage your data sources, create greater linkage between data points, and scale regardless of store and query demands.

From raw data to interactive visualisations in minutes. That is the key to enable your teams, provide context and get the right data to the right in person.

Having the right data visualisation tool isn’t a nice to have feature of your big data analytics capabilities, it is a vital component of your technology suite.

We address:

• Create and use visualisations with no BI expertise
• Integrate filters and drill-downs as part of enhanced accessibility ambition
• Combine data, content, and insights
• Support data storytelling with a connected narrative
• Enable your teams to self-serve and work independently
• Govern access to secure data without hindering wider exploration

The ease at which data is sourced, stored, and analysed on a global scale is an obvious business benefit.

What is not so easy to navigate is the prevailing regulatory compliance standards in certain regions.

In an age in which the sanctity of an individual’s digital identifiers is becoming the primary concern for consumers, it is vital that businesses protect their finances and their reputation for robust data management.

We address:

• Common challenges and pitfalls
• Cooperation and coordination between globally dispersed teams
• Tools and techniques for managing business expansion
• Seamless compliance across jurisdictions

Getting to know your customers

From a purely 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

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In today’s hyper-competitive, product-intensive environment, the retailers who succeed in retaining and building their customer base are the ones who are most adept at creating engaging digital experiences.

We address, how you can better understand why customers use your products, access and act on customer data in real-time, breakdown silos and improve overall product and customer success, as well as lock-in customer lifetime value.

Given that by 2025 75% of enterprise data will be processed at the edge, compared to just 10% today, a shift towards an autonomous management platform that has an inbuilt capability geared towards increasing scalability, variability, and rate of change will be needed to harness the value of the edge environment and ease the burden on enterprise network and infrastructure capabilities.

We address, how you can modernise your network and deliver new services at the edge.

Better data leads to more performant models.

Performant models lead to faster deployment.

Deliver value from your AI investments faster with better data.

Join us as we map out our mission is to accelerate the development of artificial intelligence.

We do this by providing a data-centric, end-to-end solution to manage the entire ML lifecycle.

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

Bias, ethics, privacy, security – these are the common denominators which are of most relevance when talking about applications based on AI.

Coupled with the dark arts of machine learning in which its inner workings cannot be accurately pin pointed, the lack of societal trust in AI is a significant factor in hindering its progress for good as business use cases are hindered by a lack of adaptability in the AI ecosystem.

We address:

• Making the black box of AI transparent with Explainable AI
• Understanding when AI systems give enough confidence in the decision that you can trust it
• Favouring traceability and transparency over raw performance and power
• Greater reciprocity between machine and human

The impressive growth in cloud data warehouses has been brought about by the business need for greater degrees of automation in place of human-led, manual data ingestion and analytics.

Rather than burdening their engineering teams with complex, hand-coded processes that cause delays and limit the effectiveness of warehouses, many are choosing to adopt a fresh approach that automates and accelerates cloud data warehouse operations.

We address, how you can:

• Continually ingest all of your data in real time
• Automate the entire data warehouse lifecycle
• Make data marts readily accessible across your business with cataloguing
• Make your data warehouse more adaptable, flexible, and responsive to analytics demands

Blockchain is fast becoming an inexhaustible tool for data science teams. From decentralized ledgers being able to provide a brand-new way of managing and operating big data, to guaranteeing transparency for complex networks of participants.

In this talk, we explore Blockchain’s role in tackling two of data sciences biggest challenges; overcoming the current need to structure information in a centralized manner where all data sets are brought together for further analysis, and the lack of traceability in relations between data sets.