Ravi Hulasi
Ravi Hulasi
Head of Strategic Solutions
SHARE
September 23, 2024

Top 5 Takeaways from Big Data London

Top 5 Takeaways from Big Data London

Summary:

  • AI was the hottest topic at Big Data London
  • Businesses are shifting their perception of AI from fear to excitement
  • Good data quality is crucial for AI success
  • Governance and ethics are top priorities for AI implementations
  • Collaboration between humans and AI is essential for ethical decision-making

Big Data London, touted as "the UK's leading data, analytics, and AI event," is one of the premier events for data professionals, providing a platform for thought leaders, innovators, and data practitioners to gain insights, connect with peers, and explore the capabilities of cutting-edge solutions.

This year, artificial intelligence (AI) was by far the hottest topic at the event. From advances in AI technologies to the best ways to navigate our new, AI-driven reality, it's clear that AI is on everyone's mind. Here are our top five takeaways from this year's event.

The data conversation is shifting

We've all heard the predictions: AI is going to takeover and replace the jobs that humans do today. And while it's true that the adoption is AI is rising, the conversation about how organizations are using AI is shifting. When first introduced, many data leaders panicked. They assumed that they must implement AI as a massive system, thus replacing the need for humans.

Thankfully, their perception is changing. Today, most businesses view AI as a strategic tool designed to assist humans by assuming responsibility for mundane tasks that otherwise distract from more strategic initiatives. Take dirty data as an example.

Duplicate data continues to be a challenge for organizations. Analysts spend many, many hours searching for and resolving duplicate entities in order to ensure the integrity of their analytics. However, when organizations employ AI, data users can now proactively identify duplicates in real time and prevent them from entering their systems in the first place. Not only does this preserve the integrity of the data, but it also enables analysts to refocus their time on more strategic work.

Good data is more important than ever before

It's a well-known fact that data quality is important. But when it comes to AI, having high-quality data is even more critical. Bad data not only erodes customer experiences, but it can also introduce bias and hallucinations, leading to inaccurate analysis, misguided decisions, and reputational harm.

Spotting and correcting inaccurate, incomplete, or duplicative data requires organizations to master their data and create golden records. Unlike traditional master data management (MDM) solutions, AI-native data mastering creates single, accurate sources of truth that integrate data from multiple, disparate data sources. These "golden records" give businesses a trusted foundation for business intelligence, data warehouses, and other applications so they can deliver personalized customer experiences, uncover hidden revenues, and protect the organization from unforeseen risks.

Governance and ethics are top of mind
As AI continues to take hold, another topic quickly emerges: AI governance. By establishing standards for fairness, accountability, and transparency, AI governance helps businesses to mitigate risks such as bias, discrimination, hallucinations, and misuse, especially as data changes over time. AI governance also plays an essential role in ensuring that AI technologies are developed and used responsibly, ethically, and transparently - and that they meet regulatory mandates such as the EU AI Act which defines rules and guidelines related to the responsible use and development of AI.

Another hot topic, ethical AI, goes hand-in-hand with AI governance. Ethical AI asks the question “are we doing the right things to ensure our AI is unbiased?” Building an ethical AI program requires AI governance. But it also requires organizations to take into account how they can build their program to not only promote accuracy and fairness but also to mitigate bias and ensure that private data remains safe. 

Humans still play a valuable role

While AI can (and will!) transform business, one thing remains clear: humans still have a valuable role to play. AI's strengths lie in processing speeds and advanced algorithms, while humans have the unique ability to apply empathy, creativity, and contextual relevance. 

Promoting collaboration between humans and AI is essential when it comes to taking accountability for ethical decision-making and responsible use of AI. Humans can help identify potential biases within the data, add relevant context, and introduce new considerations that not only strengthen the integrity of the AI, but also hold organizations accountable for using it ethically. Further, new advancements in GenerativeAI (GenAI) enable data users to interact with the data, ask questions about it, and quickly find answers in real time. That way, they can correct and enrich the data, improving its quality and completeness.

Optimism is replacing fear

Perhaps the most surprising of all the takeaways from Big Data London is the fact that the fear of AI is dissipating. Instead of being fearful of AI and the disruption it could cause, businesses are optimistic - and excited! - about its potential to enhance their organization. They're realizing that AI is not a massive system that is going to usurp what's in place today. Instead, it's a tool that, when implemented well, can support and enhance existing work, leading to greater efficiencies, better customer experiences, and newfound sources of growth.

Attendees of Big Data London left inspired by new perspectives and actionable strategies that will enable them to advance their organizations through the use of AI. And as the conversations and connections continue beyond the event, it’s clear that the shared knowledge and experiences will have a lasting impact.

To learn more about how Tamr can deliver the high-quality data needed to harness the power of AI, please request a demo.