Big Data LDN 2024, the UK’s leading data, analytics, and AI event, is less than a week away – promising two days filled with ground-breaking stories, expert insights, and endless innovation.

Taking place at the Kensington Olympia in London on September 18-19, this year’s event features fifteen theatres and over 300 expert speakers sharing insights on some of the industry’s hottest topics – from generative AI to data analytics and privacy. 

With the event less than a week away, EM360Tech’s Head of Podcast Production, Paulina Rios Maya, grabbed Big Data LDN’s Event Director, Andy Steed, for a chat about his expectations for this year’s event and its growing importance in the data world.

In the episode, they discuss: 

• The exciting themes or breakthroughs attendees can expect to see showcased this year
• How Big Data London remains relevant in such a rapidly evolving field
• The unique networking opportunities or interactive experiences attendees have at the conference
• The standout sessions or keynote speakers at the conference

00:00: Introduction to Big Data LDN 2024

01:35: Showcasing Data Stories, Transformations, and Challenges

02:33: The Networking Opportunities with Industry Leaders and Peers at Big Data LDN 2024

05:01: Staying Relevant with a Focus on Generative AI and Real-World Use Cases

06:55:The Importance of Data Events for Community Building and Learning

Big Data London is the UK's largest data and analytics event, attracting over 16,500 visitors each year. Taking place at the Olympia in London on September 18-19, this year’s event features fifteen theatres and over 300 expert speakers across the two-day conference. 

Attendees can meet face-to-face with tech providers and consultants to find solutions to your data challenges and view the latest product releases and software demos to enhance your business' data capabilities.

It’s also a great opportunity for attendees to strengthen their business network with new and existing partners, and immerse themselves within the data community and network with speakers, colleagues and practicioners all in 2 days at Big Data LDN.

Sign up to Big Data LDN 2024 today and be part of the UK’s leading hub for the Data Community to learn and share best practices, build relationships and find the tools needed to develop an effective data-driven business.

We can’t wait to see you there!

Sustainable sourcing is essential for businesses committed to environmental and social responsibility, but achieving it requires accurate and reliable data. Master Data Management (MDM) ensures that all sourcing data—such as supplier information, certifications, and compliance records—is consistent and up-to-date. This enables organisations to make informed decisions that align with their sustainability goals, reduce waste, and promote ethical practices throughout their supply chain.

MDM is the foundation of a successful sustainability strategy. By providing a single source of truth for all critical data, MDM helps businesses monitor and track their sustainability efforts effectively. With accurate data, companies can identify opportunities to improve resource efficiency, reduce carbon footprints, and ensure compliance with environmental standards, ultimately leading to a more sustainable and resilient business model.

In this episode, George Firican, Founder of LightsOnData, speaks to Matthew Cawsey, Director of Product Marketing and Solution Strategy, and Paarijat Bose, Customer Success Manager at Stibo Systems, to discuss sustainable sourcing and why accurate data matters. 

Key Takeaways:

• Sustainable sourcing involves understanding the provenance and environmental impact of products, ensuring compliance with regulations, and meeting sustainability goals.
• Data completeness and accuracy are crucial in meeting regulatory requirements and avoiding issues like greenwashing.
• Managing sustainability data requires a solid foundation of MDM to ensure data accuracy, stewardship, and semantic consistency.
• MDM solutions help companies collect, manage, and share sustainability data, enabling them to meet compliance requirements and achieve their sustainability goals.

Chapters:

00:00 - Introduction and Overview

01:07 - The Challenge of Collecting Data for Compliance and Reporting

02:31 - Data Accuracy and Completeness in the Supply Chain

05:23 - Regulations and the Demand for Transparent and Complete Data

08:41 - The Role of Master Data Management in Sustainability

15:51 - How Data Management Technology Solutions Help Achieve Sustainability Goals

21:02 - The Need to Start Early and Engage with Data Management Solutions

22:01 - Conclusion and Call to Action

Data provenance is essential for maintaining trust and integrity in data management. It involves tracking the origin of data and understanding how it has been processed and handled over time. By focusing on fundamental principles such as identity, timestamps, and the content of the data, organisations can ensure that their data remains accurate, consistent, and reliable.

Implementing data provenance does not require significant changes or large investments. Existing technologies and techniques can be seamlessly integrated to provide greater transparency and control over data. With data provenance, businesses can confidently manage their data, enhancing decision-making and fostering stakeholder trust.

In this episode, Jon Geater, Co-Chair of the Supply Chain Integrity Transparency and Trust (SCITT) Working Group, speaks to Paulina Rios Maya, Head of Industry Relations, about data provenance. 

Key Takeaways: 

• Data provenance is knowing where data comes from and how it has been handled, ensuring trust and integrity.
• The fundamental principles of data provenance include identity, timestamps, and the content of the data.
• Data provenance can be implemented by integrating existing technologies and techniques without significant changes or investments.
• Data provenance helps with compliance, such as GDPR, by providing a transparent record of data handling and demonstrating compliance with requests.

Chapters: 

00:00 - Introduction and Background

02:01 - Understanding Data Provenance

05:47 - Implementing Data Provenance

10:01 - Data Provenance and Compliance

13:50 - Success Stories and Industry Applications

18:10 - Conclusion and Call to Action

FME is a vital tool in disaster management and response. It enables the integration and transformation of geospatial data for real-time tracking of disasters and hazards. By ensuring accurate and timely data analysis, it provides essential decision support for disaster management professionals.

During the Maui wildfires, FME and the Pacific Disaster Centre were crucial in managing and analysing critical data, allowing for effective coordination and response. By facilitating seamless data sharing and collaboration among stakeholders, FME helps ensure that the correct information reaches the right people at the right time.

In this episode of the EM360 Podcast, Alejandro Leal, an Analyst at KuppingerCole, speaks to Jorma Rodieck, a GIS Specialist at the Pacific Disaster Centre, about the importance of FME. 

Key Takeaways:

• FME is an essential tool in disaster management and response, allowing for the integration and transformation of geospatial data.
• FME enables real-time data analysis and decision support for disaster management professionals.
• During the Maui wildfires, FME was instrumental in managing and analyzing critical data, providing a common operating picture for response efforts.
• FME ensures effective data sharing and collaboration among various stakeholders, enabling smooth interoperability between departments and agencies.

Chapters:

00:00 - Introduction and Background

02:35 - The Role of FME in Disaster Management

06:44 - Managing and Analyzing Critical Data with FME

10:34 - FME's Impact during the Maui Wildfires

11:59 - Ensuring Effective Data Sharing and Collaboration

15:20 - The Future of FME in the Pacific Disaster Center

18:15 - Conclusion

Open source real-time analytics offers unparalleled advantages, providing businesses with freedom and independence to maintain operations seamlessly, even if a vendor issue arises. However, the journey isn't without its challenges. Open source solutions can often be clunky and require specialised expertise to manage effectively. 

This is where DoubleCloud comes in, offering a managed platform that addresses these obstacles by handling crucial responsibilities such as backups, high availability, and security updates, allowing businesses to focus on leveraging their data.

In this podcast, Christina Stathopoulos speaks to Vladimir Borodin, Co-Founder and CEO of DoubleCloud, about open source strategies and the advantages of the DoubleCloud solution.  

Key Takeaways:

• DoubleCloud's managed platform helps overcome the challenges of open source, such as clunkiness and a lack of expertise.
• Successful customer use cases demonstrate the performance and cost benefits of DoubleCloud's solution.
• The transition phase to DoubleCloud's solution depends on the complexity of the application.
• Using open source whenever possible is recommended.

00:00 - Introduction and Background

02:29 - The Advantages of Open Source

04:21 - Challenges of Open Source

06:47 - The Power of Real-Time Analytics

09:11 - Success Stories: Improved Performance and Reduced Costs

12:54 - Navigating the Transition to DoubleCloud's Solution

15:14 - The Importance of Using Open Source

Privacy by Default and Design is a fundamental principle of the General Data Protection Regulation (GDPR). It prioritises transparency, user control, and data security from the outset. This approach ensures that privacy is integrated into systems and processes by default rather than as an afterthought. 

By embedding these practices, organisations enhance trust and accountability while meeting regulatory requirements. However, challenges such as resistance to change and the need for cultural transformation must be addressed to implement this principle effectively.

In this episode of the Don’t Panic It’s Just Data, Tudor Galos, Senior Privacy Consultant, speaks to Paulina Rios Maya, Head of Industry Relations, about the impact of privacy by default and design extend to user experience, where issues like consent fatigue and the necessity for user-friendly interfaces arise. 

Key Takeaways:

• Organisations face challenges in implementing privacy by default and design, including resistance to change and the need for cultural transformation.
• Privacy by default and design impact user experience, with issues like consent fatigue and the need for user-friendly interfaces.
• Regulations like GDPR and CCPA incorporate privacy by default and design principles, emphasising compliance and accountability.

Chapters:

00:00 - Introduction and Overview

01:00 - Core Principles of Privacy by Default and Design

02:19 - Difference from Traditional Privacy Practices

04:09 - Challenges in Implementing Privacy by Default and Design

05:33 - Impact of Privacy by Default on User Experience

08:14 - Alignment of Privacy by Default with Regulations

09:04 - Ensuring Compliance and Trust

11:24 - Implications of Emerging Technologies on Privacy

13:15 - Innovations in Privacy-Enhancing Technologies

15:50 - Conclusion

Safe Software's Feature Manipulation Engine (FME) plays a pivotal role in the City of Fremont's operations, particularly in ensuring accurate and efficient data submissions under the Racial and Identity Profiling Act (RIPA). By automating complex workflows and enhancing data quality, FME not only ensures seamless compliance with RIPA requirements but also optimises processes for their ITS and GIS divisions.

FME also drives innovation in projects like the DroneSense programme and their Cityworks asset management integration. With seamless data integration and powerful visualisations, FME empowers the City of Fremont to enhance operations, improve asset management, and support informed decision-making.

In this episode, Jonathan Reichental, founder at Human Future, speaks to John Leon, GIS Manager for the City of Fremont, to discuss: 

• FME 
• RIPA
• Public Safety 

Chapters:

00:00 - Introduction and Overview of the City of Fremont and IT/GIS Division

03:01 - Explanation of the Racial and Identity Profiling Act (RIPA)

04:27 - Challenges in Meeting RIPA Standards and Utilizing FME

06:21 - How FME Ensures Error-Free RIPA Data Submissions

09:40 - Benefits of Using FME for RIPA Compliance

10:39 - Other Innovative Projects Utilizing FME in the City of Fremont

13:30 - Future Plans for FME in the City of Fremont

17:17 - Recommendations for Government Agencies: Leverage FME for Data Submissions

Real-time data insights help identify performance bottlenecks, manage data efficiently, and drive innovation. Despite the growing need for these capabilities, organisations often face challenges in implementing effective real-time analytics. 

Achieving high-concurrency data processing is crucial for overcoming performance bottlenecks in real-time analytics. Embracing real-time analytics is not just a necessity, but a way to transform your data into actionable insights, optimise performance, and fuel business growth.

Yellowbrick is a modern data platform built on Kubernetes for enterprise data warehousing, ad-hoc and streaming analytics, AI and BI workloads that ensures comprehensive data security, unparalleled flexibility, and high performance. 

In this podcast, Doug Laney, a Data Strategy Innovation Fellow with West Monroe, speaks to Mark Cusack, the CTO of Yellowbrick, about the power of real-time analytics. 

Key Takeaways:

• Real-time analytics enables faster business decisions based on up-to-date data and focuses on enabling actions.
• Using a SQL data platform like Yellowbrick, designed for high-concurrency data processing, can address performance bottlenecks in real-time analytics.

Chapters:

00:00 - Introduction and Overview

01:07 - The Benefits of Real-Time Analytics

06:23 - Overcoming Challenges in Implementing Real-Time Analytics

06:51 - High Concurrency Data Processing for Real-Time Analytics

13:59 - Yellowbrick: A Secure and Efficient SQL Data Platform

Accurate and reliable data is essential for training effective AI models. High-quality data ensures precision, reduces bias, and builds trust in AI systems. Similarly,  Master Data Management (MDM) systems enhance data quality by integrating data from multiple sources, enforcing data governance, and providing a single source of truth. This helps eliminate discrepancies and maintain data integrity.

Integrating Product Information Management (PIM) with MDM ensures accurate and consistent product data across all channels, crucial for data-driven marketing. This combination centralises customer and product data, enabling precise targeting and personalised experiences. MDM and PIM integration leads to higher ROI and improved customer satisfaction by supporting effective marketing strategies.

In this episode of the EM360 Podcast, Paulina Rios Maya speaks to Philipp Krueger about integrating PIM and MDM functionalities and how it streamlines operations, improves data accuracy and supports data-driven marketing strategies. 

Chapters

00:00 - Introduction and Importance of Data Quality in AI Models

05:27 - Core Capabilities of an MDM System

08:13 - The Role of Data Governance in Data Management

13:37 - Enhancing Customer Experience and Driving Sales with Pimcore

19:47 - Integration of PIM and MDM Functionalities for Data-Driven Marketing Strategies

22:59 - The Impact of Accurate Data on Revenue Growth

27:28 - Simplifying Data Management with a Single Platform

One of the biggest challenges businesses face when it comes to data visualisation is handling the volume of data and the need for faster processing methods. 

There's a common misconception that effective data visualisation must be fancy and interactive, but simple visuals can be just as powerful. Ann K. Emery, an expert in the field, believes that accessibility doesn't have to be time-consuming or expensive. 

In this podcast, she shares actionable strategies for creating accessible visualizations with Paulina Rios Maya, Head of Industry Relations at EM360Tech. 

Key Takeaways

• Avoiding red-green colour combinations, 
• Ensuring proper colour contrast
• Using direct labelling instead of legends. 
• Avoiding using all-caps
• Using grey to highlight important information
• Employing small multiples to simplify complex visualisations. 

Chapters: 

00:00 - Introduction

00:54 - Defining Accessibility in Data Visualization

02:17 - Big A Accessibility Tips

06:36 - Little a Accessibility Strategies

12:28 - The Future of Data Accessibility