Don't Panic! It's Just Data

"Most companies still juggle with multiple different platforms; the communication between these tools and these platforms is happening in spreadsheets, and that is tedious, it's error-prone,” states Søren Lundtoft, Sr. Director of Product Management at Stibo Systems.

On the Don't Panic, It's Just Data podcast, host Doug Laney and Søren Lundtoft dive into how business leaders can overcome digital-first challenges, especially when managing product information across a fragmented landscape. They discuss how PXDC—Product Experience Data Cloud—provides a unified, cloud-native solution for centralizing, governing, and syndicating product data. PXDC eliminates manual bottlenecks, automates data onboarding, and ensures every stakeholder works from a single source of truth, enabling faster launches and more consistent brand experiences.

The conversation explores the future of product experience, highlighting how AI and agentic workflows are transforming data management. PXDC leverages AI to automate content creation, localization, and compliance, while its composable architecture adapts to rapid market changes. The ultimate advice for data leaders: design systems for change, build on a solid data foundation, and embrace PXDC to unlock agility and growth in the age of AI

The Sr. Director of Product Management at Stibo Systems identified the main issue as the use of multiple platforms and internal systems. He said that communication between these tools often occurs in spreadsheets, which are tedious and prone to errors. This leads to a situation where the last mile of data management becomes the longest mile, as companies struggle to share information with third parties. 

Alluding to the downsides of unstable downstream channels, where requirements change frequently. As he explained, a company might download a spec sheet from a retailer, but by the time they complete it and are ready to go live, a lot of changes have taken place. This ongoing state of change makes it nearly impossible to avoid mistakes and delays.

Lundtoft ended with an important piece of advice for C-suite leaders. He urged them to "decide for change" and shape their data systems so they can adjust to quick advancements in the market. He believes that data is "more important than it is right now" and serves as the true foundation for the current AI wave.

To learn more about the solution to managing product data and AI and its future, tune into the podcast on YouTube or Spotify at @EM360Tech.

• Brand owners face significant challenges in a digital-first world.
• Many companies struggle with multiple data platforms.
• Communication between tools often relies on spreadsheets.
• The last mile of data delivery is the most challenging.
• Consistency in product information is crucial for trust.
• AI can optimise data management processes.
• A single source of truth is essential for data governance.
• Data inconsistency can lead to costly errors.
• Future data management will involve agentic workflows.
• Adaptability in data models is necessary for future changes.

• 00:00 Introduction to Data Challenges in a Digital World
• 01:31 Navigating Complex Data Systems
• 05:46 Ensuring Consistency in Product Information
• 07:44 Streamlining Data Management for Efficiency
• 11:35 The Future of Product Experience and AI
• 15:04 Key Advice for Data Management Leaders

Stibo Systems is a global leader in Master Data Management (MDM) solutions, dedicated to helping businesses achieve data transparency and operational efficiency. With a rich history dating back to 1794, Stibo Systems empowers companies to connect, govern, enrich, and syndicate their data across various domains, ensuring a single, accurate view of their information.

Headquartered in Aarhus, Denmark, Stibo Systems is a trusted partner for many of the world’s largest and most innovative companies. Their solutions enhance the quality and value of master data, driving informed decision-making and business success.

With an erratic and fast business environment, finance teams are facing high pressure to process reports. The main challenge lies in how mid-market firms achieve digital transformation, not by abandoning familiar tools but by making them more effective. 

In this episode of the Don't Panic It's Just Data podcast, host Kevin Petrie, Vice President, Research and Head of Data Management Practice, BARC, speaks with Maeghan Carriere, Divisional Vice President, Software Sales NA, insightsoftware; and Nate Cook, Director of Product Marketing, insightsoftware. 

They discuss the importance of automation, the role of CFOs as strategic leaders, and, most importantly, how mid-market companies can leverage tools like Spreadsheet Server for digital transformation. The conversation also spotlights the need for finance professionals to upskill in AI and data analysis to remain competitive.

All speakers agree that the key is in equipping finance professionals with direct, real-time access to their data in the environment they know and trust – Excel.

Cook believes that the role of the Chief Financial Officer (CFO) and their team has changed massively. "We really expect them to look back only as it helps them find a path forward.”

This new role requires finance teams to become effective data teams but how can they do that? A recent Gartner study found that 75 per cent of CFOs "said they own or co-own enterprise data and analytics at their organisations." Unfortunately, these teams "sometimes struggle to have access to the data that they need." The time wasted on tedious data entry and report generation is time lost for important analysis and strategic thinking.

"The short answer is Spreadsheet Server," Cook told Petrie, explaining that better decisions rely on better data, and Spreadsheet Server provides the latter.

Also Watch: Struggling with ERP Data? How to Get Real-Time Reporting in Excel

Automating reporting doesn't just save time; it allows finance professionals to develop skills in areas like Excel, data analysis, and AI. Carriere points out that AI is an important tool that every professional needs to learn to use effectively.

"Once you understand how data flows and connects in real time, you're better positioned to use AI tools because you can quickly tell if the results make sense," she explains.

The key message for IT decision-makers is clear, as per insightsoftware’s divisional VP, "Stop fighting Excel and make it more powerful instead." 

By embracing the tool that finance teams prefer, organisations can achieve quicker results, faster adoption, and ultimately free their financial experts from manual tasks. Cook also notes, Spreadsheet Server is "one way to help remove a lot of that toil and refocus the time that your folks are spending on the more strategic parts of analysis and decision-making that can help drive your organisation forward."

• Finance teams are facing scaling pressures and resource constraints.
• The need for speed in decision-making is critical for finance leaders.
• Automation can save finance teams significant time and improve productivity.
• CFOs are increasingly expected to be strategic leaders within organisations.
• Excel remains a preferred tool for finance teams and should be leveraged in digital transformation.
• Mid-market companies should identify specific areas for technology to deliver efficiencies.
• Real-time data access is essential for better decision-making.
• Upskilling in AI is necessary for finance professionals to remain competitive.
• Finance teams can develop analytical skills while automating reporting processes.
• Building trust in data is crucial for effective AI implementation.

• 00:00 Introduction to the Podcast and Guests
• 02:22 Current Challenges in Financial Reporting
• 05:18 Transforming Finance Teams with Automation
• 11:21 Digital Transformation for Mid-Market Companies
• 13:43 Accessing ERP Data for Better Decision Making
• 18:04 Upskilling Finance Teams for AI Integration
• 20:45 Key Takeaways for CXOs

Welcome back to Meeting of the Minds, a special podcast episode series by EM360Tech, where we talk about the future of tech.

In this Big Data special episode of the Meeting of the Minds, our expert panel – Ravit Jain, Podcast host,  Christina Stathopoulos of Dare to Data and a data and AI evangelist, Wayne Eckerson, data strategy consultant and president of the Eckerson Group and Kevin Petrie VP of Research at BARC, come together again to discuss the key data and AI trends, particularly focusing on data ethics. 

They discuss ethical issues related to using AI, the need for data governance and guidelines, and the essential role of data quality in AI success. The speakers also look at how organisations can measure the value of AI through different KPIs, stressing the need for a balance between technical achievements and business results. 

Our data experts examine the changing role of AI across various sectors, with a focus on success metrics, the effects on productivity and employee stress, changes in education, and the possible positive and negative impacts of AI in everyday life. They highlight the need to balance productivity with quality and consider the ethics of autonomous AI systems.

In the previous episode, new challenges and opportunities in data governance, regulatory frameworks, and the AI workforce were discussed. They looked at the important balance between innovation and ethical responsibility, looking at how companies are handling these issues.

Tune in to get new understandings about the future of data and AI and how your enterprise can adapt to the upcoming changes and challenges. Hear how leaders in the field are preparing for a future that is already here.

Also watch: Meeting of the Minds: State Of Cybersecurity in 2025

• Generative AI is creating a supply shock in cognitive power.
• Companies are eager for data literacy and AI training.
• Data quality remains a critical issue for AI success.
• Regulatory frameworks like GDPR are shaping AI governance.
• The US prioritises innovation, sometimes at the expense of regulation.
• Generative AI introduces new risks that need to be managed.
• Data quality issues are often the root of implementation failures.
• AI's impact on jobs is leading to concerns about workforce automation.
• Organisations must adapt to the probabilistic nature of generative AI.
• The conversation around data quality is ongoing and evolving. AI literacy and data literacy are crucial for workforce success.
• Executives are more concerned about retraining than layoffs.
• Younger workers may struggle to evaluate AI-generated answers.
• Incremental changes in productivity are expected with AI.
• Job displacement may not be immediate, but could create future gaps.
• Human empathy and communication skills remain essential in many professions.
• AI will augment, not replace, skilled software developers.
• Global cooperation is needed to navigate the evolving AI landscape.
• Data quality is critical for mitigating risks in AI applications.
• Organisations must prepare for the proliferation of AI models.

• 00:00 Introduction to the Future of Data and AI
• 03:30 Significant Shifts in Data and AI Landscape
• 06:52 The Role of Education and Training in Data Literacy
• 09:59 Regulatory Perspectives: GDPR vs. US Approaches
• 13:06 Risks and Challenges in Implementing Generative AI
• 16:53 Data Quality: The Foundation of AI Success
• 21:59 The Impact of AI on Jobs and Workforce Dynamics
• 27:49 The Future of Workforce and AI Literacy
• 30:15 Job Displacement and the Importance of Junior Roles
• 32:05 AI's Impact on Professional Roles
• 34:26 The Role of AI in Software Development
• 39:54 The Necessity of Human Involvement in AI
• 41:10 Data Governance and Global Cooperation

Generative AI has captured global attention, powering everything from chatbots to intelligent assistants. Yet in the enterprise, its promise often hits a dead end. According to Gartner, 80 per cent of enterprise data remains unused or “dark,” because conventional AI struggles to interpret complex, domain-specific information.

In this episode of the Don't Panic It's Just Data podcast, EM360Tech host Trisha Pillay speaks with Andreas Blumauer, Senior Vice President at Graphwise, about how retrieval-augmented generation (RAG) and its advanced application, Graph RAG, are levelling up enterprise AI. Together, they explore the limitations of traditional AI, the critical role of knowledge graphs in improving data accuracy, and what it takes for organisations to successfully adopt these technologies.

While RAG enhances Generative AI by enabling it to retrieve relevant data from large knowledge bases, Graph RAG takes it further. By integrating knowledge graphs, Graph RAG preserves the relationships, sequences, and meaning inherent in enterprise data. This ensures AI outputs are not just collections of facts, but structured insights that reflect the logic of an organisation’s knowledge.

These advantages include:

• Higher accuracy: Retrieval precision can increase from 80% to 95%, reducing errors in AI outputs.
• Trustworthy results: Outputs are explainable and traceable, providing transparency that enterprises require.
• Scalable integration: Connects data across silos and departments, making AI adoption enterprise-ready.

“Graph RAG respects the structure of enterprise data instead of flattening it. That’s what makes it trustworthy,” explains Blumauer.

Generative AI opened the door to possibilities. RAG made it actionable. Graph RAG takes it to the next level. By transforming dark, siloed data into structured, actionable knowledge, Graph RAG helps organisations achieve the accuracy, trust, and scalability essential for navigating the next frontier of enterprise intelligence.

• 80 per cent of enterprise data remains unused or dark.
• Traditional AI struggles to interpret complex enterprise data.
• RAG retrieves information from within the enterprise data landscape.
• Graph RAG improves the accuracy of AI outputs.
• Knowledge graphs link data points across different silos.
• Building a knowledge graph is a strategic investment.
• Incremental growth is possible with knowledge graphs.
• Graph RAG can increase accuracy from 80 per cent to 95 per cent.
• Data quality and governance are essential for AI success.
• The future of enterprise AI relies on effective knowledge management.

00:00 Introduction to RAG and Graph RAG

03:04 Understanding the Importance of Knowledge Graphs

05:46 Adopting RAG: Organisational Readiness and Strategic Investment

08:51 Real-World Applications and Benefits of Graph RAG

11:56 The Evolution of Knowledge Graphs in AI

14:46 Future of GraphRAG and Enterprise AI

17:36 Rapid Fire Questions and Closing Thoughts

Graphwise is a leading enterprise AI company specialising in knowledge graph technologies. By combining retrieval-augmented generation (RAG) with advanced graph-based approaches, Graphwise helps organisations turn siloed, complex data into accurate, actionable insights, enabling smarter decisions, scalable integration, and trustworthy AI outcomes.

Inclusivity and accessibility remain some of the biggest challenges for data events. True inclusion is not the result of a single initiative, but continuous effort, honest reflection, and a willingness to listen to the community. At Big Data LDN, these values are rooted in the event’s mission. By highlighting both established leaders and emerging voices, the conference creates a stage where diverse perspectives can shine, while fostering a welcoming environment where every attendee feels they belong.

In this episode of Don’t Panic, It’s Just Data podcast, host Trisha Pillay is joined by Andy Steed, Event Director at Big Data LDN, and Roisin McCarthy, Founder of Women in Data. The conversation examines how Big Data LDN has become a cornerstone of the data community over the past decade and why advancing inclusivity is crucial to the conference’s continued impact in its 10th year.

Over the past 10 years, Big Data LDN has grown beyond a traditional tech conference. As Andy explains, keeping the event relevant means constantly adapting to the changing needs of the data community. From established thought leaders to rising stars, the agenda is designed to showcase the breadth of talent driving innovation across industries.

Roisin highlights how Women in Data has grown to an incredible 95,000 members worldwide, becoming an anchor of community engagement in the data industry. At Big Data LDN, this partnership translates into a stronger focus on inclusivity, mentorship, and ensuring that diverse voices are not only present but heard. The Women in Data Lounge at this year’s event will be a dedicated hub for networking, support, and inspiration.

Both guests stress that real inclusion doesn’t happen by accident. It requires intention, continuous reflection, and a willingness to address barriers head-on. Big Data LDN is taking deliberate steps to create a welcoming environment, one where accessibility, representation, and community-building are central to the experience.

Big Data LDN brings together world-class keynotes, hands-on workshops, and community-driven sessions, offering both deep technical insights and opportunities to connect. Networking is built into the event, with spaces designed for conversations that continue well beyond the conference floor. Taking place September 24–25, this year’s edition will be the largest yet, including a new conference that explores AI agents, AI governance and data products on September 23 for those seeking more focused, technical exploration.

Listen to the full episode of Don’t Panic, It’s Just Data to hear more about how Big Data LDN is shaping the future of data events.

• Big Data LDN has significantly evolved over the past decade.
• Women in Data has grown to 95,000 members, enhancing community engagement.
• Inclusivity and accessibility are ongoing challenges in data events.
• Real inclusion requires continuous effort and reflection.
• Big Data LDN showcases both established and rising stars in data.
• The event aims to create a welcoming environment for all attendees.
• Expect exceptional content and interactive community events at Big Data LDN.

00:00 Introduction to Big Data LDN and Women in Data

02:37 Evolution of Big Data LDN and Its Impact

04:39 Challenges of Keeping Events Fresh and Relevant

08:36 Inclusivity and Accessibility in Data Events

09:01 Creating Real Inclusion in the Data Space

11:19 Opportunities and Challenges for Big Data LDN

16:17 What to Expect at Big Data LDN

18:37 Women in Data Lounge and Community Engagement

20:32 Final Takeaways and Closing Remarks

Also available on #Youtube, Apple and EM360tech.com

#BigDataLDN #DataCommunity #WomenInData #InclusionInTech #TechEvents #DataInnovation

Big Data LDN (BDL), the ultimate data event of the year, celebrates its 10th anniversary this year. This year’s event is scheduled to take place on September 24 and 25, with a brand-new deep-dive conference held on September 23. 

In this episode of the Don't Panic, It's Just Data podcast, host Shubhangi Dua, Podcast Producer and B2B Tech Journalist at EM360Tech, speaks with Michael Ferguson, CEO of Intelligent Business Strategies and BDL Conference Chair, and Palesa Amadi, BDL Conference Manager. 

The speakers provide a sneak peek of the upcoming Big Data LDN conference. They discuss how AI affects data and analytics, and spotlight the importance of data governance. Additionally, they talk about the different theatres and sessions available at the conference. The conversation also shines a light on keynotes and notable speakers, the changes in data engineering and architecture, and the learning opportunities for attendees. 

Get ready for "AI madness", says the Conference Chair. This year's event will feature AI in many forms, from AI agents walking the aisles to its integration into almost every software product on the exhibition floor.

"It's going to be hectic," Ferguson expresses. He expects a strong push toward AI-driven data governance and data management. Such changes aim to simplify complex tasks and make tools easier for more users to access. Instead of just seeing copilots, attendees will experience multi-agent capabilities and even "AI apps."

With 16 theatres and numerous speakers, the conference focuses on learning. Conference Manager describes how the content is tailored for everyone—from engineers and analysts to students.

"You're going to come to this to learn and to add to your knowledge, and of course, network," says Amadi. The show has changed its focus to include new theatres like Data Products and Streaming Analytics, along with Decision Automation. Some theatres have been renamed to highlight the growing influence of AI, such as the Data and AI Strategy Theatre.

A new "Data for Good" theatre will feature sessions on sustainability and using data for unconventional purposes, like tackling global food needs.

The speaker lineup is a big draw, featuring well-known experts and thought leaders. Amadi is particularly excited about keynote speaker Zhamak Dehghani, who will discuss the "apparent redundancy" of the term data mesh. Dehghani, who wrote the viral white paper on this topic, will advocate for her views, as Amadi puts it. Another highlight will be a session on the key role of big data and AI in the gaming industry, led by Carly Taylor.

The event's closing keynote will be delivered by the world-renowned physicist Professor Brian Cox. He will talk about quantum computing and the universe as a quantum computer. 

For those looking for more, Big Data LDN is hosting a new paid pre-show conference on September 23rd called Data-Driven LDN. This event is mainly designed for attendees in a high-level enterprise role, like CDOs and CTOs, and will be led by Mike Ferguson. It’s a chance to explore topics such as agentic AI, data governance, and data products in depth.

Whether you’re an industry expert or a curious newcomer, the 10th anniversary of Big Data London promises to be an engaging and transformative experience, demonstrating how quickly data and AI are changing our world.

For further information, tune into the podcast.

• Big Data LDN is celebrating its 10th anniversary.
• AI will dominate this year's conference with new technologies.
• Data governance now includes privacy, security, and compliance.
• The conference will feature various theatres focusing on specific topics.
• Sustainability will be a key theme in the Data for Good theatre.
• Speaker quality is ensured through a rigorous selection process.
• The event aims to cater to all levels of expertise.
• Real-world case studies will provide practical guidance.
• Data architecture is evolving to lower latency and improve access.
• Networking opportunities will be abundant at the conference.

• 00:00 Introduction to Big Data London 2023
• 01:48 AI's Dominance at Big Data London
• 06:15 Navigating the Conference: Theatres and Content
• 15:02 Data Governance Challenges for AI
• 20:00 Improving Speaker Quality and Content
• 32:35 Key Technical Lessons from the Conference

In this episode of the Don't Panic, It's Just Data podcast, Kevin Petrie, VP of Research at BARC and the podcast host, is joined by Dainius Jocas, Search Engineer at Vinted, and Radu Gheorghe, Software Engineer at Vespa.ai. They discuss how Vinted, an online marketplace for secondhand products, modernised its data architecture to address new AI search use cases and the challenges faced with Elasticsearch. 

From the switch to Vespa and the advantages of supporting multiple languages and complex queries, the podcast offers insights on the trade-offs organisations must think about when updating their search systems, especially regarding AI and machine learning applications.

Vinted’s search architecture was built on Elasticsearch before they switched to Vespa. Elasticsearch is a functional system that presents a few major challenges. With over 20 supported languages, the company's "index per language" approach created significant sharding problems, leading to infrastructure imbalances and constant adjustments.

"The index for the French language, the biggest language that we support, was more than three times bigger than the second biggest language, which created imbalances in the Elasticsearch data nodes' load," Jocas explained.

In addition to these technical obstacles, organisational issues arose as teams responsible for different parts of the search process found themselves "pointing fingers at each other at an increasing rate." The need for a more integrated, effective solution became clear.

The search for a better solution led Vinted to Vespa. The initial adoption was a "one success story" when a machine learning engineer, working on recommendations, discovered that Vespa was ten times faster than Elasticsearch for their use case. 

This initial benchmark, run on a single decommissioned server, was a "true testament to how efficient Vespa is when it comes to serving requests,” Jocas told Petrie.

Vespa helped Vinted solve their language problem by allowing it to set a language per document. Thus, it eliminates the need for separate indexes and the associated sharding headaches. As Jocas put it, "We got out of the sharding problem once and for all."

• Vinted faced challenges with its initial Elasticsearch architecture.
• The need for better integration between matching and ranking was identified.
• Vespa outperformed Elasticsearch in handling image search and recommendations.
• Transitioning to Vespa involved significant learning and support from developers.
• Vespa allows for language-specific document handling, simplifying architecture.
• Organisations must evaluate the complexity and volume of their data before transitioning.
• Vespa is optimised for query performance, while Elasticsearch excels in data writing.
• The learning curve for Vespa can be steep, but support is available.
• It's important to focus on optimising new systems rather than emulating old ones.
• Partial updates in Vespa are more efficient than in Elasticsearch.

• 00:00 Introduction to Vinted and Vespa
• 02:05 Vinted's Initial Data Architecture Challenges
• 06:40 Common Trade-offs in Modernising Search Architecture
• 10:21 Transitioning to Vespa: Key Steps and Lessons Learned
• 14:11 Supporting Multiple Languages with Vespa
• 15:44 Vespa's Architecture and Its Flexibility
• 16:48 Use Cases: Vespa vs. Elasticsearch
• 20:34 Advice for Organisations Modernising Search Architecture
• 23:00 Final Thoughts on Transitioning to New Technologies

Vespa.ai is an AI Search Platform for building and operating large-scale RAG, recommendation, and personalisation systems. It unifies data, inference, and ranking in a single query path for real-time results over massive datasets. Vespa delivers high throughput and low latency, and is available as a managed service.

Vinted.com is an online marketplace making second-hand a first choice. It connects buyers and sellers of pre-owned clothing, electronics, and home décor in a community-driven space. Active in 21 European markets plus the USA, Vinted serves 120 million users and 77 million monthly visitors. Its advanced search and recommendation engines handle 1 billion items and process 25,000 searches every second, with over 10,000 real-time data updates per second to keep the experience fast, relevant, and engaging.

In this episode of the Don't Panic It's Just Data podcast, Christina Stathopoulos talks with Miller and Michael Wiley, Sr. Product Manager of Spreadsheet Server. They discuss the challenges and solutions in financial reporting, focusing on real-time reporting from ERP systems to Excel.

The discussion covers the current state of financial reporting, the obstacles finance departments encounter, and how tools like Spreadsheet Server can help. Such tools allow finance teams to become strategic partners in their organizations. The conversation also shares real-world applications and case studies, showing how these solutions affect efficiency and decision-making.

The speakers share imperative insights on how enterprises can remake their finance teams from traditional "bean counters" to proactive strategic partners within their organizations. This is driven by the power of real-time data and efficient reporting.

Considering how advancements, especially with AI in the picture, the business world is tunning at an exorbitant high pace. Due to this, access to real-time financial data is no longer a luxury but a necessity.

Despite that, many finance teams and IT decision makers face significant delays. These delays restrict timely and competent decision-making. Essentially, it all comes down to the way employees use ERP systems to deliver decision-makers reports in a usable format.

They depend on Excel because it is familiar and flexible. However, obtaining live, useful data from an ERP system into spreadsheets often requires tedious manual processes, exports, and reformatting. Unfortunately, it creates a significant blockage, wasting valuable time and delaying important insights.

"ERPs do a fantastic job with organising data, classifying it, structuring it, but then the reality is you want to then grab that data, pull it out and put it in a meaningful format so that you, your board, your leadership or your owner can digest that data,” expressed Wiley.

He added that it’s challenging to take data out of a system and put it in those unique formats. “That's where Excel is great. And that's where we typically find our users."

Imagine an Excel-based reporting solution that connects you to your ERP, providing dynamic reports that refresh with the click of a button. That's Spreadsheet Server. It cuts down the time spent on manual data extraction, reformatting, and reconciliation. This allows your finance team to move from simple number tracking to becoming strategic partners.

“Spreadsheet Server is your bridge,” says Wiley. “It connects you from your ERP to Excel in a dynamic fashion. It's a dynamic report. You can leverage, drill down and dive into details without having to reinvent the wheel all the time, export, put it in a pivot table, reformat, cleanse your data, remove all those duplicates, all those tedious steps get removed in that process."

For more information on insightsoftware, click here.

• The current state of financial reporting often lags behind real-time needs.
• Finance teams are looking to become strategic partners within organizations.
• Spreadsheet Server connects ERP data to Excel dynamically.
• Automation can significantly reduce the time spent on repetitive tasks.
• Real-world examples illustrate the efficiency gained with Spreadsheet Server.
• insightsoftware offers a range of solutions beyond just Spreadsheet Server.
• Evaluating time spent on tasks can reveal opportunities for improvement.
• Understanding user needs is crucial for selecting the right technology.
• The complexity of data reporting requires multi-source integration.
• Optimising processes can lead to better decision-making and efficiency.

• 00:00 Introduction to the Podcast and Guests
• 02:59 Current State of Financial Reporting
• 06:29 Challenges in Real-Time Reporting
• 10:26 Spreadsheet Server: Bridging the Gap
• 13:39 Real-World Examples of Spreadsheet Server
• 20:16 insightsoftware's Broader Solutions
• 22:37 Key Takeaways for Decision Makers

"Customers are now expecting highly personalised, seamless, and efficient interactions across all the touch points,” stated Rahul Sareen, the Senior Leader–Cross Industry, Private Equity and Startups Solutions at Amazon Web Services (AWS). Sareen’s statement represents the state of customer experience (CX) today. 

It’s a demand that is forcing businesses to completely rethink how they engage with customers.

In the recent episode of the Don’t Panic It’s Just Data podcast, John Santaferraro, CEO and Head Research Analyst at Ferraro Consulting, speaks to Gary Class, Industry Strategist, Financial Services at Teradata and Sareen. They discussed how data, AI, and hyper-personalisation are reshaping customer experience (CX). 

The time of one-size-fits-all customer service is long over. Sareen emphasises this change by quoting Jeff Bezos: "If you have 4.5 million customers, then you cannot just build one store. You have to build 4.5 million stores." 

This isn't just about dividing customers into groups; it’s about providing a truly unique and personal experience for each person. 

Alluding to the use of gen AI and AI agents in customer experience (CX), Class highlights a key application of AI – analysing unstructured data. He says, “The one time that customers tell you what they really want and need, it's on the phone." 

By "grafting the unstructured data analysed through speech and text onto the structured data," businesses get equipped with rich insights on untapped possibilities. Overall, it also provides a 360-degree preview of the customer experience (CX), which allows for increased proactive and custom developments.

AI agents are set to take hyper-personalisation to new heights, the podcast guests believe. Alluding to the pros, Sareen points to their "predictive and proactive nature," which lets AI agents "identify and resolve issues before even customers are aware of them." 

Imagine a situation where a device automatically finds a problem, starts a support case, identifies a solution, and even applies a fix—all in real-time and without any input from the customer. Sareen calls this the "Nirvana state" of AI-driven customer service. It promises a smooth and genuinely smart experience.

Simplifying how we manage raw data and turn it into quality data in a single system is critical for meeting the needs of AI in customer experience (CX).

This is where Teradata comes in. Class says the company’s approach centres on "harvesting all the data that you can collect in a conformed and harmonised framework." Their strategy includes providing industry-specific data models and leveraging the "capacity of a relational database engine that can deal with complexity at scale." 

This involves bringing analytic capabilities directly to the data, minimising data movement, and embracing open data formats. It allows customers to "bring their own model" and integrate with various model repositories.

Similarly, AWS focuses on delivering unified, low-code/no-code platforms designed to make development easy. Sareen articulated, "Rather than looking at multiple tabs, multiple services, how can we provide that unified experience?" 

Platforms like Amazon Connect for contact centre solutions and Amazon SageMaker Unified Studio for AI development aim to make it easier to deploy advanced customer experience applications by removing the complicated details.

The partnership between AWS and Teradata is crucial for achieving the vision of a customer experience (CX) powered by AI-driven hyper-personalisation. It relies on unified data platforms and real-time decision-making.

• Customer experience is about listening to your customers.
• Hyper-personalisation requires a unified data platform.
• AI agents can proactively resolve customer issues.
• Organisations must balance automation with human interaction.
• Data complexity necessitates a structured approach to analytics.
• Emerging technologies like NLP are crucial for understanding customer needs.
• The future of CX is being built today with accessible technologies.
• Seamless integration across channels enhances customer satisfaction.
• Partnerships are essential for scaling customer experience solutions.
• Understanding customer behaviour is key to effective CX strategies.

00:00 Introduction to the Future of Customer Experience

02:56 Innovative Applications of AI in Customer Experience

05:58 The Complexity of Data and Simplifying Processes

09:05 Hyper-Personalisation in Customer Experience

12:03 The Role of AI Agents in Enhancing Personalisation

14:44 Unifying Technology for Seamless Customer Experience

17:55 Partnerships Driving Transformation in Customer Experience

20:43 Key Takeaways for CIOs

At Teradata, we believe that people thrive when empowered with trusted information. We offer the most complete cloud analytics and data platform for AI. With harmonised data, Trusted AI, and faster innovation, we enable more confident decision-making that improves business results. Find out more at www.Teradata.com.

Artificial intelligence (AI) is on everyone’s mind, and its impact doesn't escape the cybersecurity industry. The industry experts acknowledge not just the benefits but also the cybersecurity threats of AI integrations.

As Pascal Geenens, Director of Threat Intelligence at Radware, puts it, "It's AI, so everything is changing weekly. What I talked about two weeks ago has already changed again." 

The constant change means that malicious actors are not just adopting AI, they're leveraging it to create new threats at a striking pace. 

In this episode of The Security Strategist Podcast, Richard Stiennon, an industry Analyst, Author and Chief Research Analyst at IT-Harvest, speaks with Geenens.

They discuss how cybersecurity threats are enhanced by AI. This includes how attackers are using AI tools, the implications of new technologies like agentic AI, and the challenges posed by AI advancements. 

The conversation also touches on the role of nation-states in utilising AI for cyber operations, the concept of vibe hacking, and the future of interconnected AI agents.

Malicious hackers evidently first used AI in 2023, specifically through prompt injection attacks on large language models (LLMs) such as ChatGPT. 

Attackers would find "evasion techniques" to bypass ethical guardrails, asking questions indirectly to generate malicious scripts or gather information for attacks. 

Geenens says, "If you would ask the direct question, how can I commit a murder and get away with it? He would say, no, no, no, that goes against my ethical principles. But there are ways around it."

The game changed with the emergence of offline models and specialised services like WormGPT and FraudGPT. These models, distilled from larger ones and enhanced with hacking-specific information from underground forums, lowered the barrier to entry for aspiring cyber criminals. 

"They created their own model and sold it as a service underground. And that model was geared towards helping anyone with questions to interact with a prompt and to make their malware better, increase the effectiveness of their malware," explained Geenens. 

This accessibility meant that "more actors would actually move from script kiddie level to a more sophisticated level." Teenagers, in particular, took advantage of these AI assistants. They provided a friendly, non-toxic environment to learn and develop hacking tools, unlike the often unwelcoming underground forums.

In 2024, the focus shifted to AI agents, which provide attackers with automated workflows. Unlike LLMs, agents can interact with their environment, gather updated information, execute tools, and even spawn new agents that communicate with each other. 

"You can have a manager agent that says, okay, I need to develop something. It’s a big problem here. I need to develop a tool. I have an agent that does the development. I have an agent that does the QA testing,” depicts Geenens. “And then I have another agent who's a problem solver who will help the other tools do their job. And they interact with each other.”

This agentic capability majorly hastens the exploitation of vulnerabilities. Research showed that AI agents can quickly rebuild proof-of-concept exploits for published CVEs. 

Geenens stressed the dramatic reduction in time: "Earlier, when the CVE was published, it took 24 hours-48 hours and a security researcher who typically posted a proof of concept online in Python before the actual attacks in the wild would start.” 

“But now with those agents and those workflows has been proven as much easier to get access and to get a proof of concept. That window might now reduce from 48 hours to a couple of minutes,” he added. 

• AI is changing the landscape of cybersecurity threats.
• Attackers are using AI to enhance their hacking capabilities.
• Guardrails in AI are not foolproof against malicious intent.
• Teenagers are increasingly entering the cybercrime space due to AI tools.
• Agentic AI allows for automated workflows in attacks.
• The sophistication of attacks has not drastically changed, but entry barriers have lowered.
• Nation-states are using AI for disinformation and phishing.
• Vibe hacking represents a new frontier in automated vulnerability discovery.
• MCP and agent-to-agent protocols will shape the future of AI interactions.
• AI will be necessary to combat AI-driven threats.

00:00 AI in Cybersecurity: The New Frontier

06:51 The Evolution of Hacking: From Script Kiddies to AI-Enhanced Threats

12:48 Nation States and AI: The New Age of Cyber Warfare

15:12 Vibe Hacking: The Future of Coding and Security

19:14 The Internet of Agents: A New Era in Cybersecurity

25:11 Emerging Threats: Indirect Prompt Injection Attacks