Name: Just Build It! How to Make MLOps a Reality In Your Organisation
Start: 2022-07-13T12:00:00Z
End: 2022-07-13T12:00:51.000Z
Location: BrightTALK
Rating: 5

Data is the foundation of any organization and therefore, it is paramount that it is managed and maintained as a valuable resource. 

Subscribe to this channel to learn best practices and emerging trends in a variety of topics including data governance, analysis, quality management, warehousing, business intelligence, ERP, CRM, big data and more.

Storytelling is one of humanity's oldest tools—yet in the corporate world, its power remains largely untapped. Every day, brilliant business ideas die silently, buried beneath technical specifications and corporate jargon. The result? Missed opportunities, disengaged stakeholders, and strategies that never reach their potential. What if you could break this cycle and transform how your ideas are received and implemented?

With GenAI, business leaders can go from dense and technical business language to persuasive narratives that connect and convert. By leveraging AI-powered storytelling techniques, operations leaders, IT professionals and transformation executives can overcome the traditional disconnect between technical teams and business decision-makers, creating alignment that accelerates adoption and drives measurable outcomes. For professionals working in enterprise processes, AI operations, and digital transformation, this capability isn't just nice-to-have—it's essential for ensuring your initiatives receive the right support and resources.

In this session, Inigo Antolin (Head of Data Management and Governance at The British Council) reveals how to harness GenAI to transform your business communication from forgettable presentations into strategic narratives that drive action and results.

Key Takeaways

- How the storytelling gap is undermining enterprise process improvements and technology adoption in data-driven organizations
- Practical frameworks for using GenAI to translate complex technical concepts into compelling business narratives
- Techniques to customize your storytelling approach for different stakeholders across the enterprise
- Ready-to-implement GenAI tools and prompts that enhance communication effectiveness without requiring creative expertise
- Measurable business outcomes achieved by organizations that have mastered GenAI-powered storytelling in process transformation initiatives

GenAI Storytelling: Turning Technical Complexity into Business Impact

Customer retention has never been more important - or challenging. With rising acquisition costs, keeping the customers you already have is paramount, but they're constantly bombarded with competing offers. The key to standing out and fostering loyalty lies in delivering personalized experiences that make them feel valued and understood. Are you truly leveraging your customer data to create these stickier relationships?

The surge of customer data coupled with fragmented digital touchpoints presents a complex challenge. Many companies are overwhelmed - struggling to translate raw data into actionable insights and personalised experiences that resonate with individual customers. Traditional marketing tactics fall short, leading to missed opportunities to build lasting relationships, drive loyalty, and achieve sustainable growth. This is precisely why embracing a connected customer strategy has never been more critical.

Join Anna Samkova, Co-Founder & Strategic Advisor at Albany Advisory, for this session to explore how to unlock exponential growth by turning customer data into personalized experiences with the C.L.I.E.N.T. Growth Framework.

Key Takeaways:

- Implement the C.L.I.E.N.T. Growth Framework to transform raw customer data into a powerful engine for personalization
- Discover proven strategies to activate data in ways that feel personal, purposeful, and commercial
- Gain actionable insights into what truly meaningful personalization looks like in 2025 and beyond – and how to deliver it effectively
- Identify and avoid common personalization pitfalls that can damage customer trust and undermine your ROI
- Understand how to build a connected customer strategy that drives customer lifetime value, loyalty, and sustainable revenue growth

The C.L.I.E.N.T. Growth Framework: Your Roadmap to Personalized CX Success

Small and medium enterprises are drowning in data while starving for insights. With unstructured data projected to represent up to 90% of all enterprise data by late 2025, and only 35% of SMEs leveraging cloud-based analytics, there's an enormous untapped potential for competitive advantage. In today's AI-driven landscape, the ability to transform raw data into actionable intelligence isn't just beneficial—it's essential for survival and growth.

While large enterprises deploy specialized teams and substantial budgets to tackle data challenges, SMEs face unique obstacles in extracting value from their information assets. Without proper frameworks for data cleansing, integration, and governance, organizations risk making critical decisions based on flawed insights. This session delivers practical, cost-effective strategies specifically designed for businesses without enterprise-scale resources, helping you navigate regional compliance variations and integrate data across disparate systems to drive measurable ROI.

Join Eugina Jordan (Chief Marketing Officer & Author at Telecom Infra Project (TIP)) to explore proven methods for converting data chaos into strategic advantage.

Key Takeaways:

- Practical frameworks for effectively managing both structured and unstructured data sources
- Step-by-step methods for ensuring data quality and integrity across your organization
- Integration strategies that bridge legacy systems with modern platforms without breaking the budget
- Governance approaches that address regional compliance variations while maintaining data accessibility
- Real-world examples of SMEs that have successfully converted disparate data into revenue-driving insights

From Data Chaos to ROI: Practical Frameworks for SMEs

The way employees communicate and collaborate is constantly evolving, and so too are the security threats they face. The rise of generative AI adds a new layer of complexity, requiring CISOs to proactively address the risks of data loss and potential harm to the organization.

Traditional security approaches often fall short in addressing the unique challenges posed by modern collaboration tools and the integration of GenAI. Understanding and mitigating these risks is critical for maintaining data privacy, preventing leakage, and ensuring compliance. We'll share Metrigy's latest research data to reveal actionable strategies for a comprehensive, business-aligned security strategy.

Join Irwin Lazar (President and Principal Analyst at Metrigy)  to discover the key components of a modern security approach, including how to: 

- Establish alignment between IT, collaboration, and security teams to address evolving threats.
- Identify emerging threats stemming from new applications and out-of-band communications.
- Understand how generative AI creates new risks for data privacy, leakage, and effective data management.
- Develop a comprehensive security strategy tailored for collaborative environments and the GenAI landscape.
- Evaluate the benefits of third-party platforms optimized for collaboration and GenAI security to enhance your security posture.

Secure Collaboration in the Age of GenAI: A CISO's Guide to Risk Mitigation

Sustainable business relationships center on both long-term collaboration and minimizing environmental impact throughout the supply chain. Effective manufacturing and supply chain processes are increasingly reliant on transparency, adaptability, and digital tools that allow for predictive, rather than reactive, planning.

This session presented by CEO Justin Newell will demonstrate -- using a few examples -- how organizations such as Volkswagen have leveraged generative AI and intelligent decision-making systems to enhance sustainability and efficiency within logistics, supply chain, and manufacturing. 

AI is most powerful when it processes dozens or even hundreds of factors simultaneously -- far beyond the capacity of human decision-making or traditional systems. AI-driven optimization engines allow companies to simulate countless supply chain scenarios in real time, identifying the most sustainable and efficient outcomes.

When manufacturing and logistics processes are optimized through AI, delays are minimized, expectations are met more consistently, and customer satisfaction rises due to great transparency and on time delivery performance. The transparency and speed that come from real-time data and predictive analytics foster trust and reliability -- both cornerstones of sustainable business relationships.  Solutions such as this not only reduce operational complexity but also help reach environmental and service quality goals more effectively.
This can lead to more sustainable business relationships — both in longevity and from an environmental impact -- and increased customer satisfaction. 

Key Takeaways 
1. Trends in AI for 2025 and 2026.
2. Real-world examples of AI/ML in action.
3. How complex logistics can be better managed with AI and digital decision making.
4. Innovative solutions to improve manufacturing operations and product lead times with AI.

Reshaping the Role and Impact of AI in Manufacturing and Supply Chain Logistics

In the era of zettabyte scale information, organizations often find themselves “data rich but insight poor.” While data volume has exploded, the ability to translate that data into competitive advantage lags behind. The consequence? Missed opportunities, inefficient operations, and a failure to capitalize on the full potential of today's technology.

But technology alone is not enough. True scalability demands a people‑first culture that champions collaboration, shared data definitions, and continuous learning. By aligning technical choices with clear business goals and fostering an environment where data informs every decision, companies can bridge the gap between big data and big insights - moving from dashboards that describe the past to systems that actively shape the future.

In this session, we delve into the challenges preventing organizations from maximizing their data investments. We explore how to bridge the gap between raw data and strategic insights by building scalable, purpose-built systems that leverage cutting-edge technologies like AI and machine learning. Discover practical approaches to data integration, governance, and analytics that empower your teams to make data-driven decisions, optimize processes, and unlock new revenue streams.

Join Dmytro Basan to discover how to turn your data into a strategic weapon.

Key takeaways:

- Define Clear Business Objectives: Align your data strategy with business goals to ensure a measurable ROI.
- Build a Scalable Data Foundation: Invest in a modern data architecture that can handle growing data volumes and diverse data sources.
- Embrace Automation and AI: Leverage AI-powered analytics to automate insights discovery and accelerate decision-making.
- Promote Data Literacy: Empower your employees with the skills and tools they need to access, analyze, and interpret data effectively.
- Measure and Iterate: Continuously monitor the performance of your data initiatives and adjust your strategy based on results.

Unlocking Data's Potential: Scaling Systems for Real Insights

The marketplace is buzzing with AI expectations, but where's the real business value? Despite massive investment and executive enthusiasm, most organizations remain stuck in the perpetual pilot phase, unable to translate AI potential into tangible outcomes that impact the bottom line.

Data volumes continue to expand at unprecedented rates. Meanwhile, regulatory requirements grow increasingly complex. These dual pressures demand that enterprises extract meaningful intelligence from their data assets while maintaining strict governance protocols. Practical AI implementation has become essential for survival, not just competitive advantage.

Moving from AI aspiration to implementation requires many critical decisions.
When is building in-house capabilities the right choice, and when is it best to go with third-party providers? How can you use the analytical power of AI while protecting sensitive data? And at what points does human expertise remain irreplaceable in your AI strategy?

Join TreeHive Strategy Principal, Donald Farmer as he delivers actionable strategies for enterprise leaders navigating real-world AI deployment challenges.

Key Takeaways:

- Practical decision frameworks for evaluating build-versus-buy AI strategies that align with your organization's data maturity and business objectives
- Governance approaches that protect proprietary data while maximizing AI's analytical potential across your enterprise
- Strategies for managing integration complexity when deploying AI within existing data management infrastructures
- Methods for identifying where human expertise remains critical in your AI-augmented data operations
- Actionable roadmap for scaling successful AI pilots into enterprise-wide data management solutions

AI That Works: Strategic Implementation for Measurable Business Outcomes

In today's fast-moving digital world, data has become one of the most valuable assets for any organization. However, simply collecting data is no longer enough. To fully unlock its potential, businesses must focus on making their data "AI-ready"—meaning clean, organized, accessible, and trustworthy for advanced analytics and artificial intelligence. This presentation explores the critical components of AI-ready data, emphasizing practical steps for effective data management, advanced reporting capabilities, and how these elements together drive real competitive advantage.

Through a mix of strategy, real-world examples, and simple explanations, this session highlights how organizations can move from traditional data practices to modern, intelligent systems that enable faster decisions, deeper insights, and more responsive customer experiences. Whether you're a business leader, data professional, or technologist, this talk offers actionable guidance on preparing your data ecosystem to meet the demands of AI-driven innovation.

Key Takeaways

- AI-Ready Data Defined: Understand what it means to have data that is structured, cleaned, and governed for use in AI systems.
- Data Management Strategy: Learn the essential practices for collecting, integrating, cleaning, and securing data across the enterprise.
- Smarter Reporting: Discover how AI-ready data enables predictive insights, real-time dashboards, and automated reporting.
- Competitive Edge: See how leading companies use AI-ready data to innovate faster, personalize services, and make smarter decisions.
- Actionable Steps: Walk away with a roadmap to begin transforming your data landscape for AI-readiness—starting today.

AI-Ready Data: Management, Reporting and Competitive Advantage

In an era where GenAI is proclaimed as both the savior and destroyer of traditional database development, organizations face a critical challenge: how to effectively leverage AI while maintaining SQL performance and expertise. 

This presentation cuts through the hype to explore how AI can serve as a bridge between expert and everyday developers. Through scenarios based on common industry challenges, we'll examine the potential pitfalls and opportunities in combining AI with traditional SQL expertise, including how seemingly minor query differences can lead to significant performance and cost implications in cloud environments. Drawing from industry observations and emerging practices, we'll examine concrete strategies for using AI to accelerate junior developer growth while reducing the burden on senior developers. 

Attendees will leave with actionable insights on combining AI tools with human expertise to improve query performance, reduce costs, and build stronger development teams. Whether you're skeptical about AI's role in database development or eager to implement it, this session provides practical guidance to help inform your organization's approach to AI adoption in database development.

Key Takeaways
- Learn practical approaches to SQL optimization with AI assistance that can significantly improve database performance. 
- Discover how SQL coders are adapting to AI tools - with over half of developers now using AI coding tools.  
- See demonstrations of identifying and solving common performance issues that can dramatically impact database performance. 
- Get an implementation roadmap based on successful software transformation approaches from industry experience. 
- Walk away with actionable team development strategies that combine AI tools with human expertise to build stronger SQL development practices.

Intelligent SQL Performance: Bridging the Gap Between Expert and Everyday Developers Through AI

Architectural innovation has become essential for organizations seeking to balance decentralization with control, or struggle for real-time insights while maintaining robust data governance and compliance.

This session presented by industry thought leader Donald Farmer covers practical strategies for reducing vendor lock-in, optimizing cloud and on-premises workloads, and ensuring data quality at scale, all while leveraging AI to automate and enhance governance.

Attendees will discover how forward-thinking enterprises are reimagining their data architectures to maximize agility, trust, and business impact.

Architectural Innovation for Insight and Governance at Scale

The data and analytics landscape is undergoing a profound transformation. Emerging trends in Gen AI, open table formats, collaborative analytics, and next-generation data platforms are converging to reshape the future of data architecture. In this webinar, we'll explore how:
- Gen AI is revolutionizing data roles and workflows. 
- Open table and open file formats are breaking down data silos, enabling seamless collaboration, and democratizing data access.
- Next-generation data platforms, including data lakehouses on data meshes, are emerging as powerful paradigms for managing distributed, decentralized data ecosystems

We'll delve into the architectural principles, design patterns, and implementation strategies that define these next-gen data platforms. Participants will gain insights into:
- Creating more flexible, scalable, and domain-oriented data infrastructures.
- Adapting to rapidly changing business requirements.
- Harnessing the power of Gen AI, open standards, and collaborative analytics.

Join us as we explore the pivotal trends and technologies transforming data architecture. Discover how to design and implement a unified data architecture that drives business innovation, scalability, and reliability.

Unifying Data Architecture for the Future: Gen AI, Open Standards, Collaborative Analytics, and Next-Gen Data Platforms

As organizations scale, managing data across diverse platforms and environments becomes increasingly complex. A modern data architecture must be flexible, scalable, and integration-ready to support real-time decision-making, AI-driven analytics, and regulatory compliance. In this session, we will explore:
1. The core principles of a resilient data architecture. 
2. Strategies for seamless multi-cloud and hybrid integration.
3. How to balance performance, security, and cost efficiency.
4. Best practices for future-proofing your data ecosystem.

Attendees will gain actionable insights to enhance data agility, optimize integration workflows, and maximize the ROI of their data infrastructure.

Future-Proofing Data Architecture: Strategies for Scalable and Seamless Integration

Learn what it really takes to make substantive AI progress. Tune into this talk to learn the seven tenets of enterprise transformation in the AI era which underscore both the current data situation of many companies and how to leverage this technology for the best results. 

1. Despite the long-standing hype, AI currently only includes natural language processing, not understanding. 
2. To make the best use of the AI, enterprises must build a modern knowledge foundation, i.e., data that’s logically connected, contextualized and extensible with the help of shared, interlinked, symbiotic graph data models. 
3. Organizations can’t be data mature if they’re not in data sharing mode whenever possible or pushing the data sharing envelope. Holding onto data doesn’t make sense if you’re not sharing with the partners who need what that data can provide. 
4. Transformation doesn’t matter if you’re not providing enterprise-wide and even boundary-crossing transparency in the process.
5. It doesn’t make sense to buy “AI products” without having the ability first to feed the AI the best, high quality, relevant, disambiguated data relevant to your business. 
6. Most of the AI software providers out there aren’t focused on helping you solve your data problems.
7. Most enterprises have huge, unresolved data problems. Therefore, most enterprises won’t benefit all that much from AI until they move up the data lifecycle management learning curve.

This talk will explore what effective transformation looks like in the age of AI and provide best practice guidance from organizations in leading industries.

About the Speaker
Alan Morrison is an independent consultant and freelance writer on data tech and enterprise transformation. He has a contributor to Data Science Central and TechTarget with over 35 years of experience as an analyst, researcher, writer, editor and technology trends forecaster, including 20 years in emerging tech R&D at PwC.

7 Tenets of Data and Enterprise Transformation in the AI Era

As data continues to grow in volume, variety, and velocity, emerging technologies like artificial intelligence (AI), machine learning (ML), and automation are reshaping the landscape of data management. 

This session from industry thought leader Efe Ogolo explores how these innovations are driving smarter data governance, real-time decision-making, and enhanced data quality. Join Efe Ogolo, as he discusses the strategic implications of AI-driven data pipelines, the rise of autonomous data platforms, and how organizations can future-proof their data ecosystems in this era of intelligent data management.

Manage Data for the New Era with Emerging Technologies

Digitalization and artificial intelligence (AI) are revolutionizing the process industry by capturing and leveraging tacit knowledge -- insights and expertise that are often unspoken and deeply embedded in the workforce. Through advanced data analytics, machine learning, and AI-driven decision support systems, manufacturers can now extract and codify this implicit knowledge. 

What You Will Discover in this Session:
1. Capturing Tacit Knowledge: Learn how AI can capture and codify tacit knowledge from experienced workers using AI tools, ensuring valuable expertise is retained and utilized.
2. Optimizing Operations: Discover how AI can optimize various aspects of operations, such as predictive maintenance, process optimization, and real-time monitoring, leading to increased efficiency and reduced disruptions and downtime.
3. Case Studies and Best Practices: Study real-world examples and best practices from leading companies that have successfully implemented AI and digitalization strategies.

This workshop, presented by CEO Andreas Eschbach will equip you with the knowledge to leverage digitalization and AI for improving efficiency and competitiveness in the process industry.

Unlocking Human Knowledge: Harnessing AI and Digitalization in the Process Industry

Managing data at scale presents challenges in accuracy, consistency, and accessibility. AI is reshaping this landscape by automating data quality monitoring and enabling natural language interactions for faster insights.

This session explores two AI-driven advancements in data management. AI-powered data quality and anomaly detection addresses inconsistencies by identifying duplicate records, detecting anomalies, and enforcing validation in real time. Natural language querying removes complexity from data access, allowing users to ask questions in plain language and receive structured responses without technical expertise.

Tune into this session to discover how implementing these innovations can reduce manual intervention, improve reliability, and make data-driven decision-making more efficient.

AI-Driven Data Management: Enhancing Accuracy and Accessibility

Web application environments are more complex than ever. Many span multiple clouds, often include containers, are connected via APIs, and delivered via CDNs. While exploits against known vulnerabilities remain common, advanced campaigns use a variety of tactics to compromise web applications. An abundance of tools claiming to address these challenges has left many security teams unclear as to where to focus their attention. On top of this, security teams are worried about how to address increased usage of genAI in development, and attacks that leverage AI. Join John Grady and Melinda Marks as they discuss recent research on the application protection market and modern application security to help security teams solve the challenges they face.

Trends in Application Security and Increasing AI Adoption

A monolithic approach to building systems rarely succeeds.  And yet there is clear value in having knowledge workers access data across an enterprise.  The Data Mesh concept put forth by Dehghani proposes a divide and conquer approach to tackling monolithic complexity by aligning implementation to the idea of domains.  In this workshop we will discuss an engineering approach for Data Mesh deployment that provides a practical architecture and governance framework to deliver connectivity across domains while encouraging a decentralized approach to stewardship and implementation. 

Learn how to accelerate delivery of data products using a domain-driven design approach.
Learn about best practices in data engineering aligned to a domain-driven approach to delivery of data products.
Learn how to deliver using autonomous, decentralized teams while avoiding anarchy in architecture and deployment.

An Engineering Approach to Implementation of the Data Mesh Concept

The modern data architecture is all about scale, allowing organizations to store and utilize high volumes of data. There are 2 components to realizing this scale: (1) Building an architecture with the right tools; (2) Building a team that can take proper advantage of these tools. This session will explain how you can achieve both. 

We’ll cover the key components of successful modern data architectures, along with how successful organizations are utilizing these components to achieve democratization and scale insights within their organizations.

• See examples of modern data architectures on the major cloud platforms, including AWS, GCP, Azure, Snowflake, and Databricks
• Learn tips and tricks for using modern data platforms to their full potential by scaling people, processes, and processing

Hear success stories from organizations that have successfully modernized their data warehouses

Bio: Nike Start is a Sr Sales Engineer for Asia Pacific & Japan region focused on helping companies transition to the modern stack. He has 10 years of experience engineering, analyzing, visualizing data across multiple verticals. When he’s not working with data he enjoys traveling, surfing, and spending time with his wife and 2 dogs.

LinkedIn Profile: https://www.linkedin.com/in/nikestart/
Location: Los Angeles, California – USA

How to Scale Insights With Your Modern Data Warehouse Architecture

Why do customers churn? Why is revenue down? What are my top channels to acquire new customers? Why is my business losing more orders in rural areas?

The answer is in your data.

As we move forward in modernizing the traditional data stack, companies are collecting more and more data.  Getting through all the data is a growing challenge.  Operationalized ML powered analytics can make analysts more efficient and eliminate rote data science work. Data teams need the ability to rapidly process cloud-scale data, separating signal from noise with pre-built and operationalized AI/ML tools.

Join this webinar to learn the basics of how to:

-Rapidly sift through data (in seconds)
-Determine key drivers of metric change
-Leverage artificial intelligence and machine learning in your data stack

Analysts vs. Machines: AI/ML for cloud-scale data

According to a recent Gartner study, it is estimated that 85% of big data projects fail. Main reason for this failure being that datasets significantly grow over time n size and get more complex; thus, the traditional BI solutions can’t keep pace to manage them. Augmented Analytics has proven to be the best solution for the dilemma. By utilizing AI and Machine Learning capabilities to BI solutions you can gather precise appropriate data, achieve comprehensible information patterns, perfect the data analysis models and anticipate better BI insights.

In this session we will learn how AI is helping the BI industry to transform into a better, more insightful data representations with more accurate predictions and recommendations. Using multiple AI elements, Augmented Analytics are revolutionizing the BI industry through the power of AI and Machine Learning. It is what every industry in any sector needs today to get the best out of Big Data and BI.

Takeaways:

What are Augmented Analytics and who are they for?
The benefits of adopting Augmented Analytics for BI
The AI elements for Augmented Analytics
Augmented Analytics challenges
Best practices and examples of utilization

Augmented Analytics: Discover How AI is Revolutionizing the BI Industry

Data has become the fuel that powers the Artificial Intelligence ecosystem. Global enterprises continue to face hurdles in training, development and testing of AI/ML models by Dev & Test teams, Data Scientists and MLOps teams: (a) access to data and sharing data is impeded by restrictions and regulations, (b) retention and storage of raw data is expensive and, (c) sparseness and bias in data. Gartner predicts that by 2024, 60% of data used for the development of AI and analytics projects will be synthetically generated. In this webinar, we will discuss how synthetic data can help maximize the usefulness of data by safely sharing it with whom, when and what. We will describe how recent advances in AI Generative Adversarial Networks (GANs) models can be used with structured, time series, incident log and session journey data effectively to create high-fidelity synthetic data that addresses privacy challenges.

Powering AI/ML Operational Innovation with Generative Synthetic Data

Many organizations are rightly focusing on making sure that their data is being managed, governed, and quality controlled. While this is necessary, it is not enough to add value to the business. For this investment to pay off, the data has to be used to drive decisions. This is easier said than done, and requires careful planning upfront to ensure that your architecture is built in a way that will be able to do more than reporting. I will discuss what steps you need to take before you go too far in your data journey.

Key Takeaways:
Learn what is needed for your data investment to pay off.
What are some common mistakes?
What to watch for in the coming years?

Building a Data Architecture that Adds Value to Your Business

Whatever your preferred form of analytics, interface preferences, coding skills, or architecture – cloud, on-premises or hybrid - handling more data more simply is in easy reach. In this session, we will explore, with examples from multiple industries, 3 simple steps data scientists, analysts and their teams can take to run powerful, scalable real-time analytics that reduce compute costs, data complexity, and risks. They include: 

• simplifying data management and storage processes, including real-time data. 
• automating and integrating continuous intelligence that combines new and historical data. 
• customizing code and visualization to drive faster, more useful decisions, commensurate with your organization’s bespoke needs and unique culture.

Streaming Analytics for No-Code, Low-Code or Loadsa-Code

Cloud data warehouses have fundamentally changed the way businesses capture and store data, making more complete and complex analysis possible than ever before. While most aspects of the modern data stack have continued to evolve, reporting and business intelligence tools haven’t delivered new ways to explore data and uncover ‘why’ a key metric changed.

It’s time to augment manual reporting and BI workflows. Decision intelligence allows data analysts to leverage the power of machine learning and AI to quickly and comprehensively analyze cloud-scale data—uncovering statistically relevant factors impacting metric change. By using decision intelligence along with BI workflows, data analysts can fully leverage the high-volume, high-dimensional data to quickly answer ‘why’ KPIs change and guide decision-making.

In this webinar you’ll learn:

-Industry trends driving the next wave of data and analytics—decision intelligence—forward 
-Data analyst workflows for using Sisu with BI tools, specifically highlighting using LookML to create datasets for Sisu
-Real-world examples of companies improving decision-making with decision intelligence

Augmenting BI tools with decision intelligence to improve decision-making

The increasing adoption of multicloud is leading to business-critical data getting more siloed. This makes it difficult and time-consuming for data engineers, architects, and analysts to quickly gain value from data. BigQuery Omni connects to various cloud data sources and provides a single pane of glass to access the data using SQL – like queries without leaving the interface. Looker allows you to explore, share, and visualize your company’s data to make better business decisions. In this session, you’ll see a demo of how to break down data silos using BigQuery Omni and Looker to securely and cost effectively analyze and visualize data across clouds.

Cross-cloud analytics and visualization with BigQuery Omni and Looker

Data teams are challenged with an explosion of data, and increased demand for analytic insights from a wide range of data consumers. Cloud data lakes represent the first destination for the newest and fastest-growing sources of customer and operational data, but making that data accessible for Business Intelligence and reporting workloads typically requires building and maintaining complex, manual, and ad hoc ETL processes and proliferating data copies. 

In this session, learn how an open lakehouse architecture simplifies and streamlines access to the data lake, and enables self-service analytics for technical and non-technical data consumers.

Accelerate Time to Insight with an Open Lakehouse Architecture

Advanced Big Data Analytics for the Data-Driven Business

Orchestrating data science and machine learning in an industrial setting is difficult. The data landscape is challenging, littered with complex problems and without the necessary freedom to design experiments or create observational studies on real-world processes. On top of that, you must manage stakeholders, operate cloud technologies, write software or wrap your solution into a product required to run predictions 24/7/365 – all while supporting business operations! With all this on your plate, you will need strategies to successfully incorporate MLOps in your organisation. In this talk, we will explore:


How to “bootstrap” ML Engineering (MLEng) and MLOps practices in your organisation.
Different ways to organize your MLOps teams to create optimum value.
Where to start on your MLOps journey.
How to avoid some common “gotchas” of starting out in MLOps.

Just Build It! How to Make MLOps a Reality In Your Organisation

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Just Build It! How to Make MLOps a Reality In Your Organisation

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