Name: An Engineering Approach to Implementation of the Data Mesh Concept
Start: 2022-07-14T21:00:00Z
End: 2022-07-14T21:00:44.000Z
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.

Decision intelligence promises to augment human judgment with AI-derived insight, but governance frameworks have not kept pace. When an AI system contributes to a decision, who is accountable for the outcome? How do we trace the information that informed the decision back to its sources? What documentation must exist before the decision is made, and what must be preserved afterward? These questions become urgent as AI moves from analysis into action.

This session presents a governance framework for AI-assisted decisions built on the distinction between lineage and provenance. Lineage documents the flow of data through systems: which sources informed the AI, through what transformations, with what dependencies. Provenance records the history of specific values: who created them, when they were modified, and what parameters governed their treatment. For decision governance, both are necessary. Lineage shows the decision drew on approved sources through documented pipelines; provenance ensures specific values can be traced through a custody chain.

Particular attention is given to retrieval-augmented generation, where decisions may be informed by content retrieved from knowledge bases. Governance must begin at ingestion, documenting sources before they enter the system. Access controls must filter retrieval by permissions. Citation trails must connect generated outputs to retrieved passages and original sources. The session examines what governance can automate and what it cannot.

Key Takeaways:
- Decision governance requires both lineage (system flows) and provenance (value histories).
- RAG systems require governance from ingestion through retrieval to output.
- Accountability cannot be automated; governance must preserve human responsibility.
- Citation trails enable defensible AI decisions.

Governing the Decision: Accountability in AI-Assisted Intelligence

AI governance presents an architectural challenge that centralized approaches cannot solve. Monolithic review processes create friction that development teams bypass; purely decentralized models produce inconsistency that regulators will not accept. The session presents a federated architecture for AI governance: central functions set policies and standards while domain functions retain authority to adapt implementation within defined thresholds for risk, financial impact, and infrastructure cost.

This federated model rests on five components: policies and standards that define acceptable use; governance structures that distribute authority appropriately; risk assessment processes that scale from self-service questionnaires to expert review; technical infrastructure including model registries and automated policy enforcement; and monitoring systems that track drift, accuracy, and fairness over time. Each component must be designed for the distinctive nature of AI: algorithms that learn and change, rather than fixed logic that can be reviewed once and approved indefinitely.

The session traces a maturity progression from experimentation through restricted deployment to production scale, with governance controls calibrated to each stage. Participants will examine how permission models, audit schedules, and oversight intensity can be tiered to match risk levels, avoiding both the paralysis of excessive control and the exposure of insufficient oversight.

Key Takeaways:
- Federated governance balances consistency with domain-appropriate flexibility.
- Five components (policies, structure, risk assessment, infrastructure, monitoring) form a complete architecture.
- Tiered permission and audit models match oversight intensity to risk level.
- Maturity progression from experimentation to production requires different controls at each stage.

Architecting AI Governance: Federated Models That Scale

In the world of business analytics, technical accuracy does not equate business impact. Without the trust of leadership, even the most sophisticated analytics models fail to translate data into action. Data managers must recognize the critical gap is not in the data, but in the narrative they present. 

In this session, Google’s Jiaxi Zhu will introduce a four-part, narrative-driven analytics framework for bridging the divide between analytics and real-world business outcomes. 

Through in-depth examples, he will demonstrate how to:
- Design analytics models with action in mind.
- Frame outputs in a decision-ready context. 
- Shape complex insights into coherent, compelling stories that drive action.

From Insights to Impact: Building Narrative-Enabled Analytics to Drive Executive Decision-Making

In a world where AI, automation, and digital systems are advancing faster than most organizations can keep pace, the real differentiator isn’t the technology itself, it’s the culture behind it. Innovation cannot thrive in environments where teams are overwhelmed, siloed, or unclear about how to adapt. In this session, Theo Harvey reveals how leaders can build a foundation for continuous innovation, enabling teams to navigate uncertainty with confidence and creativity.

Drawing from his two decades as a senior engineering leader at Cisco and his current work as CEO & Co-Founder of SynsorMed—a Google-backed digital health company—Theo shares practical frameworks for cultivating accountability, adaptability, and human-centered innovation. Attendees will learn how to shift from reactive problem-solving to proactive idea generation, fostering a culture where creativity is an everyday practice rather than a special event.

Whether your organization is scaling AI adoption, modernizing information systems, or leading large-scale transformation, this session provides actionable strategies for aligning people, processes, and technology. The result: teams that are more engaged, more empowered, and more equipped to turn complexity into opportunity.

Attendees will learn:
- How to build a culture where innovation is habitual, not hierarchical.
- The connection between accountability, adaptability, and creativity.
- Frameworks for driving progress amid uncertainty and rapid technological change.
- How to inspire teams to turn ideas into implementation with speed and clarity.

Innovation Starts With You: Building a Culture of Creativity in the Age of AI

Healthcare organizations are generating more data than ever before, yet meaningful insights remain out of reach due to siloed systems and fragmented infrastructures. In this session, Anita Nayak, Founder and CEO of ClinDCast, explores how interoperability is transforming healthcare analytics from reactive reporting to real-time intelligence. She’ll reveal how breaking down data silos across EHRs, labs, and payer systems can dramatically enhance decision-making, predictive accuracy, and patient outcomes.

Through real-world examples, Anita will share how ClinDCast has helped healthcare networks unify disparate data sources using HL7 FHIR, API orchestration, and master data mapping frameworks. Attendees will gain a practical understanding of how integrated data models reduce manual reporting, strengthen clinical insights, and enable proactive care delivery.

Participants will leave with actionable strategies to:
- Build interoperable data architectures that scale analytics.
- Overcome technical and organizational barriers to data integration.
- Champion a culture of data ethics, integrity, and shared accountability.

Join this session to learn how interoperability is not just a technical goal—but a strategic pathway to better care, smarter operations, and a truly connected healthcare ecosystem.

Bridging Data Silos: How Interoperability is Shaping the Future of Healthcare Analytics

By 2026, most organizations aren’t struggling with a lack of data — they’re struggling with turning that data into something leaders can actually use. The gap between “collecting data” and “making decisions with it” has never been wider. This session will help you to close that gap.

Tune into this webinar from CEO and industry thought leader Eugina Jordan to learn:
- What’s changing in 2026: the rise of governed AI access, unified metadata layers, predictive correlation engines, and analytics workflows that run continuously instead of once a quarter.
- A clear, step-by-step architecture for building analytics that scale — from data ingestion and quality controls to semantic layers and real-time operational dashboards.
- How to govern AI and analytics together, so insight generation doesn’t outpace your security or compliance posture. RBAC/ABAC integration, auditability, data lineage requirements, and context-aware access policies. 
- How to enable self-service analytics while protecting quality and preventing shadow AI or shadow dashboards.
- How to prove ROI, with simple ways to tie analytics outcomes to operational efficiency, revenue lift, or risk reduction.
- Case studies showing how companies reduced insight lag, increased forecast accuracy, and improved decision velocity by fixing just a few critical parts of their data pipeline.

If you want a session that gives you frameworks you can implement immediately, this is it. You’ll leave with a checklist, a reference architecture, and actionable steps to build a data organization that finally produces insights leaders trust — and use.

2026 Data Insights Playbook: How to Build It, Govern It, and Prove ROI

Organizations are investing more in data than ever, yet many still struggle to translate those assets into clear, actionable insights. In this session, industry thought leader Farid Sheikhi will explore practical strategies to eliminate data chaos, strengthen governance, and build an insights-driven enterprise equipped for 2026 and beyond.

Attendees will learn:
- How to eliminate data chaos by strengthening governance, definitions, and data visibility across the enterprise.
- A practical blueprint for becoming an insights-driven organization, including the maturity stages that matter in 2026.
- How to operationalize analytics and GenAI so insights move beyond dashboards and drive real decision-making.
- Actionable strategies to convert data investments into measurable business impact, including culture, process, and technology enablers.

From Data Chaos to Clarity: Building an Insights-Driven Enterprise in 2026

As AI capabilities expand, a common assumption takes hold: that analytics will gradually be absorbed into automated systems, with human analysts playing a diminishing role. This session challenges that assumption. AI excels at pattern detection, summarization, and prediction at scale; but analytics provides something AI cannot automate. Verification against business context, interpretation for stakeholders, accountability for decisions, and iteration based on feedback remain irreducibly human contributions. Analytics is not being replaced; it is being repositioned as the last mile of insight.

The session examines how the relationship between AI and analytics is shifting. AI handles volume and velocity; analytics supplies judgment and meaning. Organizations that understand this complementarity can design workflows where AI accelerates the routine while analysts focus on the consequential. Those who misunderstand it risk automating the wrong things: optimizing speed while losing the interpretive depth that makes data valuable.

Drawing on research into how AI affects analysts of varying skill levels, the session also addresses a differential effect: experienced analysts often find AI amplifies their creativity and productivity, while less experienced staff may feel threatened or displaced. This has implications for hiring, training, and team design that analytics leaders cannot afford to ignore.

Key Takeaways:
- AI and analytics serve complementary functions; substitution narratives misread the opportunity.
- Verification, interpretation, accountability, and iteration remain human responsibilities
- Experienced analysts benefit disproportionately from AI augmentation; organizations must design for this differential.
- The strategic value of analytics increases, not decreases, as AI capabilities expand.

The Last Mile: Why Analytics Matters More in an AI-Driven World

The customer journey doesn't live in one department. It stretches across marketing, sales, finance, and support — and along the way, manual processes, disconnected tools, and slow handoffs create gaps that frustrate customers and weaken business performance. From invoices that go unpaid, to limited visibility into cash flow, to rising churn, enterprises are feeling the impact of these disconnects in tangible ways that affect the bottom line.

This session cuts through the GenAI hype to focus on practical, safe, and measurable implementation across end-to-end customer process flows. You'll discover methodologies for identifying which problems are worth solving first, how to integrate manual and digital steps into one intelligent workflow, and where human oversight remains essential to prevent costly mistakes. We'll also dig into real-world strategies for managing GenAI's inaccuracies — ensuring invoices are correct, forecasts are reliable, and customer interactions build trust instead of risk.

Join Eugina Jordan, CEO and Co-Founder, YOUnifiedAI, as she reveals proven approaches to GenAI implementation that deliver measurable business outcomes across critical enterprise functions.

- Identify high-ROI opportunities for GenAI implementation across three critical workflows: invoicing, cash flow management, and customer retention
- Implement effective human-in-the-loop protocols that mitigate GenAI hallucinations while maximizing automation benefits
- Design cross-functional workflows that eliminate departmental silos and create seamless customer experiences
- Develop practical metrics to measure GenAI's impact on revenue acceleration, operational efficiency, and customer satisfaction
- Apply a strategic framework for scaling successful GenAI implementations across your organization

Transforming the Customer Journey with GenAI Across the Enterprise

Support teams are at a critical inflection point, with GenAI reshaping customer expectations overnight. While automation promises efficiency, it often creates impersonal experiences that damage loyalty. The most successful organizations are discovering that GenAI's true power lies not in replacing human interactions, but in strategically enhancing them at every touchpoint.

Generative AI offers transformative potential for support experiences, but only when implemented with a human-centered approach. This session defines GenAI specifically within the support context and explores practical applications that benefit both end-users and support agents. You'll discover how intelligent automation can scale operations while simultaneously improving satisfaction metrics across your support ecosystem.

Join Phyllis Drucker (Influencer, EZ2BGR8 Service Management) to explore proven frameworks for implementing GenAI solutions that put people first while driving measurable business outcomes.

Key Takeaways:

- Find the right balance between scaling support operations and maintaining customer satisfaction
- Implement GenAI-powered chatbots that deliver near-human experiences through contextual understanding
- Create customized, tailored responses that increase satisfaction and engagement 
- Establish measurement systems that quantify GenAI's impact and inform future initiatives
Develop a practical roadmap for implementing GenAI in support functions, regardless of your starting point

Human-Centered GenAI Support: Winning Customer Loyalty in the Automation Era

Despite massive investment and executive enthusiasm, most organizations implementing GenAI remain stuck in perpetual pilot purgatory. Behind the headlines and hype, a select group of companies has quietly crossed the chasm from experimentation to enterprise-wide transformation—achieving what their competitors merely discuss in boardrooms. Their secret isn't better technology, but rather a systematic approach to implementation that balances ambition with pragmatism.

GenAI's true power lies in enterprise-wide applications—reimagining customer experiences, supercharging product innovation cycles and transforming operational efficiency across every business function. The question isn't whether to implement GenAI, but how quickly you can transition from experimentation to organization-wide transformation while balancing innovation with responsible governance and ethical considerations.

Join Pankul Chitrav (Application Release Engineer, TD Bank) to discover how your organization can build an actionable GenAI roadmap that delivers immediate business impact while positioning you for sustainable competitive advantage.

Key Takeaways:

- Explore how leading organizations are applying GenAI to create tangible business value across functions
- Learn specific strategies to expedite product development, content creation, and decision-making through GenAI implementation
- Master techniques for streamlining operations while delivering hyper-personalized services at scale
- Gain actionable insights on governance frameworks, ethical considerations, and sustainable GenAI adoption strategies

Unleashing Generative AI: Transform Business Strategy from Concept to Competitive Edge

With AI tools exploding, organizations today are bombarded with new technical tutorials and prompt engineering tactics. But they often become obsolete within weeks. While competitors chase fleeting GenAI techniques, the true competitive advantage lies not in mastering specific tools, but in cultivating something far more fundamental. This presentation reveals the missing catalyst that transforms GenAI from a specialized technology into an organization-wide force multiplier.

The challenge isn't in teaching your teams how to use GenAI—they're already adopting it in their personal lives. It's in creating an environment where innovation flourishes organically across all levels of your organization. Brian Kirsch demonstrates why empowering employees to apply their unique job expertise to GenAI implementation delivers more sustainable results than top-down technical approaches. Learn how to establish the right framework that enables continuous innovation without requiring constant retraining as the technology evolves.

Join Brian Kirsch, IT Instructor and Industry Expert, to discover the one evergreen principle that drives successful GenAI adoption across organizations of all sizes.

Key Takeaways:

- Understand why traditional GenAI implementation approaches fail to deliver lasting competitive advantage
- Learn to empower employees at all levels to become drivers of AI innovation without technical expertise
- Discover a sustainable framework for GenAI adoption that remains effective despite rapid technological changes
- Gain practical strategies for fostering an environment where AI-driven innovation flourishes organically
- See real-world examples of how simple ideas can create profound change across departments

Unlocking the Secret to Sustained Innovation with GenAI

Generative AI is rapidly transforming collaboration by enabling capabilities such as summarization, workflow automation, translation and more. Generative AI offers the potential to fundamentally transform how teams and individuals collaborate and create, eliminating mundane activities and fostering innovation.

In this session, we’ll explore how Generative AI is changing the workplace collaboration landscape, including availability of tools within collaboration applications, third-party tools, and studios enabling custom AI tool creation. We’ll look at organizational strategies and share real-world examples of approaches that achieve measurable success. We’ll also look at security, compliance, and governance concerns to minimize risk.

Key Takeaways:

- Learn how to identify and implement the right Generative AI tools for your team's specific needs, whether that means using built-in features in existing applications, integrating third-party solutions, or building custom tools for unique workflows.
- Understand how to create a foundational strategy for introducing Generative AI into your organization, including understanding which collaborative tasks can be automated to free up time for strategic work.
- Gain knowledge to evaluate and address security, compliance, and governance risks associated with Generative AI, enabling you to implement these tools responsibly and minimize potential liabilities.
- Discover how to measure the success of Generative AI initiatives by measuring before and after improvements resulting from implementation.

Leveraging Generative AI to Improve Collaboration

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

Sustainability has become a defining element of modern customer experience (CX), shaping how companies build trust, deliver value and foster long-term relationships. Today’s stakeholders expect more than responsiveness and services; they want transparency, environmental accountability and data-driven insights.  

CHEP's transformation from traditional platform provider to data-driven sustainability partner illustrates how organizations can turn environmental commitments into competitive advantages. By embedding circular economy principles into customer touchpoints, companies create dual value: reducing environmental impact while enhancing customer relationships through transparency, efficiency and shared purpose. This approach transforms sustainability from a compliance exercise into a powerful engine for customer loyalty and operational excellence.

Hear from CHEP leaders on how sustainability is reshaping customer experience expectations across industries.

Key Takeaways:

- Insights for embedding sustainability metrics and circular innovation across customer touchpoints 
- How transparent environmental reporting strengthens customer relationships and reduces friction points
- Techniques for leveraging digital systems to enable circular business models and quantify sustainability impact
- Methods for integrating self-service platforms with predictive analytics to deliver more sustainable, responsive service

Accelerate Loyalty Through Circular Innovation in Today's Sustainability Market

Customer conversations contain strategic revelations that most executives never hear, yet these unfiltered moments often predict market shifts before formal data ever could. Although 84% of CX leaders advocate for bringing these insights to the executive table, many organizations still treat customer interactions as operational noise rather than the strategic signal they actually represent. 

Although 48% of companies now employ interaction analytics, many struggle to effectively distribute these insights beyond the contact center. Additional advantages come fromsystematically analyzing customer interactions to inform product development, marketing strategies, and executive decisions; creating a unified approach to customer experience that drives measurable business outcomes and strengthens customer loyalty across the entire journey. 

Join Robin Gareiss, CEO of Metrigy, as she shares research-backed strategies that transform conversation data into business value. 

Key Takeaways: 

- Discover which conversation metrics deliver the most substantial business intelligence and how to capture them effectively 
- Learn practical frameworks for distributing interaction insights across departments to create organization-wide customer-centricity 
- Understand how to quantify the ROI of conversation intelligence through improved business metrics and enhanced customer satisfaction 
- Gain actionable implementation strategies that work regardless of your current analytics maturity

Conversation Intelligence: The Hidden Gems that Impact all Business Decisions

Your customer just spent 15 minutes navigating your support portal only to end up exactly where they didn't want to be: on hold with your call center. This all-too-common scenario reveals the main flaw in how organizations approach digital self-service: designing for scale rather than CX success. 

Unfortunately, customer portals are often designed primarily to eliminate contact via more costly resources. When designed solely to scale support, they fail to meet the CX mark. The best portals humanize the digital experience by understanding the customer's journey and anticipating needs before frustration sets in.

This session examines the critical need to balance operational efficiency with customer-centered design principles that transform portals from necessary evils into preferred channels.

Join Phllis Drucker (Influencer, EZ2BGR8 Service Management) to explore how to achieve great CX results through innovative portal design that puts customers first while still delivering operational benefits.

Key Takeaways:

- Identify common pitfalls that typical portals fall into and how to avoid them
- Apply customer journey mapping techniques to create intuitive, emotionally intelligent digital experiences
- Implement a practical roadmap for designing and innovating a new portal experience
- Measure the true ROI of your portal beyond call deflection metrics
- Balance automation with human touchpoints to create meaningful digital interactions

Humanizing Digital Portals: Transform Self-Service in the Age of CX Expectations

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

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

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

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

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An Engineering Approach to Implementation of the Data Mesh Concept

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