Name: Choosing the right data platform for your business
Start: 2023-04-19T10:00:00Z
End: 2023-04-19T10:00:56.000Z
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

Data architecture-as-a-service or DAaaS is a new self-service paradigm that is ideal for data meshes. It empowers local data owners to create architecturally compliant data repositories, domains, and pipelines without IT assistance. It is the culmination of self-service, where business units liberate themselves almost entirely from enterprise IT. If done right, DaaS eliminates data silos, reduces data bottlenecks, eases the burden on enterprise data teams, and empowers local domains to service their own data needs. It’s also a key ingredient in the data mesh, an emerging distributed architecture for data ownership and management. 
 
Data architecture-as-a-service is a verbal twist on cloud processing environments, such as software-as-a-service or platform-as-a-service. This moniker conveys that it’s possible to abstract architecture and build it into easy-to-use, customer-facing tools. When we abstract data architecture, we solve the most enduring data pain point in the data world: the proliferation of data silos and pipelines that wreak havoc on data consistency and trustworthiness.
 
You will learn:  
• What DAaaS is
• Why DAaaS is critical for governed self-service
• How DAaaS prevents data silos and empowers data domains

About the speaker:
Wayne Eckerson is an international thought leader in data and analytics who thinks critically, writes clearly, and presents persuasively about complex topics. He is a best-selling author, sought-after consultant, and noted speaker. Eckerson has advised a range of companies about how to implement data and analytics programs, architectures, and infrastructure, including Walmart, New Balance, and Children’s Hospital of Philadelphia. Eckerson is President of Eckerson Group, a consulting and research firm that helps organizations get more value from their data. He has degrees from Williams College and Wesleyan University.

Unlocking the value of data mesh with Data Architecture as a Service

Data architecture provides a blueprint for how an organizations can collect, store, manage, and utilize their data to meet business objectives. It defines the rules, standards, and guidelines for managing data as a valuable organizational asset and supports the integration of data from disparate sources into a coherent and usable whole. 

Anti-patterns of data architectures refer to common mistakes and pitfalls that data architects may encounter while designing and implementing data solutions. These anti-patterns can lead to suboptimal performance, data inconsistencies, and high maintenance costs. For example, over-normalization, complex data models among other specific characteristics. 

This webinar from data industry thought leader Dr. Pragyansmita Nayak will identify and analyze the most prevalent anti-patterns in data architectures, along with their root causes and potential solutions. By understanding these anti-patterns, data architects can systematically avoid them and design more effective and efficient data solutions while continuing to meet the needs of their organizations.

Speaker bio:
Dr. Pragyansmita Nayak is the Chief Data Scientist at Hitachi Vantara Federal (HVF). She explores the "Art to the Science" of solution architectures orchestrating data, APIs, algorithms and applications. She has over 24+ years of experience in software development and data science (Analytics, Machine Learning and Deep Learning). She has led projects for several Federal Government agencies (DoD/Civilian) in the domain of Federal Accounting, Operational Analytics, Data Fabric, Object Storage, Metadata management, Records Management and Data Governance.  She holds a Ph.D. in Computational Sciences and Informatics from GMU (Fairfax, VA) and Bachelors of Science in Computer Science. For more information on Pragyan's professional experience, please visit her LinkedIn profile at https://www.linkedin.com/in/pragyansmita and Twitter profile at https://twitter.com/SorishaPragyan.

Anti-patterns of data architectures

Many businesses are looking to gain more impactful insights out of their data. Snowflake is a modern data analytics platform many businesses use to help gain those insights. However, going from the starting point of knowing what you want your data to provide to actually extracting valuable business insights is challenging. Whether you’re trying to figure out how to collect all of your disparate data from various sources or you’re struggling to analyze your data effectively - it’s hard work. It takes a ton of time, resources, and expertise to create and build the infrastructure required to collect the data and analyze it effectively.

Luckily, Growth Acceleration Partners (GAP) can help overcome those challenges. GAP has a team with the knowledge, expertise, and experience to help your business get what you want out of your data using Snowflake and Microsoft Azure. Learn how GAP can help any company accelerate their timeline and get to valuable data insights faster.

What you’ll get out of this webinar:
- A detailed understanding of what it takes to build the infrastructure required for a modern data analytics platform with Snowflake and Azure
- A better understanding of why Snowflake is the right choice for your data platform needs
- An overview of how the GAPAccelerator helps you get value out of your data faster
- Technical details of how the infrastructure is built and what goes into
- Description of how the experts at GAP can work with your team to provide continuous data analysis after implementation

Accelerating Time to Value with Snowflake

Data management methods are evolving quickly, as enterprises invest in gaining data agility to accelerate insightful decision-making. Modern data management is being driven by an accelerated shift to data in the cloud and the subsequent innovation in data technologies and advanced analytics. Enterprises are recognizing new opportunities to derive value from their data and to save time and money, and they’re taking a fresh look at new data management approaches to reap the rewards of smarter and accelerated decision-making. 

In response to this demand for more modern data treatments, a confusing array of architectures, technologies, and approaches has sprung up – and it’s not easy to tell which ones will truly deliver better business outcomes and which ones are just hype. Should you invest in graph technology and metadata management? What exactly is a data fabric? How can you leverage the power of AI and machine learning? 

This session will take a look at trends in data management that are worth investigating, and explain how a modern data platform can help you implement them in a way that delivers business value for your enterprise.

Data Platform Capabilities for Modern Data Management Architectures

When organizations build new applications and services, they often need scalable and lightning-fast data management platforms to support their innovations. What does it take to support new, innovative applications? Register for this tech talk to hear from Fern Halper, VP of Research at TDWI Research, who specializes in advanced analytics, along with representatives from Redis and Ekata, a Mastercard company, as they discuss one such use case: supporting Ekata’s smart identity verification solution to detect fraud. 

Presented by: 
• Henry Tam, Principal Solutions Marketing Manager | Redis
• Fern Halper, Ph.D., VP Research | TDWI
• Milena Babayev, Product Marketing Manager | Ekata
• Jason Frazier, Software Engineering Manager |Ekata

Real-time, Scalable Applications Powered by a Modern Data Platform

In today’s rapidly evolving business landscape data plays a critical role. Modern data architecture provides the tools and practices to harness the power of data and turn it into strategic advantage.  Whether it is data lakehouse, data fabric, data mesh or cloud data governance modern data architecture is enabling business leaders to make better decisions, quicker.
 
In this talk, Rajeev Pai, Director of Technology Strategy & Transformation at Deloitte, will explore what does modern data architecture entail and how it can be used to unlock business value. 
 
Key takeaways:  
- The need for newer ways for bringing, processing and distributing data in the enterprise. 
- Leading patterns and practices organizations are adopting in this journey with examples.
- Challenges organizations may face when adopting some of these new approaches.
- And how to overcome those challenges.

Whether you are a business leader looking to drive growth and innovation or a data professional seeking to help your business this talk will provide valuable insights from frontline and research.

About the speaker:
Rajeev is a leader in the space of Technology Consulting & Transformation with over 20 years of experience across the globe in the financial services industry, helping business leaders navigate change. He combines deep domain expertise in Capital Markets F2B Processes and Banking to provide thought leadership and tailored advise while challenging business leaders to achieve their vision. He is experienced in large scale System Integration, Platform Re-engineering, Enterprise Architecture, Enterprise Data Management (Data Strategy, Architecture and Management), Analytics & Data Visualisation, Operating Model constructs, Design Thinking, and has strong familiarity with Emerging Tech. He is a certified in Business Sustainability Management from the 
Cambridge Institute of Sustainability Leadership(CISL).

Data-driven success: How modern data architecture unleashes business value

Data lineage and knowledge graphs are data management capabilities that many companies would like to or have implemented. These capabilities assist in getting value from data and sustaining competitive advantage. However, several challenges with the implementation of these capabilities exist:
- Data management professionals have different views on these capabilities. Their definitions are not aligned within the data management community. These capabilities have a lot in common.
- The implementation of these capabilities is time- and resource-consuming. The correctly defined scope is one of the implementation success factors.
- Many companies still document data lineage and knowledge graphs manually in Excel.
- Plenty of various automated solutions exist. However, finding a proper solution is problematic as providers don’t have an aligned terminology to describe functionality. Often, quite differently labeled solutions deliver similar functionality.

In this session, we will discuss how to solve challenges and:
1. Demonstrate a metamodel of data lineage and knowledge graphs.
2. Show differences and similarities of these capabilities in terms of business drivers, architecture, and use cases.
3. Provide an overview and comparison of various data lineage and knowledge graphs IT tools.

About the speaker
Dr. Irina Steenbeek is a data management practitioner with more than 12 years of experience. The key areas of her professional expertise are the data management maturity assessment, implementation of data management frameworks, and data lineage. Irina has practical experience in software implementation such as ERP and DWH/BI, management consultation, financial and business controls, and data science.

Social links:
1. https://datacrossroads.nl/
2. www.linkedin.com/in/irina-steenbeek

Data Lineage & Knowledge Graphs: Similarities and Differences

The problem with data warehousing and lakehousing is that they don’t go far enough. They don’t attack the root input problem: dumb, increasingly siloed and duplicative data and stranded, duplicated logic. It’s the proverbial garbage in, garbage out scenario, unless companies allocate their entire innovation budgets to integration.
 
More and more apps create the need for more and more reinvent-the-wheel integration, because each app has its own repository and its own data model. This is one reason why big banks have more than 10,000 databases each.
 
More and more apps with their own repositories is one reason we could be in the Yottabyte Era by 2030. If the world has to store two yottabytes of data per year, 40% of the economy could be dedicated to just storing data–90% of which is duplicated data that’s hard to reuse. 
 
For their part, applications duplicate the core description and predicate logic that should live with the data, where it can be shared and reused repeatedly within a knowledge graph.
 
A better, data-centric approach puts data and data models first. A FAIR data approach -- smarter data that’s designed to be findable, accessible, interoperable, and reusable–addresses the integration problem up front. No more garbage in, so no more garbage out. Data becomes self-describing with the help of the logic that used to be trapped in applications. 
 
This talk will examine how organizations in various industries use semantic knowledge graphs to solve their data integration and analytics problems at a fraction of the cost of data warehousing or lakehousing. It’s an organic approach to data and logic that can eliminate growing amounts of waste and complexity. Companies can run these systems at low cost in parallel with their legacy environments until they commit to graph data model-driven development, which is when even more substantial benefits will accrue.

FAIR data: Superior data visibility and reuse without warehousing

As companies build their data analytics practice, they quickly outgrow running analytics off their operational store that powers their applications. Building a read replica only buys them time until they hit scalability limits with their growing internal and customer demand. This is where one hits the crossroads of going all in with a cloud data warehouse or choosing an open data lake house approach to future-proof them for scale, performance, and cost efficiency. In this workshop, Matt Fuller and Tom Nats lead you through how you can easily build and manage an open data lake house architecture using open-source technologies such as Trino and Apache Iceberg to support your growing analytics. Trino is an open source highly parallel and distributed query engine built from the ground up at Facebook for efficient, low-latency analytics. Iceberg is an open source, high performant table storage format that enables an engine like Trino to perform data warehousing SQL functionality such as UPDATE, DELETE, and MERGE commands on the data lake house. In addition, Matt and Tom will lead you through combing these technologies to perform near real-time analytics with streaming ingestion with database functionality on the lakehouse. This workshop will use the Starburst Galaxy SaaS product making it simple to leverage these technologies for your modern data lake house without having to worry about the operational aspects of running Trino and other software.

Building an Open Data Lake House Using Trino and Apache Iceberg

Embedded analytics has become a vital aspect of business intelligence, enabling organizations to gain insights from their data at the point of decision, in their operational applications. 

This webinar will explore the architecture and experience of embedded analytics, showcasing how it can be effectively implemented to drive informed decision-making and enhance user experiences. Join us to learn about the various components, best practices, and real-world applications of embedded analytics in modern data architectures. With this webinar, you will:

- Understand the role of embedded analytics in the context of decision intelligence.
- Gain insights into the architecture of embedded analytics, including its components, scalability, performance and security.
- Explore best practices for designing effective embedded experiences.
- Discover real-world applications and use cases of embedded analytics across various industries.
- Stay informed on future trends and opportunities in the embedded analytics space including natural language.

Embedded Analytics: Architecture and Experience

Data Architecture Best Practices

As businesses strive to make data-driven decisions, choosing the right data platform has become a critical component in today's data-driven world. However, with a plethora of options available, it can be overwhelming to identify the best fit for your business. This webinar offers a comprehensive guide to the most popular data platforms, helping businesses evaluate critical factors such as data processing speed, scalability, cost-effectiveness, security, and integration capabilities to determine the ideal platform. 

Participants will gain insight into the advantages and limitations of both cloud-based and on-premise data platforms, and how different modern data platforms can support various business needs and objectives, such as data analytics, machine learning, and big data processing. By the end of this webinar, participants will have a clear understanding of which modern data platform is the best fit for their business context and objectives.

Key Takeaways:
1. Understand the strengths and weaknesses of the most popular data platforms available in the market today.
2. Learn how to evaluate crucial factors, such as data processing speed, scalability, cost-effectiveness, security, and integration capabilities while selecting a data platform for your business.
3. Recognize how different data platforms can support diverse business needs and objectives, including data analytics, machine learning, and big data processing.
4. Identify the advantages and challenges of cloud-based and on-premise data platforms and guidance on how to choose between them.

About the speaker:
Gopinath Manimayan is lead architect of digital experience at Moonraft Innovation Labs, he has over 15+ years of experience crafting exemplary technology solutions. You can always count on him for guidance with the best architecture to suit any need, or for learning about the latest technologies in the market.

Choosing the right data platform for your business

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Choosing the right data platform for your business

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