Name: Setting the Budget for the ML Stack for Analytics
Start: 2022-05-10T17:00:00Z
End: 2022-05-10T17:00:49.000Z
Location: BrightTALK
Rating: 0

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

At the core, Machine Learning Operations (MLOps) takes an experimental machine learning model into a production system. MLOps is an emerging practice distinct from traditional DevOps. ML lifecycle involves using patterns from training data, making MLOps workflow sensitive to data changes, volumes, and quality. Additionally, matured MLOps should support monitoring both ML lifecycle activities and production model monitoring.

Key Takeaways:
• Build cloud-native serverless analytics pipeline. 
• Learning different data automation pattern including data lake, lake house and data mesh architecture.
• Automate end-to-end data pipeline with MLOps.

Decoding Data Automation With Cloud-Native Analytics Pipeline and MLOps

The past two years have taught us that "business as usual" is a thing of the past, exposing sharp differences between companies prepared for the digital economy and those that were not. Organizations are looking for ways to leverage their digital assets to best position themselves to succeed through disruption. In this session you will hear stories of different organizations that used business intelligence and analytics to successfully navigate their digital transformation journeys.

The Future of Business Intelligence

Content is more pervasive than ever, and enterprises need to keep pace to effectively manage and leverage it. Whether managing owned in-house content or from outside sources such as social media and YouTube, organizations need a way to manage that content at scale, improve searchability and shareability, and automatically produce insights that help them more efficiently and effectively run their businesses.

In this webinar you will:
 - Uncover the top areas where you’re likely not making best use of your content today
- Learn how AI-powered digital media asset management can automatically produce more insight and workflow automation
- Discover how AI can help identify faces, logos, on-screen text, and more in unstructured media content such as video and audio
- Hear how PROGRESS, a German film archive with more than 20,000 assets, is making its trove of historic and modern films easier to discover, share, and monetize
- Understand how to accomplish all of this and more using an enterprise AI platform approach

Don’t miss this informative event — register today.

Next-Generation Asset Management: How AI Helps Enterprises Activate their Media

Promises, promises… everybody’s swearing that your unstructured data is a goldmine. All you have to do is buy their software, engage their services, hire fancy tech pros and magic will happen. Buy, buy, buy, right? But you’re not so sure.

The real secret to lucrative results is no secret at all. You’ve got to align data and analytics with your everyday business goals and practices. It’s not rocket science, yet you must understand and manage the process.

In this session, you will learn:
• How to spot the easiest opportunities to profit through data analytics. 
• Why technical staff can’t produce the results you want without active management involvement.
• When to integrate unstructured data with predictive analytics to support decision making.

Make Your Data Pay -- What Non-technical Executives Need to Know

The extraction of actionable information from data is a key enabler for optimizing workflows, automating processes, and providing meaningful insights to decision makers. But we live in an unstructured world, and unstructured sources of data can be particularly difficult to parse. In this session, we will share the latest developments in the field, including the modern tools and machine learning techniques employed by insight engines to intelligently extract information from unstructured data. We’ll also address the following questions:

  *   What are the challenges we face in processing unstructured data sources?
  *   Which tools are available to extract insights from this data?
  *   How can we use extracted insights to automate processes and workflows?

We will close the session by providing a real-world example of process automation employed to assist with bid management.

Business Transformation Through Insights-Driven Automation

While traditional approaches to data integration provide much needed aid in efforts to synchronize data across silos, this delivery is just table stakes now. To maximize and deliver scalable value of data investments, organizations should employ a semantics-based approach. Semantic frameworks embed human understanding of concepts and relationships with your data assets, helping your systems grow smarter and better equipped to discover rich linkages and reveal non-obvious relationships. Through semantic frameworks, business and technical users can have access to a semantically enriched, highly linked and available store of firm data, upon which they can perform smart data analysis and leverage a reusable framework for deploying machine learning models. 

In addition to explaining and making the case for a semantic integration framework, Reclassify AI will cover key use cases and the successful deployment of semantic data integration for AI-enabled decision-making.

The Future of Decision-Making: Human-Powered AI

Organizations looking to maximize business intelligence (BI) use may consider constructing a business intelligence team consisting of four key roles -- the expert, designer, analyst and steward. In this session, we'll detail these four important roles -- how to hire them, how to inspire them and how to manage their work.

Attendees will learn:

• How to integrate existing informal skills and practices into a new practice.
• The steps to build BI team from scratch.
• Inspiring and retaining key roles
• Pitfalls -- personal and technical -- to watch for .
• Expanding new capabilities such as data science and AI

Keys to building a successful business intelligence team

Business Intelligence as a practice has rapidly evolved since the pandemic - the expectations in 2022 are that Analytics are not only accessible, but also actionable in real time.  Data Automation capabilities in Customer Data Platform solutions closely align with BI organizations, both disciplines share common goals and benefits, and, if integrated correctly, can lead to new Revenue streams that add 5-15% to ROAS due to Personalized Customer Experiences.  

Setting them up as a Data Center of Excellence enables the CDP to produce significant operational ROI due to real time automation and uniform process governance.

Join this session to learn:

• What a Data Center of Excellence (DCoE) is
• How incorporating BI and a CDP into your DCoE can increase revenue and operational excellence 
• Business practices and topics in scope for optimization by a DCoE

The Perfect Marriage: BI and CDPs Create a Data Center of Excellence

With a gigantic increase in data capturing capacity, corporations, government agencies, and research organizations face complexity in determining the best variables for their data models. Feature engineering techniques like exploratory data analysis are valuable for establishing the right variables for different statistical and machine learning models. In this session, attendees will learn the basic steps in R programming (and comparable techniques for Python) with use cases to apply these techniques in a sample training data model.

Topics will be based on the following: R programming, R Studio, Python, Machine Learning, Data Science , APIs, Feature Selection/Feature Engineering, Exploratory Data Analysis, and Dimensionality Reduction.

This session is meant for developers and managers using R or Python with an interest in statistical models or machine learning. The session will cover a few basic data type concepts. Prior experience with R or Python is not necessary.

Do More With Data We Have: Engineering Strategies To Match Good Data to Products

The amount of global data creation and replication will grow at a compound annual rate of 23% for the next three years, according to research firm IDC. That makes managing enterprise data more critical than ever, and business intelligence is playing an important role. As adoption of BI increases across enterprises, businesses are looking to advanced capabilities to address challenges with sophisticated use cases and allow all levels of stakeholders to perform tasks that previously required  the skills of a data scientist. 

In this panel discussion, experts in the field will explore the features that will help organizations move toward a data-driven culture. These include augmented analytics, embedded analytics, self-service and interactive enablement, mobile application delivery, and natural language querying.

Join us to find out:
--Which BI features can best support your business model and goals
--Augmented analytics capabilities that give business users power to build their own queries
--Guided analysis that steers users through the appropriate workflow
--How self-service and interactive tools can improve productivity and expand data comprehension and usage

Moderated by:
Craig Stedman, Industry Editor, TechTarget

Panelists:
Ryan Ries, Practice Lead, Mission
Donald Farmer, Principal, TreeHive Strategy
Pierre DeBois, Founder and CEO, Zimana

Advanced BI Features Help Democratize Data

While there are a lot of discussions on what defines good analytics and reliable insights, how do we recognize bad analytics? Bad analytics involves more than lacking good insights. Unless companies can actively identify bad analytics, they cannot prevent them. In this presentation, Dr. Prashanth Southekal, Data Analytics Consultant, Author and Professor, and inventor f the DEAR model for data-driven decision-making, discusses the 10 key characteristics of bad analytics. 

Here are the key takeaways for the audience upon attending my presentation.
1. Understand good and bad analytics.
2. Spot for patterns for bad analytics based on 10 key characteristics.
3. Improve the adoption of data and analytics solutions in the organization.

Bad Analytics: What They Are and How To Avoid Them

As data is increasingly critical in order to draw insights and expand business potential, companies must be able to process this data faster, more accurately and in time to monetize on critical business advantages. That's when automation of data processing techniques become essential. 

Data security is another critical aspect to consider as companies increasingly handle more customer data. In this chat, we will look at certain data automation and security principles that an organization should follow in order to best ensure the trust of their customers.

Ensure customer trust with data automation and security best practices

Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They also need a platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. Above all, they need a worry-less experience with the architecture and its components.

A complete machine learning infrastructure cost for the first modern use case at a midsize to large enterprise will be anywhere from $2M to $14M. Get this data point as you take the next steps on your journey.

Setting the Budget for the ML Stack for Analytics

Machine Learning

Enterprise Datacenter

Enterprise Cloud

Enterprise Architecture

Data Management

Data Analysis

Data Analytics

Data Architecture

Analytics

Enterprise applications have become a crucial piece of infrastructure for many businesses. The enterprise applications community on BrightTALK features the latest insights in enterprise application integration, enterprise application architecture and EAS software. Join the community to gain access to thousands of videos and webinars presented by recognized enterprise information systems experts and arm yourself with the knowledge you need to succeed.

Enterprise Applications

Practicing business intelligence allows your company to transform raw data into sets of insights for targeted business growth. The business intelligence and analytics community on BrightTALK is made up of thousands of data scientists, database administrators, business analysts and other data professionals. Find relevant webinars and videos on business analytics, business intelligence, data analysis and more presented by recognized thought leaders. Join the conversation by participating in live webinars and round table discussions.

Business Intelligence and Analytics

Welcome to the big data and data management community on BrightTALK. Join thousands of data quality engineers, data scientists, database administrators and other professionals to find more information about the hottest topics affecting your data. Subscribe now to learn about efficiently storing, optimizing a complex infrastructure, developing governing policies, ensuring data quality and analyzing data to make better informed decisions. Join the conversation by watching live and on-demand webinars and take the opportunity to interact with top experts and thought leaders in the field.

Big Data and Data Management

As an IT professional, many of the problems you face are multifaceted, complex and don’t lend themselves to simple solutions. The information technology community features useful and free information technology resources. Join to browse thousands of videos and webinars on ITIL best practices, IT security strategy and more presented by leading CTOs, CIOs and other technology experts.

Setting the Budget for the ML Stack for Analytics

Presented by

About this talk

Business Intelligence and Analytics