Name: AI Drives Measurable Success in Customer Engagement
Start: 2019-08-21T12:00:00Z
End: 2019-08-21T12:59:49.000Z
Location: BrightTALK
Rating: 4.75

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.

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

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 latest mission critical complex IT environment for enterprises is their data architecture. This session will cover how AIOps concepts are being applied to today's complex modern data architectures. How is AIOps being embedded in Data Observability platforms today?  How are those platforms helping DataOps teams eliminate operational blind spots while increasing proactive optimization and cost intelligence around cloud based architectures? How do business leader close the data talent gap  using these platform and DataOps?

About the speakers:
Bill Lloyd has over 25 years of experience in design, development and management of data and analytic programs. As a Managing Director in the AI & Data Operations practice, Bill leads our Delivery Transformation and Quality, and has served as overall program lead on dozens of critical data and analytics programs for global Fortune 100 organizations across industries.  His responsibilities include developing our end-to-end strategy as it relates to bringing our Deloitte solutions to the market as a managed service, and leveraging AI and intelligent automation to bring greater value to our clients.

Greg Lato has over 25 years’ experience partnering with enterprises clients to address their business challenges.  With the last 8 years focused on data, he has seen the explosive growth of using data to drive business outcomes and the corresponding increase in complexity of the IT Systems that are required to process that data.  At Acceldata he focuses on driving efficiency and automation across this complex environment for the enterprise data operations team using Data Observability.

Using AIOps to scale and control costs of enterprise data

What is AIOps and why should we use it?  When is it appropriate to use AIOps and how do we implement it? AIOps may be viewed as the application of machine learning analytics- platforms that utilize big data, modern machine learning and other advanced analytics technologies to enhance IT operations. These platforms allow concurrent use of multiple data from varied data sources, providing proactive, personal and dynamic insight. Using and implementing AIOps requires an understanding of the fundamental essence of what AIOps can do in real time. 

This presentation covers the core components of AIOps, specific use cases of when to use it, and how you can implement AIOps.  

The first section covers key components of AIOps, the “what” question.  It includes:  
1. Engaging-historical analysis, anamoly detection, performance analysis, predictive insights, correlation and contextualization
2. Automating-automated root cause analysis, optimization suggestions, and task automation
3. Monitoring-historical data, real-time data, and decision-making insights 

The second section covers “why” and “when” to deploy AIOps. 

AIOps use cases presented in this session cover three core outcomes intrinsic to IT operations:
1. Leveraging AIOps capabilities to detect, diagnose, and resolve incidents faster with a clear outcome to decrease mean time to recovery (MTTR)
2. Proactive performance monitoring, preventing problems with actionable alerts that identify anamolies with a clear outcome to improve operational efficiencies and overall health of the business.
3. Surface insights to drive better and faster decision making, using machine learning technologies.

What is AIOps: Why, When to use and implement AIOps

An unspoken requirement has emerged calling for IT staff to conduct data management in a non-intrusive manner. Skipping risk mitigation to avoid interfering with employee productivity is a trade-off that will hurt an organization. 

Automating and orchestrating data protection activities is critical for resilient and sustainable operations. Companies must be in a good position to "dust themselves off" as quickly as possible after an outage like a corrupt database, data pattern change due to new normal, missing data or malfunctioning sensor, and become operational with minimal disruption of service.

Tune into this talk, where Dr. Pragyansmita Nayak of Hitachi Vantara Federal will focus on five best practices to better manage your data protection strategy:
- Automation of data characterization like summary statistics and metadata management.
- Explore knowledge graphs to determine strengths and gaps in the business glossary.
- Use of lineage to understand the changing nature of data from origin through transformations.
- Track the data shift and its patterns that impact reports, dashboards, ML models and more.
- Review metrics that minimize false alarms and timely trigger deeper analysis.

Managing data protection: If you can't measure it, you can't manage it

Data quality is a difficult concept to quantify while still being vital enough to sabotage any project, strategic initiative, or even an entire business. What's going on with the quality of your data? Is the data well-suited for all of its uses? Is your data everywhere  – in operational databases, data warehouses, data lakes, master data management, etc.?
 
Information management expert William McKnight will go over 23 lessons he’s learned about data quality, frequently the hard way, in this presentation. Any problem with data quality, and quite possibly with data, that you may be experiencing is likely the result of someone failing to take one of these lessons to heart. Focusing on one or more of these could be the key to achieving understanding between parties and, ultimately, finding a solution to your data quality, and consequently data, problems. 
 
In this presentation, you’ll discover up to 23 facts about data quality as well as fixes for the issues that the absence of data quality causes.

23 things I’ve learned about data quality in 23 years of consulting by 2023

Fit-for-purpose data is the key driver of positive business outcomes. The path forward focuses on transforming an organization into a data-driven organization by culturally treating data as a strategic asset managed through a holistic and trusted data program. 

The overall complexity of large organizations is naturally reflected in the complexity of their data landscapes. A holistic and trusted data program, implemented at scale, is the right (and only) solution to leverage full data potential and provide fit-for-purpose data to cross-business and cross-functional data consumers.

Join Bojan Ciric from Deloitte Consulting to dig into an FSI example focused on enabling a holistic and trusted data program. Furthermore, this use-case will include common success factors and challenges that organizations face on the transformative journey to data-driven organizations. Lastly, you will see how organizations can leverage concepts like data product, data contract, data culture and data literacy within ongoing data program modernization efforts to align with emerging data architecture approaches (data mesh, data fabric). 

About the speaker:
Bojan is a Specialist Leader with Deloitte LLP, AI & Data Engineering practice. He helps clients define and execute data management and analytics strategies to drive positive business outcomes. Bojan has helped enable data and analytics organizations and data governance structures in several global financial services institutions. He has a record of delivering large transformation programs in cross-functional, cross-business roles in data management, data governance, metadata management, ontologies, data quality,  IBM Banking, Financial Market Industry Models, FIBO, data architecture, and modeling. Lastly, he serves as a trusted advisor to the Chief Data Officer agenda within Deloitte.

Enabling holistic data programs to drive positive business outcomes

Improving data management demands strong data governance practices. Occasionally, data governance and data management are used interchangeably, in multiple industry reference guides, sources, and in different contexts. 

This webinar focuses on critical components of improving the management of data from a data governance point of view, detailing meaning and relationships with each other. 

Key topics that will be covered include:
1. Foundational building blocks of data governance and data management.
2. Content of building blocks.
3. The role of IT in data management and data governance. 
4. Conducting data risk analysis.
5. How to build an operational roadmap. 
6. Data governance metrics. 
7. Embedding continuous quality improvement.

Improving data management from a data governance lens

Are you getting started with data governance? Whether you’re about to commission a new initiative or you’ve been tasked with running one, you’re probably feeling overwhelmed. You may have lots of questions like:

- Where do I start?
- How will I know if I’m doing the right things, at the right time?
- What can I expect to get in place in the next 6 months? What will be longer term?
This webinar will answer your initial questions about data governance and share six principles that underpin all successful data governance initiatives.

You’ll learn: 

o What to do from the start so you can avoid mistakes which take time and money to resolve
o The opportunities data governance will bring to your organization so know why you are doing this and what the wins will be
o How to approach your data governance initiative in the right way, so you have a realistic idea of what is involved

About the speaker:
Known as The Data Governance Coach, Nicola helps organizations understand and manage their data better.
For almost two decades she’s helped corporates to reduce costs, inefficiencies and to remain competitive. Typically, people turn to her because their data is a mess, and they need help unravelling it.

As well as providing coaching and consulting to help her clients better manage their data, Nicola runs three popular training courses every year, because she feels it is important to give people the skills to make sure that data is used to solve problems and make better informed decisions. 

Nicola initially worked for a leading UK Bank and moved into consultancy at the beginning of 2009. 
Nicola is a Director and Committee Member of DAMA UK, she sits on the Expert Panel of Dataqualitypro.com, and regularly writes and presents internationally on data governance best practice.

The 6 principles for successful data governance

What is data governance and why is it still a fundamental prerequisite for business success? Presented by Rajeev Pai, Director of Technology Strategy & Transformation at Deloitte, this session will explore what data governance is and the key reasons why organizations should have a data governance program in place. 

Attendees will learn:
- Key principles of data governance.
- Value delivered due to effective data governance.
- Key trends shaping up in the space of data governance.
 
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 an AWS Certified Solutions Architect experienced in large scale System Integration, Platform Re-engineering, 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 certified in Business Sustainability Management from the Cambridge Institute of Sustainability Leadership (CISL). 

Rajeev has nurtured trusted relationships and high performing teams throughout his career and is committed to help business leaders succeed in their stated mission by combining his industry and digital transformation experience. He is passionate about solving enterprise architecture, sustainability & data challenges for clients.

Take charge of your data: Enabling it for a profitable and sustainable business

Operational and budget planning can quickly become a jumble of numbers as organizations attempt to finance next quarter’s operations. Business Intelligence (BI) software can speed out the process, automating tedious tasks such as compiling past performance data and lay out the metrics in front of you.

Adaptable budgets are essential in today’s increasingly fast-paced environment, organizations need the ability to update forecasts and prepare for unexpected scenarios. Join Streamline Budget Planning with Business Intelligence with Host Gowri Selka and guests as they delve into best BI  practices for agile, data-driven budget planning.
 
Panelists will discuss: 
- How can operational business intelligence support financial analytics and budget planning
- How HR can utilize data analytics to better understand today’s employee needs and demands 
- Effective strategies organizations are utilizing to prepare for the Future of Work

Streamline Budget Planning with Business Intelligence

AI/ML-driven data applications are often considered as the final data application -- the target of all data preparation and metadata management activities performed upfront. However, AI/ML can also effectively aid the data aggregation and categorization for search, analytics, and discovery. Data tagging and metadata management can be automated and thereby, enhance the overall data experience for all entities involved in the data process – business or technical. In this session, we'll cover certain examples of AI/ML in data engineering.

About the Speaker
Dr. Pragyansmita Nayak is the Chief Data Scientist at Hitachi Vantara Federal (HVF). She explores the "Art to the Science" engaging with several challenging Data Engineering and Data Science projects in the US Federal Government domain. She has over 22+ years of experience in software development and data science. She holds a Ph.D. in Computational Sciences and Informatics from GMU (Fairfax, VA. She has presented as part of various BrightTalk and other similar technical events in the past. She lives in DC Metro with her husband and enjoys cooking, travel, and photography. 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.

AI and Machine Learning in Data Engineering

Enterprises increasingly deploy machine learning models to analyze corporate data that informs vital business issues. Identifying and gathering high quality data to develop these models is hugely challenging, however, as some information is protected by regulatory or proprietary reasons. Quality data collection is also expensive and time-consuming, leaving huge gaps in AI and machine learning model development. The development of synthetic data and explainable AI is helping fill these gaps by helping train machine learning models or develop new algorithms with insights that are readily available for data scientists.

In this fireside chat with machine learning scientist Vincent Granville, he'll discuss:
o Synthetic data design techniques, and how to identify business processes where most useful;
o How to test the quality of synthetic data;
o The benefits, and potential detriments, of explainable AI;
o Common modern enterprise data issues, such as managing unbalanced, inconsistent, small, outdated, and unstructured data;
o Ways to address data leakage, as well as small, wide and unobserved data;
o How synthetic data, explainable AI and other modern technologies are used to overcome these issues.

The Business Analysis Benefits -- and Limitation -- Of AI and Synthetic Data

Tune into this webinar to hear University of Washington's Deveeshree Nayak discuss the role of AI in cyber security and the how AI can be applied to address present cyber attacks.

Key Takeaways:
- How AI can be used in cybersecurity;
- Does cyber security have components of AI?
- Examples of cybersecurity in AI.

The Power of AI Cybersecurity

Organizations are relying on increasing amounts of data to drive decisions; Gartner's "5 Key Actions for IT Leaders for Effective Decision Making" predicts that by 2025, graph technologies will be used in 80% of data and analytics to better understand their data through visual representation.
 
By combining data from multiple datasets and providing interactive visualizations, organizations can easily identify patterns and grasp concepts. Visual analytics allows organizations to interact with data visually and find hidden relationships and patterns. Join Understanding Data Through Visual Analytics with host Gowri Selka and guests to discover how visual analytics can better your organization, and what techniques and goals to set your organization for success.
 
Join us to learn more about:
- How to utilize visual analytics to learn new data patterns and valuable business insights
- Selecting the right visual analytics tool to address your business needs
- Best practices to set your organization for visual analytic success and how to avoid visualization pitfalls

Understanding Data Through Visual Analytics

Tune into this webinar to hear AI security issues and the best approaches to apply while using AI-powered products in daily life. 

Key Takeaways:
- Security challenges in AI;
- Best practices to use AI products;
- Best practices to implement security practices.

Cyber Security Issues and Challenges in AI

An AI Experience Playbook for Enterprise IT

AI and Machine Learning in Action

AI & Machine Learning in Real Life

Leveraging Machine Learning and AI in R&D, Engineering and Science

How AI and Machine Learning Unlock Unstructured data for Enterprise Automation

Using a Machine Learning Data Catalog to Reboot Data Governance

Keep your AI under Control - Governance of AI

The Ethics of Machine Learning and Artificial Intelligence

Unlocking the AI-Enhanced Enterprise with Automated Analytics

Data & Analytics Technology Trends in AI

The machine says….I don't know where I got that number from

Best data practices for implementing Artificial Intelligence & Machine Learning

How AI Can Be Used By Almost Anyone For Powerful Analytics

Lessons Learned Applying Natural Language Processing for AI in Healthcare

Enhancing Analytics with Machine Learning: How AI is Changing the Knowledge Game

Machine Learning and AI in Analytics

Intelligent Customer Engagement Series [Ep.1] AI Drives Measurable Success in Customer Engagement

Nearly 50% more companies are using or planning to use AI in their customer engagement initiatives. 
Nemertes recently studied how 518 companies are using AI and analytics to improve their customer experiences. This webinar details: 
• how these companies use AI 
• what measurable improvements resulted.

Join BrightTALK's LinkedIn Group for BI & Analytics
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AI Drives Measurable Success in Customer Engagement

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AI Drives Measurable Success in Customer Engagement

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