Name: Data Lineage & Knowledge Graphs: Similarities and Differences
Start: 2023-04-19T12:00:00Z
End: 2023-04-19T12:00:30.000Z
Location: BrightTALK
Rating: 4.8

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.

Enterprise Strategy Group Practice Director Christophe Bertrand Presents: 

AI is a topic frequently discussed in various contexts, primarily regarding its impact on society and its application in specific scenarios. When it comes to backup and recovery, two questions are significant: How much AI-generated data needs protection? How will backup and recovery processes adapt to take advantage of these rapidly growing technologies?

Reinventing Data Protection with AI and ML

Enterprise Strategy Group Principal Analyst Mike Leone Presents: 

The meteoric rise of generative AI has sparked a surge of AI challenges that organizations must overcome to ensure greater levels of trust in the underlying technology and its outputs. Recent Enterprise Strategy Research highlights that while 78% of people are using generative AI for personal use like boosting productivity or improving efficiency, just 36% of organizations have policies in place to limit usage. The lack of organizational action to minimize issues like potential biases, legal liability, unknowingly sharing intellectual property, and more is leading organizations down a path of potential disaster. Uncover the potential shortcomings and challenges organizations face, as well as issues already encountered due to a lack of action being taken by business teams.

Evaluating the Pillars of Responsible AI

Enterprise Strategy Group Principal Analyst Dave Gruber Presents: 

Security professionals claim that increasing security operations difficulty is driven by the sophisticated and evolving threat landscape, a growing attack surface, and a rising volume of security alerts. Trained large language models (LLMs) and generative AI have the potential to help cybersecurity teams analyze and prioritize decisions in each of these areas, mitigating cyber-risk. Uncover how organizations are using AI technologies today and whether these technologies have met expectations.

Securing GenAI Within Your Organization, While Gaining Leverage Using It

Enterprise Strategy Group Senior Analyst Jon Brown Presents: 

Generative AI is rapidly transforming technology and business strategies. According to recent Enterprise Strategy Group research, 85% of organizations are actively using, planning to use, or considering generative AI across many of their functional areas, including IT operations. This session will explore current use cases for generative AI in IT operations, how AI is impacting the selection and use of IT operations tools, customer sentiment about desired outcomes and expected challenges related to AI adoption, key stakeholders influencing purchasing decisions, functional requirements, business and technology drivers, tool consolidation plans, and more.

The Impact of GenAI in IT Operations

In the dynamic landscape of modern business, strategic decisions hinge on foresight and adaptability. Predictive analytics is critical to this, allowing us to build pathways to informed decision-making through data-driven insights.  

GenAI introduces a paradigm shift, augmenting traditional predictive models with advanced techniques such as deep learning and natural language processing. This integration not only enhances predictive accuracy but also uncovers hidden insights, accelerating decision-making processes and shaping the future of business intelligence. 

However, the promise lies does not come without challenges. Ethical considerations and technical complexities abound as organizations navigate the integration of GenAI into predictive analytics workflows.  

In this session, Efe Ogolo (Director of DS & Analytics at Sago Mini) discusses the transformative potential of GenAI-powered predictive analytics and shares strategies for overcoming challenges to harness its full potential. 

Topics for discussion include: 
- Understand the significance of predictive analytics in modern business strategies. 
- Explore the transformative potential of integrating generative AI into predictive analytics workflows. 
- Identify the challenges associated with implementing and scaling GenAI-enhanced predictive analytics. 
- Strategies for mitigating ethical concerns and algorithmic biases in AI-driven predictive modeling. 
- Recognize the importance of interdisciplinary collaboration and skill development in maximizing the benefits of GenAI in predictive analytics.

GenAI-Enhanced Predictive Analytics: Unlock Advanced Strategic Insights

In today's rapidly evolving business landscape, organizations are increasingly recognizing the importance of embracing a data-driven culture to drive innovation, enhance decision-making, and achieve digital transformation goals. 

This webinar will delve into the strategies and best practices for fostering a data-driven culture within organizations. Participants will gain insights into the critical role of data in driving organizational success, understand the benefits of cultivating a data-driven mindset across all levels of the organization, and learn practical strategies for promoting data literacy, transparency, and accountability. 

Through real-world examples and case studies, attendees will discover how organizations can leverage data to optimize processes, enhance customer experiences, and gain a competitive edge in the digital age. Whether you are a business leader, data professional, or digital transformation enthusiast, this webinar will provide valuable insights and actionable strategies for unlocking the full potential of data to drive organizational success in today's digital world.

Key takeaways: 
1. Understanding the critical role of data in driving digital transformation initiatives
2. Practical strategies for instilling a data-driven mindset throughout the organization
3. Learning how to empower employees to make data-informed decisions
4. Recognizing the benefits of transparency and accountability in data management
5. Exploring real-world examples of successful data-driven cultures and their impact on organizational success
6. Discovering actionable steps to integrate data-driven practices into everyday workflows

Fostering a Data-Driven Culture for Digital Transformation

AI isn't new, there are many AI experts who have worked in this industry for a number of years. While I am no AI expert, my area of expertise - data governance - positions me as a guardian against the potential pitfalls organizations may encounter as they delve into the realm of AI. Data governance and AI governance are critical bedfellows to safeguard against evolving technology moving organizations into the unknown.

At a time where AI can be both empowering and daunting, understanding is critical to preventing misuse and fostering harmony. Join me as we explore how data governance and AI interact, and receive a roadmap to navigate this thrilling yet complex terrain.

Audience takeaways:
- The role of data governance in shaping the journey of AI initiatives.
- Cultivate a culture of ethical awareness and responsibility in the deployment of AI technologies.
- Strategize for prudent investments in AI by discerning value propositions and mitigating risks.
- Evaluate the quality and integrity of your organization's data as a base for AI systems.
- Align AI practices with your corporate ethos to design and uphold ethical standards.

About the Speaker
Joanne has recently worked in the Insurance industry as a Deputy Head of Data at DUAL UK and Head of Data Governance at Hastings Direct - prior to this Joanne has over 20 years of experience working in the Finance industry. Joanne has run teams whose key remit is to ensure data accuracy, follow legal and regulatory requirements and produce evidence of this compliance. Since specialising in Data Governance Joanne has been able to lean on this experience to help organisations to treat data as an asset. It is this expertise which has led Joanne to be asked for guidance and support to navigate technological advancements such as AI.

Securing the Future: Data Governance in the Age of AI

In today's rapidly evolving technology landscape, organizations recognize that the success of any digital transformation hinges on the human element driving the change. 

This talk navigates the complex interplay of modernization efforts and human behavior and offers actionable insights into leveraging people's power for impactful change. 

Led by a seasoned expert, the session dives into six key pillars of a human-centered digital transformation:
- Crafting a Vision for Value Delivery: Learn the art of creating your North Star of a vision that embraces digital advancements and emphasizes delivering tangible value to customers and stakeholders.
- Fostering Organizational Alignment: Dive into strategies for fostering alignment across all levels of the organization, ensuring that every team member is united in pursuit of the transformational vision.
- Communication Transparency: Discover the power of transparent communication channels in building trust, fostering collaboration, and ensuring clarity of purpose throughout the transformation journey.
- Leveraging Resistance as a Catalyst for Change: Learn how to leverage resistance as a catalyst for driving meaningful change, converting challenges into opportunities for growth and innovation.
- Supporting Your People Through Change: Delve into the importance of providing comprehensive support mechanisms, including training, mentorship, and clear frameworks, to empower individuals in embracing and adapting to change.
- Mastering Accountability: Uncover strategies for cultivating a culture of accountability, where every team member takes ownership of their role in driving the success of the transformational initiatives forward.

Whether you're leading transformation efforts or navigating change as a team member, this talk equips you with the knowledge needed to start unlocking the full potential of your organization in the digital era.

Human-Centered Strategies for Digital Transformation Success

Data has become the source to empower an organization’s productivity and artificial intelligence (AI) is its driving force. Data management and data governance has never been more important for businesses and society due to the new age of technology.  Today, we rely on data to power business performance as data volumes grow as a result of AI adoption. There are benefits to leveraging data to make better strategic decisions to accelerate business growth and be cost effective.  Adding to the challenge between data governance and AI: AI itself is being utilized to support an organization's governance practices and data management.

It is important to note that data governance is different from data management. Data governance is a collection of practices and ideas that focus on organizing data in order to enforce data policies. Data management, on the other hand, focuses on how data is organized and the actions taken to achieve efficiency.  The term has its roots in the 1960s, when it was primarily used to describe professional training programs and quality assurance (QA) metrics, but has evolved.  AI and data management systems go hand in hand; and when AI is integrated into and across the data management systems, it has the capability to enhance database precision and performance, as well as optimize system resources.

This webinar will discuss:
- The essentials of data management and data governance, and where the inclusion of AI brings value in digital transformation.
- History of data management applied in technology for businesses and incorporating AI.
- Data management and data governance tools:  leveraging tools to increase business performance and to assess risk.

Data Management:  How Can Data Governance Help Drive Digital Transformation?

In today's data-driven world, machine learning (ML) has become an essential tool for businesses to gain insights and build innovative products. However, successfully deploying and managing ML models at scale requires a robust and scalable infrastructure. 

This session will explore the key considerations for building and maintaining an ML infrastructure that supports product development and growth. We will discuss topics such as model training and deployment pipelines, data management, monitoring, and scalability. Additionally, we will share best practices for collaboration between product managers and ML engineers to ensure alignment and success.

Build Scalable and Viable Machine Learning Infrastructure for Product Success

This session will explore the integration of AI technologies such as machine learning, natural language processing, and deep learning with cloud-based data management platforms. In particular we will focus on how AI can enable organizations to automate and optimize various data management tasks, from data discovery and integration to governance and analytics.

There are some key technical considerations such data architecture, security and privacy, but we will also consider ethical and organizational issues as well as looking to the future with the latest trends in AutoML, edge analytics, and multi-cloud data management.

Attendees will leave this session with a clear understanding of the potential of AI-driven data management in the cloud, as well as practical insights and strategies for harnessing this exciting technology to drive their own digital transformation.

AI-Driven Data Management in the Cloud

The primary purpose of data management is to organize data in a way that allows end-users to derive value from it. Understanding the significance of data is crucial for informed decision-making and organizational growth. 

In this webcast, we will delve into the concept of value in the data landscape, recognizing it as a dynamic and multifaceted concept essential for business expansion. We will focus on how data storytelling can aid in transforming data into value. Through storytelling, organizations can bridge the gap between complex data and practical decision-making, fostering a deeper understanding of insights and facilitating more informed decisions.

3 key takeaways:
- The objective of data management.
- The concept of value.
- How data storytelling can help to turn data into value.

Using Data Storytelling to Turn Data into Value

In an era where digital transformation is at the forefront of business strategy, the importance of embedding a privacy-centered culture within organizations cannot be overstated. As businesses increasingly rely on data to drive decisions, the ethical management and protection of personal information have moved from being mere regulatory requirements to a cornerstone of customer trust and business resilience. 

The rapid evolution of technology, especially with the advent of generative AI, along with the complexity of the global regulatory landscape, underscores the need for a shift from compliance-based approaches to fostering a culture deeply rooted in privacy by design. 

In this session, Jo Ann Davaris (Chief Privacy Officer, Global Privacy at Booking Holdings) and Anne Fealey (Chief Privacy Officer at Citibank) discuss why developing a pro-privacy mindset is not just beneficial but essential for modern businesses to thrive and sustain in today's data-driven environment.  

Topics for discussion include: 
- Understanding the current data privacy regulatory landscape and the unique challenges faced by different sectors, particularly tech and financial services. 
- How to navigate the shift from compliance to culture. 
- The importance of accuracy and accessibility in building robust data privacy programs that enhance data resilience. 
- Best practices, tools and metrics for measuring the success of privacy initiatives and the impact of a privacy-first culture on organizational success.

From Compliance to Culture: Embedding a Pro-Privacy Mindset

In the transition to a green economy, regulators, consumers and investors alike are keen to ensure that their money is going towards organisations with low ESG risk. In turn, it's never been more important for businesses to assess and monitor their own ESG performance.  

As operations become increasingly digitised, businesses have more intelligence than ever, providing them with a clearer view of efficiencies and challenges. By collecting valid and relevant data based around international indicators and the company’s self-assessment, businesses can harness data analytics to build a comprehensive ESG risk-assessment that monitors ESG tasks in real-time.  

The challenge is in analysing and interpreting the raw data in a way that makes it easier for business managers, CFOs and ESG officers to understand what the potential ESG risk factors are and what actions can be taken to overcome them. In this session, Compliance and Integrity Officer and Consultant Szilvia Andriasik she shares her insights on the power of big data and analytics to optimise ESG outcomes.  

Topics for discussion include: 
- Best practices for ESG risk analyses.
- Combine international standards with internal data to conduct comprehensive ESG analysis.
- Leverage business intelligence and tools like Power BI to spot ESG red flags. 
- And more...

Harness Big Data and Business Intelligence to Assess ESG Risk

Enterprise Strategy Group Distinguished Analyst Jon Oltsik Presents: 

From skills shortages to nuanced cyber-attacks, cybersecurity leaders have well-documented challenges that threaten the successful protection of their business. Generative artificial intelligence (GenAI) has burst onto the scene promising a long list of potential applications, with cybersecurity improvement touted among them. This session will explore cybersecurity use cases, including those employed by Security Operations Analyst for threat detection and response purposes.

Cybersecurity GenAI Use Cases

Enterprise Strategy Group Principal Analyst Mike Leone Presents: 

Application developers are challenged with efficiently creating innovative solutions while managing time constraints, which can be mitigated by the transformative impacts of generative artificial intelligence (AI) streamlining code generation and accelerating development processes. Organizations have integrated generative AI (GenAI) into their operational setup to accelerate code creation, refine code structures, elevate code quality, and deliver personalized customer experiences. By harnessing GenAI, application developers tackle issues by capitalizing on the technology’s ability to automate tasks, drive creativity, and deliver innovative solutions.

The Role of GenAI in Application Development

Enterprise Strategy Group Senior Analyst Jonathan Brown Presents: 

A common criticism of AI is its high potential to increase carbon footprints due to its heavy compute requirements. As a result, sustainability has risen to become a critical consideration for nearly all organizations. This session will explore how organizes balance deploying AI-ready infrastructure with well-defined sustainability requirements and the metrics designed to monitor energy-intensive AI environments.

Deploying Sustainable AI Infrastructure

Enterprise Strategy Group Principal Analyst Mike Leone Presents:

The larger and more accurate the data set, the more effective the AI model. However, the road leading to that seemingly simple goal is strewn with roadblocks that pose substantial challenges for many organizations. Data experts and data quality tools and techniques to ensure the quality and accuracy of collected data while also assuring data is compliant with industry regulations is critical. This session will explore these considerations as well as how moving huge data sets to AI is also subject to service level agreements.

AI Data Quality, Compliance, and Infrastructure Requirements

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

Anti-patterns of data architectures

Accelerating Time to Value with Snowflake

Data Platform Capabilities for Modern Data Management Architectures

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

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

Choosing the right data platform for your business

FAIR data: Superior data visibility and reuse without warehousing

Building an Open Data Lake House Using Trino and Apache Iceberg

Embedded Analytics: Architecture and Experience

Data Architecture Best Practices

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

Data Management

data knowledge

data intelligence

knowledge graphs

data lineage

data maturity

Data Analysis

Data Analytics

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Data Lineage & Knowledge Graphs: Similarities and Differences

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