Can enterprises tame the dragon? Maybe. They need to govern the full AI/ML lifecycle—data and feature engineering, model development, and model operations—with special attention to the operations stage. They need to carefully monitor, control, and reuse models during the entire lifecycle.
Eckerson Group analysts study this problem. Our consultants live it. Join both for a free-wheeling discussion of the tradeoffs and pitfalls of AI/ML governance.
You will hear:
• What AI/ML governance means, and why enterprises need it
• How governance applies to key stages of the AI/ML lifecycle
• Guiding principles, best practices, and tradeoffs of AI/ML governance
• Case studies and what they teach us
• How AI/ML governance is likely to evolve
This webinar will explore recent trends among business intelligence platforms—namely the rise of data storytelling features and a shift toward the cloud.
Gathering and analyzing data isn’t enough to make your organization data-driven. Successful business intelligence (BI) initiatives depend on getting people to actually use your BI platform. Unfortunately, as long as there’s been BI, companies have struggled with adoption. Modern BI vendors have begun to combat this challenge by embedding data storytelling features directly into their platforms. A new, storytelling-centric approach to BI promises to make it easier to communicate and consume insights, helping more people derive value from data.
You will learn:
• Trends in modern business intelligence tools
• How to sync your BI strategy with your cloud strategy
• The importance of embedded data story-telling
Joe Hilleary - Research Analyst, Eckerson Group
JP Serhal - Product Marketing Manager, Toucan Toco
Sponsor: Toucan Toco
Many large and mid-sized enterprises standardize their analytics projects on Microsoft SQL Server. To meet modern business requirements, they now need to integrate data from myriad operational platforms into consolidated Azure cloud platforms to support data warehouse and data lake use cases. Their data engineers must manage pipelines that ingest and transform these high volumes of multi-sourced data, automating where possible and customizing where necessary.
This webinar defines a framework for data integration tools that address these requirements, then examines their adoption drivers, challenges, benefits, and use cases. It offers guiding principles and product evaluation criteria to simplify the data integration process, improve performance and scale, and modernize your analytics architecture.
Join Eckerson Group and TimeXtender to learn:
- What data integration means for Hybrid Azure environments
- Why enterprise data teams need modern data integration tools
- What benefits they can achieve, and what use cases they can support
- How data engineers and architects can meet modern requirements
- How to evaluate data integration tools for your team
Kevin Petrie - VP of Research, Eckerson Group
Joseph Treadwell - Solution Specialist Director, TimeXtender
Malware, theft, espionage, and other cybersecurity threats are the bane of digital business. Bad guys get better each year at stealing money, customer identities, and secrets. To reduce their risks, enterprises need to sharpen their surveillance.
Security log analytics can help. It enables security operations (SecOps) teams to improve the cost and scale at which they predict, prevent, and mitigate rising threats. These tools study events such as user logins, password changes, and firewall alerts to identify, assess, and respond to security threats. New low-footprint indexing tools make security log analytics more scalable and flexible for cost-conscious enterprises.
Join this webinar to learn:
- Adoption drivers, challenges, requirements, and benefits of security log analytics
- Primary use cases for security log analytics
- Why security log analytics offers a scalable and cost-effective alternative to security information and event management (SIEM) tools
Kevin Petrie - VP of Research, Eckerson Group
Thomas Hazel - Founder, CTO, and Chief Scientist, ChaosSearch
Augmented analytics uses artificial intelligence (AI) and machine learning (ML) to make business intelligence (BI) and analytics tools easier to use and generate insights not possible with earlier generations of products. However, this doesn't mean all business users will universally adopt the new features. Analytics leaders need to understand the target audience for these features before rolling them out broadly.
This webinar explains what augmented analytics is and shows how data and analytics leaders can increase adoption of these new capabilities. It presents an Analytics Adoption Framework that describes the major factors that contribute to widespread adoption of BI and analytics tools and features. It features a case study of Roche, an SAP customer that successfully implemented SAP Analytics Cloud to support predictive planning.
- Understand the range of augmented analytics, from natural language search and assisted insights to predictive planning.
- Drive adoption of augmented analytics by knowing the target audience and the features best suited to it.
- Learn the major objections business people have to BI and analytics tools in general and augmented analytics features more specifically.
- Leverage the Analytics Adoption Framework to identify potential objections to new technology rollouts.
- Listen to one company’s implementation of predictive planning using SAP Analytics Cloud.
Wayne Eckerson - President, Eckerson Group
Orla Cullen - Solution Manager Augmented BI, SAP
Irina Lichkova - Solution Architect, Roche