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How do you unlock siloed data across and within international geographies to enable tangible business value and promote a data-centric culture?

Hear this journey of walking a tightrope of technological development, immediate value creation and compliance to empower business users to make data-informed decisions. The role of data domains, functional engineering, ML/AI & automation in driving innovation, adoption and efficiencies are explored as well as broader cultural impacts that fostered a data-centric mindset across the organisations.

How do GDPR and the EU AI Act impact creative data use? The author of 'How to Use Customer Data' and Acxiom’s European Privacy Officer shares her insight into best data governance practices for building great customer relationships using a CDP to create an effective data-driven business.

The topics will include:

Embracing the 'personalization paradox' and its benefits for marketing data scientists.

Devising GDPR-powered data use strategies to empower both customers and brands along the way.

Tips for data scientists to kick start with AI governance

As Generative AI continues to revolutionize industries, having high-quality, well-prepared data has never been more crucial. In this session, Emma McGrattan, SVP of Engineering & Product at Actian, and Guillaume Bodet, CPTO at Zeenea, will explore how Zeenea's cutting-edge Data Discovery Platform, now part of Actian, is poised to play a pivotal role in achieving data readiness for GenAI. Attendees will discover how Zeenea’s metadata management solutions, including its comprehensive data catalog, lineage insights, quality index, business glossary, and data marketplace, empower organizations to truly know and trust their data. Join us to learn how to leverage these tools to mitigate risks, ensure compliance, and confidently unlock the full potential of GenAI in your organization. Don’t miss this opportunity to prepare your data for the next wave of AI innovation! Speaker Bios: Emma McGrattan, SVP of Engineering & Product, Actian Emma is SVP of Engineering and Product at Actian leading global research and development. She is a recognized authority in data management and analytics technologies and holds multiple patents. Emma has over two decades of experience leading a global software development organization focused on innovation in high-performance analytics, data management, integration, and application development technologies. Prior to joining Actian, Emma was Vice President for Ingres at Computer Associates. Educated in Ireland, Emma holds a Bachelor of Electrical Engineering degree from Dublin City University.

The success of any AI strategy hinges on the quality, accessibility, and relevance of the data that powers it. Data products play a crucial role in this context by transforming raw data into valuable, trusted, and purpose built data assets that fuel AI-driven innovation and decision-making.

By integrating data products into our AI initiatives, we can:

- Accelerate AI Development

- Enhance Decision-Making

- Foster Innovation

- Ensure Data Quality

Join us to learn how Starburst Data Products are feeding data hungry AI strategies across the enterprise to improve productivity, unlock new opportunities, drive competitive advantage, and lead in the era of intelligent business.

As organisations shift from generative AI proof of concepts to building production ready applications, the requirements for efficiency, monitoring, safety and governance become critical to both trust and success.

You will learn:

Key design patterns and methodology for evaluating, experimenting and monitoring enterprise gen AI apps to address common failure modes

The role of iteration and improvement as part of ongoing delivery

Practical considerations for implementation using examples from Snowflake’s Cortex Analyst, Cortex Search and TruLens, an open source project.

In this short presentation, Big Data LDN Conference Chairman and Europe’s leading IT Industry Analyst in Data Management and Analytics, Mike Ferguson, will welcome everyone to Big Data LDN 2024. He will also summarise where companies are in data, analytics and AI in 2024, what the key challenges and trends are, how are these trends impacting on how companies build a data-driven enterprise and where you can find out more about these at the show.

In today’s episode, I’m going to perhaps work myself out of some consulting engagements, but hey, that’s ok! True consulting is about service—not PPT decks with strategies and tiers of people attached to rate cards. Specifically today, I decided to reframe a topic and approach it from the opposite/negative side. So, instead of telling you when the right time is to get UX design help for your enterprise SAAS analytics or AI product(s), today I’m going to tell you when you should NOT get help! 

Reframing this was really fun and made me think a lot as I recorded the episode. Some of these reasons aren’t necessarily representative of what I believe, but rather what I’ve heard from clients and prospects over 25 years—what they believe. For each of these, I’m also giving a counterargument, so hopefully, you get both sides of the coin. 

Finally, analytical thinkers, especially data product managers it seems, often want to quantify all forms of value they produce in hard monetary units—and so in this episode, I’m also going to talk about other forms of value that products can create that are worth paying for—and how mushy things like “feelings” might just come into play ;-)  Ready?

Highlights/ Skip to:

(1:52) Going for short, easy wins (4:29) When you think you have good design sense/taste  (7:09) The impending changes coming with GenAI (11:27) Concerns about "dumbing down" or oversimplifying technical analytics solutions that need to be powerful and flexible (15:36) Agile and process FTW? (18:59) UX design for and with platform products (21:14) The risk of involving designers who don’t understand data, analytics, AI, or your complex domain considerations  (30:09) Designing after the ML models have been trained—and it’s too late to go back  (34:59) Not tapping professional design help when your user base is small , and you have routine access and exposure to them   (40:01) Explaining the value of UX design investments to your stakeholders when you don’t 100% control the budget or decisions 

Quotes from Today’s Episode “It is true that most impactful design often creates more product and engineering work because humans are messy. While there sometimes are these magic, small GUI-type changes that have big impact downstream, the big picture value of UX can be lost if you’re simply assigning low-level GUI improvement tasks and hoping to see a big product win. It always comes back to the game you’re playing inside your team: are you working to produce UX and business outcomes or shipping outputs on time? ” (3:18) “If you’re building something that needs to generate revenue, there has to be a sense of trust and belief in the solution. We’ve all seen the challenges of this with LLMs. [when] you’re unable to get it to respond in a way that makes you feel confident that it understood the query to begin with. And then you start to have all these questions about, ‘Is the answer not in there,’ or ‘Am I not prompting it correctly?’ If you think that most of this is just an technical data science problem, then don’t bother to invest in UX design work… ” (9:52) “Design is about, at a minimum, making it useful and usable, if not delightful. In order to do that, we need to understand the people that are going to use it. What would an improvement to this person’s life look like? Simplifying and dumbing things down is not always the answer. There are tools and solutions that need to be complex, flexible, and/or provide a lot of power – especially in an enterprise context. Working with a designer who solely insists on simplifying everything at all costs regardless of your stated business outcome goals is a red flag—and a reason not to invest in UX design—at least with them!“ (12:28)“I think what an analytics product manager [or] an AI product manager needs to accept is there are other ways to measure the value of UX design’s contribution to your product and to your organization. Let’s say that you have a mission-critical internal data product, it’s used by the most senior executives in the organization, and you and your team made their day, or their month, or their quarter. You saved their job. You made them feel like a hero. What is the value  of giving them that experience and making them feel like those things… What is that worth when a key customer or colleague feels like you have their back with this solution you created? Ideas that spread, win, and if these people are spreading your idea, your product, or your solution… there’s a lot of value in that.” (43:33)

“Let’s think about value in non-financial terms. Terms like feelings. We buy insurance all the time. We’re spending money on something that most likely will have zero economic value this year because we’re actually trying not to have to file claims. Yet this industry does very well because the feeling of security matters. That feeling is worth something to a lot of people. The value of feeling secure is something greater than whatever the cost of the insurance plan. If your solution can build feelings of confidence and security, what is that worth? Does “hard to measure precisely” necessarily mean “low value?”  (47:26)

Join Kirk and Kevin as they delve into the intricate world of modern technology, exploring its impact on our daily lives and the future of innovation. They discuss the latest advancements in artificial intelligence, how it’s shaping industries and the ethical considerations that come with it. Additionally, they explore the dynamic intersection of technology and human behavior, examining how digital tools are transforming communication, work, and leisure. Their conversation also touches on emerging trends in tech and what they mean for both businesses and individuals, offering insightful perspectives on the challenges and opportunities that lie ahead.

For more about us: https://linktr.ee/overwatchmissioncritical

This episode features an engaging discussion between Raja Iqbal, Founder and CEO of Data Science Dojo, and Amr Awadallah, Founder and CEO of Vectara, the trusted GenAI Platform for All Builders. Raja sits down with Amr Awadallah, a visionary who has played a key role in shaping the world of technology. From his early days at Microsoft to his leadership roles at VMware and Vectara, Awadallah has been a driving force behind groundbreaking innovations in data, cloud computing, and artificial intelligence.This episode is a must-watch for anyone interested in a comprehensive outlook on AI's current state and future trajectory.

In healthcare, data is becoming one of the most valuable tools for improving patient care and reducing costs. But with massive amounts of information and complex systems, how do organizations turn that data into actionable insights? How can AI and machine learning be used to create more transparency and help patients make better decisions? And more importantly, how can we ensure that these technologies make healthcare more efficient and affordable for everyone involved?  Travis Dalton is the President and CEO at Multiplan overseeing the execution of the company's mission and growth strategy. He has 20 years of leadership experience, with a focus on reducing the cost of healthcare, and enabling better outcomes for patients and healthcare providers. Previously, he was a General Manager and Executive VP at Oracle Health. Jocelyn Jiang is the Vice President of Data & Decision Science at MultiPlan, a role she has held since 2023. In her position, she is responsible for leading the data and analytics initiatives that drive the company’s strategic growth and enhance its service offerings in the healthcare sector. Jocelyn brings extensive experience from her previous roles in healthcare and data science, including her time at EPIC Insurance Brokers & Consultants and Aon, where she worked in various capacities focusing on health and welfare consulting and actuarial analysis. In the episode, Richie, Travis and Jocelyn explore the US healthcare system and the industry-specific challenges professionals face, the role of data in healthcare, ML and data science in healthcare, the future potential of healthcare tech, the global application of healthcare data solutions and much more.  Links Mentioned in the Show: MultiplanPlanOptix: Providing Innovative Healthcare Price Transparency   Using a Data Mining Service on Claims Data Can Reveal Significant OverpaymentsConnect with Travis and JocelynCourse: Intro to Data PrivacyRelated Episode: Data & AI for Improving Patient Outcomes with Terry Myerson, CEO at TruvetaRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Bruce Kasman is joined by Joe Lupton to discuss how the Fed will take center stage in a week marked by a number of significant central bank decisions that don’t revolve around a common theme. We anticipate a 50bp rate cut from the FOMC as it responds to a material shift in risk bias. At the same time, we expect the BoE to follow the ECB in guiding toward a cautious rate normalization path while we anticipate the BoJ will continue to emphasize further rate hikes lie ahead. We also see Norges Bank on hold, an easing from SARB, and a tightening from Brazil’s BCB.

This podcast was recorded on 9/13/2024.

This communication is provided for information purposes only. Institutional clients please visit www.jpmm.com/research/disclosures for important disclosures. © 2024 JPMorgan Chase & Co. All rights reserved. This material or any portion hereof may not be reprinted, sold or redistributed without the written consent of J.P. Morgan. It is strictly prohibited to use or share without prior written consent from J.P. Morgan any research material received from J.P. Morgan or an authorized third-party (“J.P. Morgan Data”) in any third-party artificial intelligence (“AI”) systems or models when such J.P. Morgan Data is accessible by a third-party. It is permissible to use J.P. Morgan Data for internal business purposes only in an AI system or model that protects the confidentiality of J.P. Morgan Data so as to prevent any and all access to or use of such J.P. Morgan Data by any third-party.