Episodit
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Successful AI projects/ideas have many necessary ingredients, one of the most important being the people you identify and choose to be part of the project. How to you decide and how to you build culture? Ron Green is a successful entrepreneur, listen in as he discusses his most current company (almost seven successful years now!) as he walks us through finding the right folks to add to the team, how they build their culture of discovery and delivery at KungFu, and how they deliver projects to their clients.
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The first Federal Chief AI Officer, Oki Mek, runs through three key components in building responsible and secure AI programs, something desperately needed in today's organizations.
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Puuttuva jakso?
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How do you get all the cowfolk to giddy-up in the same direction? Smart people are notorious for having opinions; combine intelligence with the independent streak in North Dakota folks and it makes for a lot of work in driving change. Kim Weis has some insightful tips in lessons learned in trying change, failing, learning, and trying again.
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Do you want some AI with that? It's everywhere, though thankfully not IN our coffee (yet). So if AI conversations are ubiquitous, how do we sort value from folly? Keatra Nesbitt is a practiced strategy leader and data scientist, listen in as she shares her thoughts on how she navigates these complex conversations with her clients.
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Utilizing Gen AI towards your organization's specific needs presents both an opportunity and a challenge. Opportunity to take advantage of massive investments by large tech firms; challenges in that it can be difficult to know what is correct and usable at scale out of these projects. Sabre's Laura Palomino discusses novel approaches they've used towards that have helped her team, and others at Sabre, pursue innovation and change, and be more efficient in testing, and more proactive in resolving potential issues before users find them.
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What does it mean to have AI ready data? And once I know that, what do I do about it? Ian Stahl is Director of Product Management @ Informatica and has seen many data centric applications come and go. He provides insights into what's happening in the market today and how we all may work better together to make data highly useable and fit for purpose.
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We have come a long way since the publication of "Hidden Technical Debt in Machine Learning Systems" was published almost a decade ago. ML Ops has transformed how data science work is delivered, managed, and monitored. Great?
Maybe. In this discussion we cover what is still one of the most glaring gaps in the AI/ML field. Disagreement is accepted and encouraged.
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Audio from our LinkedIn Live Event!
Organizational structure, team, and culture are critical components to repeatedly and consistently delivering innovation, business results, and absorbing innovative techniques from outside the org walls. AI is rightfully getting the lion’s share of attention, but to make purposed and impactful use, and to have it generate value, many teams across many people and many business units need to align on a baseline operating model. Without this, work efforts, collaboration, and implementations will continue to have marginal gains, if any.And the future will be left to the companies that have or will figure out operating models for innovation and AI.
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Engineers are famous for building the amazing, and for wasting time on pet projects, dead ends, and losing track of the customer and real problems. How do you balance creativity and solving real problems? Jodi Blomberg, VP of Data Science at Cox Automotive, has sound, entertaining, and insightful advice!
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Digital transformation has been around for a while, but have we succeeded at it? AI, for better and worse, is pushing change in organizations. One of the positives is that AI is creating urgency for organizations to do the things they have deprioritized for a long time: data management, governance, or in this case, digital transformation. Now that this is come back into focus, what are the foundations of effective change?
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At the end of a spectacular achievement, the journey there can sometimes seem obvious, but we all know clarity at the start is frequently missing. Nancy Tickle was at the center of Chesterfield County's pioneering transformation in using data to forecast micro-level growth, achieving something no other city or county had done before. In this episode, Nancy offers a detailed account on how Chesterfield achieved remarkable results.
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Software is eating the world (or used to, that was soooo 2011). AI is eating the world now at a blistering pace, and while a lot of it feels like hyperbole, and in some cases faked gains (Devin), many initiatives in AI & data are very real, and organizations are adopting and adapting their cultures to include these gains and take advantage of what they enable.
Within this astounding pace of change, we have lots of anxiety on learning and adapting. How do I stay current? Is RAG even going to be a thing a year from now?
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Want some AI with your coffee? It seems anywhere you turn, AI is infused into every interaction, decision, and experience, so it makes sense that your organization should upgrade (or create) your AO strategy. But how? CDO Peggy Tsai offers incredibly practical advice on AI Strategy for today's Chief Data Officers.
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"What's in a name?" Shakespeare's romantic notion that naming something is irrelevant may work in purposes of his plays, but for the data world, naming, and conformance on the meaning of names, is critical. Amit Pahwa has spent a good portion of his career dedicated to making our data lives easier, all by focusing on that most basic of tasks: naming things.
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What is most useful for making decisions? Leaders and business owners face decisions almost every moment of the day. We aspire to be an entirely data driven world, so where does this leave 'gut' or intuition? Seyi Fabode helps us explore these questions; be prepared for some philosophical and practical insights.
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Mergers and acquisitions have a high failure rate in terms of hitting the envisioned objectives. There are many root causes, but lack of alignment on operating models is definitely on the list. Jeremy Kingry discusses how Argano quickly analyses a target acquiree using data and enforces aligned operating models, using data!
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A data strategy is an AI strategy. Subroto has 20+ years in data, innovation, and creating companies; he walks us through several topics that everyone should pay attention to in assembling their AI and data strategy.
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We have worked across the globe for a while, but not everyone succeeds at it. Laura of IFC, a division of the World Bank, provides expert advice and guidance on how you might better understand your colleagues across the globe, gain understanding, and succeed together.
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Emerging Leaders Series: Where do millennials get their leadership advice? There is a bias in the market to perceive that what big tech and other large progressive companies are publishing and discussing around open, collaborative, and supportive models for growing, supporting, and mentoring the next generation of talent and leaders IS the pervasive model in the market. But there are many orgs across the US and the world, with many different cultures, and many different models in how they grow and support the next generation. Stephanie Rennie shares her experiences in coming to data via the Army to her current position at Sysco.
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Collaborating smoothly using data with our colleagues can be a challenge, but what about with external partners? Properly sharing insights and analytics is a challenge even today. Solomon has worked in some of the largest data environments wherein sharing and collaborating with external partners was critical to success, and has some useful insights on collaboration challenge history and what we do about it.
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