เกี่ยวกับพอดแคสต์นี้ 🔗
Welcome to DataFramed, a weekly podcast exploring how artificial intelligence and data are changing the world around us. On this show, we invite data & AI leaders at the forefront of the data revolution to share their insights and experiences into how they lead the charge in this era of AI. Whether you're a beginner looking to gain insights into a career in data & AI, a practitioner needing to stay up-to-date on the latest tools and trends, or a leader looking to transform how your organization uses data & AI, there's something here for everyone.
Join host Richie Cotton as he delves into the stories and ideas that are shaping the future of data. Subscribe to the show and tune in to the latest episode on the feed below.
Join host Richie Cotton as he delves into the stories and ideas that are shaping the future of data. Subscribe to the show and tune in to the latest episode on the feed below.
ความถี่ในการอัพเดต:
weekly
ความยาวเสียงโดยเฉลี่ย:
48 minutes
สัมภาษณ์แขก
ภาษาอังกฤษ
สหรัฐอเมริกา
411 ตอน
ตั้งแต่ 15 มกราคม 2018
episodic
AUDIENCE OF THIS PODCAST 🔗
~70.50% of listeners are from United Kingdom.
🇬🇧
GB
70.50%
🇺🇸
US
13.32%
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IE
3.17%
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NL
1.16%
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FR
1.05%
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IN
0.98%
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DE
0.85%
🇭🇰
HK
0.68%
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IT
0.50%
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ES
0.48%
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CA
0.47%
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KE
0.47%
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AE
0.44%
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ZA
0.33%
🇨🇭
CH
0.32%
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NO
0.30%
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SG
0.27%
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TR
0.23%
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TW
0.20%
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PT
0.19%
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BG
0.19%
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JP
0.19%
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MC
0.19%
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BE
0.18%
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RO
0.18%
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MA
0.16%
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PK
0.15%
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PH
0.15%
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MY
0.13%
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PL
0.12%
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GR
0.11%
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AU
0.10%
Others
2.24%
* Data source: directly measured on Listen Notes. แบบเรียลไทม์
ตอนล่าสุด 🔗
AI agents are being pitched for almost every part of sales, marketing, and customer support, from AI SDRs to support bots that resolve tickets without a person. But most businesses aren't excited about agents for their own sake. They want more leads, more closed deals, and happier customers. For go…
ตอนก่อนหน้านี้ 🔗
Most large organizations have bought into AI, yet far fewer have turned it into measurable value. Surveys of thousands of business leaders show the same pattern: spending on technology is up, while investment in data foundations, process redesign, and people lags behind. For data and AI professiona…
AI agents are only as useful as the data they can reach, and most company data is scattered across clouds, data centers, and legacy systems. Giving agents access is easy to get wrong, especially when the data includes salaries, customer records, or financial results. That's why governance, permissi…
AI agents are getting easier to build and connect. Open standards now let them discover each other, pass messages and use shared tools. But connecting agents is not the same as getting them to work toward the same goal, and research on multi-agent systems shows that misalignment grows with every ag…
Career goals in data have shifted quickly. Data scientist was long the default target, but AI engineer has appeared from almost nowhere, and companies are moving training toward AI literacy, agents, and prompt engineering. Certifications are becoming a common way to prove these skills, while many p…
Most hiring decisions still come down to gut feel, and gut feel is full of blind spots — a favorite school, a firm handshake, the type of coffee someone carries into the room. As AI reshapes recruiting, the industry is being forced to ask whether decisions can be made on evidence instead of instinc…
A job title can stay the same while the work underneath it changes completely. That idea ran through RADAR 11x, our online conference, and this recap keeps the best moments from four of the day's eight sessions. You will hear how to sort your tasks between you and AI, which durable skills hold thei…
AI agents are moving from demos into production, but many teams run into the same wall: responses that take too long and cost too much. As models grow bigger and reason through more steps before answering, every chained agent adds delay, and that adds up fast at scale. For data and AI professionals…
As agents take over more of the actual coding, the nature of technical work is shifting from producing software to judging it. Engineers increasingly spend their time specifying what should be built and then checking whether an agent's output actually solved the problem, rather than writing every l…
Hypergrowth companies rarely scale on strategy alone — they scale on how fast decisions get made and how much risk employees feel safe taking. A recurring idea in high-growth environments is treating decisions differently depending on whether they're reversible, and rewarding people for making an i…
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