39.5M
THHow can organizations keep agentic AI governed, trusted and scalable? #IBMPartner More insights from my interview with Jordan Byrd, Product Marketing Lead @ibm.
#Data #AI #IBM #TheRavitShow
@theravitshow
#Unstructured Data Analytics
Watch Reels videos about Unstructured Data Analytics from people all over the world.
Watch anonymously without logging in.
Related Searches
Trending Reels
(12)
54.5K
TH#IBMPartner What has changed in how enterprises adopt AI? I sat down with Jordan Byrd, Product Marketing Lead @ibm to find out. #Data #AI #IBM #GartnerDa #TheRavitShow
@theravitshow
384
WAIn the enterprise world, the biggest hurdle for AI isn’t the intelligence of the model; it’s the messiness of the data infrastructure. This caption breaks down why major companies are dismissive of the hype and where the real work is happening.
The Enterprise Reality: Utility > Hype
Most AI projects fail because big companies are dealing with “bad plumbing.” While startups show off flashy demos, the “Dismissive” camp is focused on the unsexy reality of legacy systems.
1. The Pilot Trap
• Everyone loves a cool AI demo, but most enterprise pilots die before they ever reach production.
• They fail because they hit the “pipes”—the underlying data systems that aren’t ready for AI.
2. Data is the Real Barrier
• Corporate data is often a mess: it’s broken, siloed in different departments, and trapped in outdated legacy systems.
• You can have the best AI model in the world, but if the data “plumbing” is clogged, the AI can’t function.
3. Who Actually Wins?
• The winners in this space aren’t the ones with the flashiest chatbots.
• The real value is created by those fixing the data systems so that AI can actually be useful in the real world.
The Bottom Line
In the corporate world, utility beats hype every single time. If you want AI to work at scale, you have to fix the pipes first.
Next, we look at where the smartest money is moving: Physical AI. This is Wave Theory Insights. 🌊 #ai #business #startup #entrepreneurship #businessmindset
@wavetheory.insights
46.0K
THWhat is the future of Knowledge? Most mainframe challenges today are not caused by broken systems.
They are caused by disappearing knowledge.
Senior experts are retiring.
Documentation is incomplete or outdated.
And newer team members are expected to operate some of the most critical systems in the enterprise with very little context.
In Part 1 of this podcast, we focus on where the AI journey really begins.
- Why mainframe teams are under pressure right now.
- What types of institutional knowledge are most at risk.
- And how AI changes the way that knowledge is captured, shared, and used.
Liat Sokolov, @bmcsoftware walks through why AI is showing up at this moment and why traditional approaches have not been enough.
Anthony DiStauro, BMC Software explains how AI helps close the skills gap by shortening learning curves and guiding teams through complex systems with more confidence.
This episode is not about autonomy yet.
It is about preserving what matters and helping people work better with the systems they already depend on.
#data #ai #mainframe #agents #theravitshow
@theravitshow
80.2K
THMost mainframe challenges today are not caused by broken systems.
They are caused by disappearing knowledge.
Senior experts are retiring.
Documentation is incomplete or outdated.
And newer team members are expected to operate some of the most critical systems in the enterprise with very little context.
In Part 1 of this podcast, we focus on where the AI journey really begins.
- Why mainframe teams are under pressure right now.
- What types of institutional knowledge are most at risk.
- And how AI changes the way that knowledge is captured, shared, and used.
Liat Sokolov, @bmcsoftware walks through why AI is showing up at this moment and why traditional approaches have not been enough.
Anthony DiStauro, BMC Software explains how AI helps close the skills gap by shortening learning curves and guiding teams through complex systems with more confidence.
This episode is not about autonomy yet.
It is about preserving what matters and helping people work better with the systems they already depend on.
#data #ai #mainframe #agents #bmi mainframe skills bmc theravitshow
@theravitshow
1
BDEnterprise AI adoption is accelerating, but the challenges organizations face are often misunderstood.
On Deconstructing Data, Mike Finley, spoke about how implementing AI inside large organizations differs from traditional IT transformations.
Many enterprises initially treat AI adoption like previous technology migrations. However, AI introduces new complexities around data readiness, process design, and organizational change.
Key themes from the discussion include:
→ Defining agency in AI-powered systems
→ Transforming enterprise processes with AI
→ Preparing organizations for a GenAI roadmap
→ Building the supporting tech stack
As AI continues to evolve, enterprises that rethink how they approach data and process transformation will be best positioned to capture its value.
🎧 Listen to the full episode:
https://www.bdex.com/deconstructing-data-podcast/
@bdex_llc
1.5K
NOAI is making most traditional tech moats obsolete, but network effects remain the only truly defensible business advantage. Full episode is at the link in bio.
@notanotherpodcast.clips
110
CRCompanies race to deploy AI agents before building the knowledge base those agents need to function. The technology stack is being built backward.
Our conversation with data infrastructure architect Shota Ishii exposes the sequence mistake on the latest episode of The Private Equity Data Guy: https://youtu.be/IWvGxR6UQZc
Here's what we found out in our podcast episode:
• Shota calls the combination of management accounting principles and business understanding "the knowledge of the company," the foundation required before AI delivers results.
• Once knowledge is organized, you can create dashboards, make management decisions, and build agents that negotiate, but the knowledge base must exist first to make sense of business data.
• The correct sequence is: create data platform, build knowledge base, add intelligence layer, then deploy AI agents or automation tools.
• Without underlying data structured properly, AI cannot understand what numbers in tables mean or how records relate to each other.
• Most companies skip foundation steps and jump straight to AI implementation, creating agents that lack the context to produce actionable results.
Are you building AI on organized knowledge that defines what every term means, or deploying agents that cannot understand your business?
#PrivateEquity #DataStrategy #AIImplementation #KnowledgeManagement #WorkingCapital #ThePrivateEquityDataGuy
@crawfordmcmillan
178
DDAI success isn’t just about compute. It’s about data and compute working together. 🤝
As DDN's Alex Bouzari on TBPN explains, organizations can’t focus on GPUs alone if they want AI to deliver real value.
With compute resources constrained and demand continuing to grow, the real challenge becomes maximizing the effectiveness of every AI workload.
That means building infrastructure that:
• Eliminates data bottlenecks
• Maximizes GPU utilization
• Makes AI deployments economically viable
Because AI adoption only accelerates when the ROI truly pencils out.
@ddnintelligence
162
AIAn unfiltered look at the ultimate AI accelerator! When asked what the *real* bottleneck is when scaling innovative solutions, Naratix AI Co-founder Cristian Orasanu points directly to the source. Forget small tweaks; the dream scenario is an internet-wide data deep-dive. Imagine the power unlocked by classifying and grouping data by scraping the web *ten times over*. That's the scale of ambition required to truly revolutionize AI classification models when time and money are irrelevant constraints. This perspective, emerging from a journey that spans cybersecurity to e-commerce AI, highlights the foundational struggle: the crucial need for robust **Data Aggregation and Standardization**. A masterclass in prioritizing the raw fuel for intelligent systems.
#AIData #DataAcquisition #AIEcommerce #ScaleUp #TechInsights #FoundersJourney
@aiinterconnect
203
HAThe most common mistake teams make when building AI products: not looking at their data. They set up observability platforms. They run benchmarks. But they haven't spent 30 minutes reading 20 actual LLM outputs.
This isn't new. As I mention in this clip, looking at data has always been table stakes. SQL, spreadsheets, dashboards. For AI, the same principle applies with one twist: the data you need to look at is the actual text your model produces.
Here's what happens when teams skip this:
- They write evaluators for problems they imagine, not problems that exist
- They reach for generic metrics (BERTScore, ROUGE) that can't catch application-specific failures
- They outsource labeling to people who lack the domain knowledge to judge quality
The fix is unglamorous. One domain expert can make a huge impact by reviewing ~ 100 traces. More on this here: hamel.dev/blog/posts/evals-faq
@hamelsmu
2
10Is your AI starving for context? 🧠💻
Most people think AI needs more data. In reality, it needs better data.
I sat down with Alex Todd on the 10X AI Podcast to dive into why raw volume is useless without behavioral context. Alex explains how his work at ReliablyMe is bridging the gap between "noisy" data and "valid" data.
The 10X Takeaway:
If you want exponential results, you have to feed the machine the "why" behind human behavior—not just the "what."
🚀 "A godsend for AI." — Alex Todd
👇 Drop a "DATA" in the comments if you want to know how to clean up your AI strategy!
.
.
.
#10XAI #ArtificialIntelligence #DataStrategy #Innovation #JuliusNeil #AlexTodd #TechTalk #FutureOfWork #AIInsights #ReliablyMe
@10xaijulius
✨ #Unstructured Data Analytics Discovery Guide
Instagram hosts thousands of posts under #Unstructured Data Analytics, creating one of the platform's most vibrant visual ecosystems. This massive collection represents trending moments, creative expressions, and global conversations happening right now.
#Unstructured Data Analytics is one of the most engaging trends on Instagram right now. With over thousands of posts in this category, creators like @theravitshow, @notanotherpodcast.clips and @wavetheory.insights are leading the way with their viral content. Browse these popular videos anonymously on Pictame.
What's trending in #Unstructured Data Analytics? The most watched Reels videos and viral content are featured above. Explore the gallery to discover creative storytelling, popular moments, and content that's capturing millions of views worldwide.
Popular Categories
📹 Video Trends: Discover the latest Reels and viral videos
📈 Hashtag Strategy: Explore trending hashtag options for your content
🌟 Featured Creators: @theravitshow, @notanotherpodcast.clips, @wavetheory.insights and others leading the community
FAQs About #Unstructured Data Analytics
With Pictame, you can browse all #Unstructured Data Analytics reels and videos without logging into Instagram. No account required and your activity remains private.
Content Performance Insights
Analysis of 12 reels
💡 Top performing posts average 9.9M views (3.0x above average). Moderate competition - consistent posting builds momentum.
Post consistently 3-5 times/week at times when your audience is most active
Content Creation Tips & Strategy
💡 Top performing content gets over 10K views - focus on engaging first 3 seconds
✨ Many verified creators are active (42%) - study their content style for inspiration
📹 High-quality vertical videos (9:16) perform best for #Unstructured Data Analytics - use good lighting and clear audio
✍️ Detailed captions with story work well - average caption length is 788 characters
Popular Searches Related to #Unstructured Data Analytics
🎬For Video Lovers
Unstructured Data Analytics ReelsWatch Unstructured Data Analytics Videos
📈For Strategy Seekers
Unstructured Data Analytics Trending HashtagsBest Unstructured Data Analytics Hashtags
🌟Explore More
Explore Unstructured Data Analytics#unstructured data#analyte