We Love Ugly Data! The Deep Analysis Podcast - Episode 11 January 2024

We Love Ugly Data! The Deep Analysis Podcast

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المحتوى المقدم من Alan Pelz-Sharpe. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Alan Pelz-Sharpe أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.

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Understanding the Elegant Math Behind Modern Machine Learning 1:14:43

منذ 8 أيام1:14:43

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1:14:43

Artificial intelligence is evolving at an unprecedented pace—what does that mean for the future of technology, venture capital, business, and even our understanding of ourselves? Award-winning journalist and writer Anil Ananthaswamy joins us for our latest episode to discuss his latest book Why Machines Learn: The Elegant Math Behind Modern AI . Anil helps us explore the journey and many breakthroughs that have propelled machine learning from simple perceptrons to the sophisticated algorithms shaping today’s AI revolution, powering GPT and other models. The discussion aims to demystify some of the underlying mathematical concepts that power modern machine learning, to help everyone grasp this technology impacting our lives–even if your last math class was in high school. Anil walks us through the power of scaling laws, the shift from training to inference optimization, and the debate among AI’s pioneers about the road to AGI—should we be concerned, or are we still missing key pieces of the puzzle? The conversation also delves into AI’s philosophical implications—could understanding how machines learn help us better understand ourselves? And what challenges remain before AI systems can truly operate with agency? If you enjoy this episode, please subscribe and leave us a review on your favorite podcast platform. Sign up for our newsletter at techsurgepodcast.com for exclusive insights and updates on upcoming TechSurge Live Summits. Links: Read Why Machines Learn, Anil’s latest book on the math behind AI https://www.amazon.com/Why-Machines-Learn-Elegant-Behind/dp/0593185749 Learn more about Anil Ananthaswamy’s work and writing https://anilananthaswamy.com/ Watch Anil Ananthaswamy’s TED Talk on AI and intelligence https://www.ted.com/speakers/anil_ananthaswamy Discover the MIT Knight Science Journalism Fellowship that shaped Anil’s AI research https://ksj.mit.edu/ Understand the Perceptron, the foundation of neural networks https://en.wikipedia.org/wiki/Perceptron Read about the Perceptron Convergence Theorem and its significance https://www.nature.com/articles/323533a0…

منذ عام واحد 35:34

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The eleventh episode of the podcast we now know and love as "We Love Ugly Data!" is out; it's the first of season 3 and kicks off our 2024!
The eleventh episode of the podcast – the first of 2024 and therefore the first of season 3 – that we know and love as “We Love Ugly Data!” is out; available everywhere you get your podcasts from and, of course, in video form via YouTube (which we’ve embedded below). As always, it’s three topics in (just a touch over) 30 minutes, and show notes are, as usual, included below the embedded video. Matt and Alan are in the chairs this time around.
In this month’s episode:
Topic 1: 2024 is a money year?
First up, the pair discuss Alan’s recent 2024 blog opener, in which the changing face of funding, divestitures, and a general subtle change in focus for private equity and venture capital make the year a very interesting prospect. Matt pins the analyst industry with some blame for firms' intense clustering on some use cases, where much of the landscape remains greenfield.
Topic 2: Salesforce & Ugly Data
Looking back at one of the last sets of vendor announcements of 2023, Matt runs through his analysis of Salesforce’s stated intent to take on unstructured data as a knowledge source for AI, and Alan reflects on how we’re heading into another cycle of discovering this stuff is hard to do well.
Topic 3: Time to re-think our life of files?
Ahead of the release of the year's first Analyst Briefing and in the midst of its final editing, the pair discuss their thoughts about how files as containers of information aren’t really suitable for most of the purposes we use them for and are frankly really overweight (and not just holiday weight, real weight issues). It also contains Matt’s attempt to drag Forrest Gump into a related analogy, which, in hindsight, was ill-advised.
Show notes for Series 3, Episode 1.
Topic 1: 2024 is a money year?
Alan’s blog post “2024 – A Year of Change for Enterprise Software.”
Topic 2: Salesforce & Ugly Data
Matt’s blog post “Now They Love Ugly Data Too! Salesforce embraces the unstructured”.
Salesforce’s World Tour NYC announcement press release.
Topic 3: Time to re-think our life of files?
COMING SOON – Analyst Briefing “ECM in 2024 and Beyond – Reframing the Equation” (free registration required for download).
The previous “We Love Ugly Data!” episode where Matt and Dan discuss the repetition of “n% of data is unstructured”.

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We Love Ugly Data! The Deep Analysis Podcast - Episode 11 January 2024

We Love Ugly Data! The Deep Analysis Podcast

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published منذ عام واحد

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#Trading #Business #Alan Pelz-Sharpe #File Sharing #Industry Analyst #Blockchain #Artificial Intelligence #Content Services #Gartner #Machine Learning #Tech #MBA #ECM #RPA #M

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Enterprise Search (again) - Just don't call it a comeback! 35:43

منذ 17 يومًا35:43

35:43

The twenty-fourth episode of the podcast that you know and love as "We Love Ugly Data!" is out; available in audio form everywhere you get your podcasts from and additionally in video form via YouTube (which we've embedded below). We're back to our usual format this time, so it’s three topics in (just a touch over) 30 minutes, and the show notes are as usual included just below the embedded video. Matt and Alan are in the chairs for this edition, this time discussing; our latest MMI research on AI and automation (that we've conducted with support from Hyland), that Enterprise Search appears to be back (again) and another instalment of how analysts add value (and when they don't). In this month’s episode: Topic 1: New MMI available As you know, we're not the greatest at telling people about the great work that we do, but that stops right now* with Matt and Alan discussing the "Deep Analysis Market Momentum Index™: Intelligent Automation, Artificial Intelligence, and Data" report that we've recently launched with support from Hyland. It's packed with fresh research data on a range of topics around AI, automation and data, but for this segment Matt has picked out just two of the data points to discuss; 1. it's not a shortage of workers that's driving AI (and intelligent automation) projects and 2) data; important, disparate and often incomplete. The results of what is and what is not driving enterprise AI and the state of enterprise data will astonish (if not surprise) those of you with a passing interest in the subject. (* we can't guarantee that this will stop now, unfortunately) Topic 2: Don’t call it a comeback (Enterprise Search) Alan's written a blog post - "The Revival of Enterprise Search: Lessons from the Past in the Age of Agentic AI" - in no small part inspired by the revival of lots of the use cases around Enterprise Search (if not the name itself, because of its terrible reputation for being a bottomless money pit). Here he discusses how the underlying problems have never gone away - as pointed to in the aforementioned research data - and how those challenges, with the more recent appearance of LLMs, have driven new interest in the technology. Much like last month's discussion about Agents (and the very common deterministic tasks vs less common probabilistic ones), the pair point to a very similar and not unrelated split in search query intent (Matt calls this recover vs discover). Topic 3: Analyst Value (pt 2) Finally, Matt and Alan return to the topic that they started in the last podcast of what makes great value for customers with industry analysts and what doesn't. This time Alan talks about "telling the truth" and "invented numbers" while Matt adds "super specialisms" and declaring "X is dead". https://www.youtube.com/watch?v=HJ9M8wxFTbA Related Links for Series 4 Episode 2 You can download the Hyland sponsored "Deep Analysis Market Momentum Index™: Intelligent Automation, Artificial Intelligence, and Data" here, for FREE. If you missed the previous MMI report "AI and Unstructured Data Management, conducted by Deep Analysis with support from AIIM and M-Files", you can also download that for FREE too! If you'd like to hear Matt and Alan discussing that previous MMI report, then you'll want to go back September '24's podcast for that, which you can find right here. Support the show…

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Explaining AI Agents & looking at CRUD 35:35

منذ 7 weeks35:35

35:35

The twenty-third episode of the podcast that you know and love as "We Love Ugly Data!" is out; available in audio form everywhere you get your podcasts from and in video form via YouTube (which we've embedded below). We're back to our usual format this time, so it’s three topics in (just a touch over) 30 minutes, and the show notes are, as usual, included just below the embedded video. Matt and Alan are in the chairs for this edition, this time discussing our brand new report on AI Agents, whether business applications are just a bunch of CRUD, and how analysts add value (and when they don't). In this month’s episode: Topic 1: AI Agent Explainer Having confidently introduced this episode as the first of series 3, when it's the beginning of series 4 (apologies for that), Matt introduces the first big report release of 2025; "AI Agents: What They Are, How They Work, and Where Organizations Might Best Use Them". The pair discuss why this report is important at this present time and how it is designed as a primer for people being asked in their organizations, "What's our approach to agents?" and provide a guide to their immediate construction and use. Matt goes on to explain that it's the specificity that makes agents different from assistants (and indeed, there's a blog post on this exact subject that, for some reason, he forgot to mention). Oh, and the report is free to download, so there's nothing to stop you from getting your copy right away. Topic 2: …a load of CRUD? Next up, Satya Nadella (CEO of Microsoft) was on the B2G podcast (with Bill Gurley and Brad Gerstner) last month, talking about a range of things, including his belief that business applications are simply CRUD (create, read, update, and delete) databases with business logic attached and that the business logic will soon be moving to AI Agents. Matt and Alan discuss this, with Matt rather annoyed at the reductive description being employed and wondering why customers would want to shift logic from well-understood applications into a more expensive, less well-understood alternative. Topic 3: Analyst Value Finally, for this month, following up from last year's discussions about the dos and don'ts of analyst briefings and user conferences, Matt and Alan are now taking on their peers in the analyst industry to determine where we all add value to clients and where sometimes we don't. We're fans of the insights from talking to customers but less of the hype (and just plain lousy analysis). https://www.youtube.com/watch?v=fJGd10oTpGQ Show notes for Series 3, Episode 12. Related Links for Series 4 Episode 1 Specificity is the key to understanding AI Agents ; Matt's intro blog to the new AI Agent research. AI Agents: What They Are, How They Work, and Where Organizations Might Best Use Them ; here's where to grab the report from. Satya Nadella Reveals ‘How AI Agents Will Disrupt SaaS Models ; Outlook Business' write up of Satya Nadella's appearance on the B2G podcast (where he outed all business apps as CRUD). Here's the full 90 minute YouTube video of the B2G podcast with Satya Nadella. In case you missed it, here's the post "Analyst Briefings; Dos and Don’ts" that we published last year af Support the show…

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2025 Unstructured Data Market Predictions 34:21

منذ 12 weeks34:21

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The twenty-second episode of the podcast that you know and love as "We Love Ugly Data!" is out; available in audio form everywhere you get your podcasts from and in video form via YouTube (which we've embedded below). This time, the format for our last episode of the year is slightly different, as both Alan and Dan join Matt to look back at the 2024 predictions and highlight 3 of the predictions from our 2025 report . In this month's special episode, Alan, Dan, and Matt each get to reflect on their prediction from last year and pick out one of this year's to highlight: Alan's 2025 Prediction : Structured data people will stop treating unstructured data like something that got stuck in their shoes. First up, Alan reflects on his 2024 prediction, "Knowledge graphs and data meshes will gain traction," which, given all the discussion around enabling data for AI agents, he's giving himself a win (even though it's still a trend that's unfolding). This isn't unconnected to the 2025 prediction, which won't also allow unstructured data to come to the foreground in planning. Still, Alan reckons it will generate a wave of M&A activity as vendors scramble to find tools to fold into their products quickly. Dan's 2025 Prediction: Intelligent document processing (IDP) companies must cross the border to grow. Dan gives himself a thumbs up for his prediction of last year: "For the first time in human history, machines will read and process more documents than knowledge workers do," which isn't only likely accurate, it's pretty much impossible to disprove. Clever Dan. For 2025, he points out that for IDP companies to continue to grow, they will have to b reak out of their respective comfort zones and embrace a broader range of use cases and industries . Oh, and here, too, he's expecting a glut of M&A. Matt's 2025 Prediction: The shift to “payment on outcome” is going to lead to some awkward conversations between customers and suppliers. Matt recalls his 2024 prediction: "Generative AI will face its first cold winter.” He reckons it probably did as organizations weighed up the difficulties of making those assistant (Copilot) use cases work within their ways of working. But in 2025, amidst the wave of AI agents, it's the shift in how vendors look to alter their payment terms where Matt reckons there will be some ructions. Payments based on outcomes are a trend here, and while it's not the predominant way of charging (and probably won't ever be), Matt reckons aligning what each side believes success looks like will be a real challenge. The Salesforce statistic Matt mangled a bit in his recollection of the 2024 Dreamforce keynote was monthly numbers of 83.2 billion Flows being executed vs 112,000 prompts. https://www.youtube.com/watch?v=TmTqPX1BwsI Show notes for Series 3, Episode 12. Predictions Related Links Here's our 2024 predictions podcast, so you can here past us making those predictions in full. Our 2025 report in full! Dan's extended blog on the 2025 IDP trends; Crossing the Border. Here's Matt talkin Support the show…

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Hyperscience, Rossum & AI2Z - Why they won Deep Analysis Awards 33:21

منذ 17 weeks33:21

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The twenty-first episode of the podcast you know and love as "We Love Ugly Data!" is out. It's available in audio form everywhere you get your podcasts from and additionally in video form via YouTube (which we've embedded below). This time, the format is different, as Alan and Dan join Matt to discuss the five winners of this year's Deep Analysis Innovation Awards . In this month's special episode: Winner: Hyperscience First up, Dan talks about Hyperscience and its Hypercell for GenAI , which helps organizations process large volumes of data ready for use in GenAI applications. Winner: ai12z Next, Matt outlines why AI12z is among this year's winners. Their approach to generative AI enables not only mid-market customers but also the systems integrators that service them to deliver AI assistants and agents. Winner: Rossum Dan's back to introduce Rossum's award and discuss its T-LLM (Transactional LLM) model, which it built from millions of transactional business documents. Winner: Composable Matt introduces Composable as the next award winner , explaining how the company focuses on organizations that have outgrown the available tooling for their AI application development and need Composable's help managing the plethora of models and inference suppliers that those heavily invested in developing GenAI applications in-house need to work with. Winner: UiPath Finally, Dan explains why UiPath won this year's awards for its DocPath and CommPath AI models, which were launched earlier this year . https://www.youtube.com/watch?v=nT0RtYNIFGQ Show notes for Series 3, Episode 11. Innovations Awards Related Links! Download the full Innovation Awards report . Support the show…

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New research, a look at why automation and AI go together and a look at the Brazilian market 33:19

منذ 20 weeks33:19

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The twentieth episode of the podcast that you know and love as "We Love Ugly Data!" is out; available in audio form everywhere you get your podcasts from and additionally in video form via YouTube (which we've embedded below). As always, it's three topics in (just a touch over) 30 minutes, and the show notes are as usual included just below the embedded video. Matt and Alan are back again, this time discussing; the latest vendor research reports on DRUID, M-Files and Writer, how automation and AI agents are friends forever and the (really positive) state of the automation software market in Brazil. In this month's episode: Topic 1: New Vendor Research ! First up, Matt and Alan discuss the recently published set of vendor profiles (or whatever it is we're supposed to call them these days). DRUID is up first; an all-in-one platform for the development of conversational AI assistants and agents, which can be embedded into business processes. M-Files is a Microsoft-focused knowledge work automation that provides an innovative “no-folders” approach to document and knowledge management. Finally, Writer enables enterprises to create custom GenAI applications to assist workers in producing precise, detailed, and compliant content which can be plugged into routine tasks to help streamline processes (it's also out there raising an apparently huge funding round right now and - as Alan notes - is also an acquisition target right now too). Matt also mention Writer is one of a host of vendors now using synthetic data to speed up development new models (with all the potential issues that creates). Topic 2: AI Agents and Automation; forever friends Following on from last month's discussion about AI Agents in the run-up to Salesforce's Dreamforce conference, Matt's published something wrapping up how integral automation (think workflow, RPA etc) is to the success of any AI Agent exercise and the pair discuss their takes on how those efforts might play out for those looking to adopt. There's a chance to have a look at the Agentforce announcements in the rear-view mirror, reviewing the takes from Matt in his home office at the east Kent seaside and Alan on the ground in San Francisco . Topic 3: Automation in Brazil Back in August Alan visited Brazil to speak and catch-up with the state of the automation software market in the country (the world's 8th or 9th biggest economy, depending on whether you're using the IMF of the World Bank's numbers). Turns out that it is thriving and in the context of the other discussions about AI Agents, this puts the country in a potentially advantageous position. https://www.youtube.com/watch?v=fkvWyu0e1s4 Show notes for Series 3, Episode 10. Topic 1: New Vendor Research! "New Research Dropping Today" blog post (with summaries of the three profiles). The Deep Analysis vendor research database ; links to all the vendor research we've published (you can browse as a non subscriber, but you'll need to be a subscriber to get the full reports). The CNBC report "AI startup Writer, currently fundraising at a $1.9 Support the show…

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The do's and don'ts of analyst briefings, a look at our recent Al survey and the Agentic money grab 32:45

منذ 24 weeks32:45

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The nineteenth episode of the podcast that you know and love as "We Love Ugly Data!" is out; available everywhere you get your podcasts from and, of course, in video form via YouTube (which we've embedded below). As always, it's three topics in (just a touch over) 30 minutes, and the show notes are, as usual, included in the embedded video below. This time around, Matt and Alan are in the chairs, discussing a new research report we've just released on AI and unstructured data, how AI agents are coming and how they might be after your wallet, and the dos and don'ts of storytelling when you're presenting your software product. In this month's episode: Topic 1: AI & Unstructured Data, Survey Out Now First up, Matt and Alan discuss the brand new report that Deep Analysis has collaborated on with AIIM and MFiles; " Market Momentum Index: AI and Unstructured Data Management ". The report - which you can download from AIIM or MFiles directly - provides insight into the adoption and use of AI in organizations and provides information on how those same organizations manage unstructured data for AI and their view of its importance. They pick up on a couple of data points on AI adoption and how vast the sprawl of unstructured data is across IT estates (as well as mentioning that there's more related research already in the works). Topic 2: Agents and Cash In the second topic this month, Matt has recently posted a new blog post, "Here are the agents. They've come to collect," about a shift in how AI is likely to be paid for as the generative wave moves from assistants to agents. With Salesforce's "Dreamforce" conference only days away at the time of recording (and neatly avoiding saying anything that will break any news embargos), the pair chat about how the economics had shifted from when we first predicted a metered future for generative AI a year ago and the company's announcement of its "hard pivot" to AI agents with Agentforce . Matt also tries to extend an analogy about buffets far too far for its own good. Topic 3: Analyst Briefings, Dos and Don'ts (Pt 2) Over recent podcasts, Matt, Alan, and Dan discussed their dos and don'ts for analyst briefings (which we recently rounded up here , so you don't have to search through old podcasts to find them). It turned out to be quite popular, so to try and help further - and inspired by Dan's suggestion that good storytelling was a definite do - here Matt and Alan suggest their suggestions for dos and don'ts. It sadly quickly heads off-piste, and you end up with further conference recommendations ( that we covered in part previously ). https://www.youtube.com/watch?v=iaU_jlchk1M Show notes for Series 3, Episode 9. Topic 1: AI & Unstructured Data, Survey Out Now Our Market Mome Support the show…

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GenAl & Data, how vendors should present to analysts (top tips) and some new research 36:25

منذ 29 weeks36:25

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The eighteenth episode of the podcast that you know and love as “We Love Ugly Data!” is out; available everywhere you get your podcasts from and, of course, in video form via YouTube (which we’ve embedded below). As always, it’s three topics in (just a touch over) 30 minutes, and the show notes are, as usual, included in the embedded video below. This time around, Matt and Alan are in the chairs, discussing 4 new Vendor Profiles (Tungsten Automation, Cognaize, Composable, and iGrafx), catching up on how the meeting of unstructured data and generative AI is working out, and – our second edition of – Analyst Briefings; Do’s and Don’ts. In this month’s episode: Topic 1: New Research! We recently published the latest batch of profiles for our Vendor Analysis research (the artist formerly known as Vendor Vignettes, although we often forget that that’s supposed to be the former title and still use the phrase all the time). Here, Matt and Alan discuss the new profiles on Tungsten Automation, Cognaize, Composable, and iGrafx (a real mix of the old and the brand new here). Topic 2: GenAI and Data: an update Matt has just begun his third year with Deep Analysis, and to celebrate, he and Alan look at the path that generative AI and its intersection with unstructured data have taken over the last few years: from the first flush of “isn’t it amazing/scary!” to “here’s a complicated way to avoid asking it anything much” and onto the exciting world of agents and large action models . Topic 3: Analyst Briefings, Do’s and Don’ts (Pt 2) As analysts, we spend much of our time briefed by software vendors. Last month, Dan and Matt discussed the dos and don’ts of presenting your company and products to analysts, and now, it’s Alan’s turn to pitch in with his free advice on the subject. Show notes for Series 3, Episode 7. Topic 1: New Research! Here’s the intro blog for the new vendor research: New Research . Subscribers have access to all the vendor research, but you can browse the catalog here . Dan’s blog post heavily features Cognaize , as discussed in the previous pod . If you’d like access to the Composable profile, you can download it for free! Topic 2: GenAI and Data: an update Matt’s first blog post from (almost)2 years ago; “AI-Enabled BizApps driving the next wave of adoption .” “The AI with a two-track mind” (AI without access to business data is a “parlor trick”). “Models and the RAG trade” (the 5 micro ages of massive models). Support the show…

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How to train your LLM - what works at vendor conferences and more 34:56

منذ 34 weeks34:56

34:56

The seventeenth episode of the podcast that you know and love as "We Love Ugly Data!" is out; available everywhere you get your podcasts from and, of course, in video form via YouTube (which we've embedded below). As always, it's three topics in (just a touch over) 30 minutes, and show notes are, as usual, included below the embedded video. This time around, Matt and Dan are in the chairs, discussing how to train your LLM, The Usual Suspects (chosen by the usual analyst firms), and Analyst Briefings: Do's and Do not. In this month's episode: Topic 1: How to train your LLM Inspired by a blog post of his from last year, Dan discusses the finer points of how LLMs can be trained to ensure that your IDP processes work better. He also confirms his membership in the court of King Charles, which, to be honest, wasn't necessary. Topic 2: The Usual Suspects Again, inspired by another blog post—this time a bit more recent—Dan wants us to remember to look beyond those in the top right-hand corner of the 2x2 diagram when we're selecting software. It's naturally focusing on IDP again, but the lesson can easily be applied across the board. Matt also suggests that we may not have paid for the associated image, but he turns out to be entirely wrong (so call off the lawyers). Topic 3: Conference programs; what works and what doesn’t? As analysts, we spend much of our time being briefed by software vendors. Here, Dan and Matt discuss the do's and do nots of presenting your company and products to analysts (hint, hint: go to town on talking about case studies and the actual details of projects you've done). https://www.youtube.com/watch?v=-OmIWNC5QsA Show notes for Series 3, Episode 7. Topic 1: How to train your LLM Dan's original blog post "How to Train your LLM." Topic 2: The Usual Suspects Dan's original blog post, "Don't Settle for the Usual Suspects." Support the show…

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We Love Ugly Data! The Deep Analysis Podcast - Episode 16 June 2024 36:32

منذ 36 weeks36:32

36:32

The sixteenth episode of the podcast that you know and love as “We Love Ugly Data!” is out; available everywhere you get your podcasts from and, of course, in video form via YouTube (which we’ve embedded below). As always, it’s three topics in (just a touch over) 30 minutes, and the show notes are, as usual, included in the embedded video below. Matt and Alan are in the chairs this time, discussing how GenAI assistants are becoming agents, how RPA is growing in the shade, and what parts of vendor conferences work (and which bits don’t). In this month’s episode: Topic 1: AI Assistants become Agents For Topic 1, Matt and Alan discuss how after 2023 being the year where everyone launched an GenAI assistant, 2024 is the year when everyone is launching a GenAI agent . And to keep it confusing (or simple?) they’re all called Copilots. The pair discusses the phenomenon, based on the differences, of how the agent approach might suit some mature use cases better than others and how quickly a “decision tree” approach to developing and testing an agent will get really tricky indeed. Also, Matt references Spinal Tap (amazingly, for the first ever time on this pod). Topic 2: Automation; zero spotlights and doing fine? Alan’s been on the road again, most recently visiting Automation Anywhere’s “Imagine 2024” conference in Austin, Texas . In discussing all of that, the pair wonder whether being in the shade suits RPA, given the r ecent strong results for UiPath and general good market health (as predicted by the Task Execution market projections within the 2023 and 2024 editions of the Work Intelligence Market Analysis ). Could it also be that the scramble for GenAI use cases is throwing up good opportunities for RPA that could deliver faster value? Topic 3: Conference programs; what works and what doesn’t? As analysts, we are privileged to attend many in-person conferences and have primo seats and often luxuries like refreshments and power sockets (I know, it’s a gilded existence). Given that experience, Matt and Alan each pick one thing that works well at these conferences and another that should be avoided at all costs. Unsurprisingly, this topic took this edition of the podcast over time. Show notes for Series 3, Episode 4. Topic 1: AI Assistants become Agents Matt’s recent blog post concerns Salesforce’s strategy regarding GenAI assistants and agents. “Copilots may have misdiagnosed the problem; humans don’t do what we thought they did.” Matt’s blog post about how we might be overestimating how much human-like experience we can gain from knowledgebases. This short clip from the film “Spinal Tap” should explain Matt’s reference to lukewarm water. Topic 2: Automation; zero spotlight and doing fine? Alan’s recent blog post discusses his thoughts on having visited Automation Anywhere’s “Imagine 2024” conference . Support the show…

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We Love Ugly Data! The Deep Analysis Podcast - Episode 15 May 2024 33:32

منذ 41 weeks33:32

33:32

The fifteenth episode of the podcast that we know and love as “We Love Ugly Data!” is out; available everywhere you get your podcasts from and, of course, in video form via YouTube (which we’ve embedded below). As always, it’s three topics in (just a touch over) 30 minutes and show notes are, as usual, included below the embedded video. Matt and Alan are in the chairs this time, discussing new vendor profiles and AI readiness. In this month’s episode: Topic 1: 5 New Vendor Vignettes First up, Matt and Alan discuss the latest set of – what used to be called – Vendor Vignettes, that have recently been published ; debuts for ai12z , Docuvela and Reshape AI and completely updated profiles for Appian and Apromore . The pair discuss how these research updates connect to current trends and how they fit into the existing application landscape for organizations who might look to adopt them. Topic 2: 5 Questions for AI Readiness In conjunction with AIIM (Association for Intelligent Information Management) , Deep Analysis (along with Kash Kompella of RPA2AI ) have compiled an infographic that provides organizations with 5 key questions they need to be able to positively answer before they should begin any AI project (especially generative AI). Here Matt and Alan discuss the individual steps, why they are all important and also, why the document sports a cartoon Alan at the center. Topic 3: Why are process tools having such a hard time? Alan’s been on the road a lot in the last few weeks and – as he does – having a lot of conversations with the people he meets. From these conversations, he reckons that as much as Deep Analysis highly rate the importance of Task and Process Mining, they are proving a hard sell in the marketplace (especially Process Mining). Here the pair discuss why that might be, what approaches they’ve heard are being used to offset these challenges and what the future might look like (the real future, not whatever weird and wild guesses Gartner are making this time). Show notes for Series 3, Episode 4. Topic 1: 5 New Vendor Vignettes Alan’s introductory blog post on the latest set of Vendor Vignettes. Topic 2: 5 Questions for AI Readiness Download the AIIM infographic “5 Questions – Is Your Organization Ready for AI?” [COMING SOON] Alan’s recent blog posts; “Orchestrating AI – Appian” and AI Readiness – “Ready for what, exactly?” . Topic 3: Why are process tools having such a hard time? To join the mailing list and receive the newsletter (or challenge us as to how your attempts to popularize Task ad Process Mining are going), contact us . Support the show…

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We Love Ugly Data! The Deep Analysis Podcast - Episode 14 April 2024 35:02

منذ 48 weeks35:02

35:02

The fourteenth episode of the podcast that we know and love as “We Love Ugly Data!” is out; available everywhere you get your podcasts from and, of course, in video form via YouTube (which we’ve embedded below). As always, it’s three topics in (just a touch over) 30 minutes, and the show notes are, as usual, included in the embedded video below. Matt and Alan are in the chairs this time for a Work Intelligence-focused episode. In this month’s episode: Topic 1: Work Intelligence Market Analysis 2024-2029, Out Now! We kick off this episode by discussing the newly released “Work Intelligence Market Analysis 2024-2029,” which is now out for subscribers (and for one-off purchase for non subscribers) . Matt and Alan discuss the constituent parts that make up Work Intelligence and how they fit together before going on to look at some of the biggest revenue contributors to that market and a quick discussion about some of the new data points that have debuted in this new edition of the report (geographic location and company size). Topic 2: Farewell, Workfellow Very much still in the area of Work Intelligence, Matt and Alan talk about the sad demise of one of our favorite Mining Intelligence start-ups, Workfellow . Pulling some further data from the Work Intelligence report, the pair discuss the relative performance and funding in particular for Process Mining and Task Mining vendors and how even that data is somewhat skewed by the presence of one, high earning and fabulously funded player in that sub-market. Topic 3: RAG SLAM Matt has recently published a blog post, “Models and the RAG trade”, discussing how the proposed enterprise use of generative AI has shifted quickly from it being a magic box to how it’s necessary to build and support a lot of information scaffolding around it to produce useful results. Here, the pair discuss the differences between using RAG (Retrieval Augmented Generation) and domain-specific, smaller language models (which you could call a SLAM) and where current thinking on each is likely heading. It also touches on the news – covered in another recent blog from Matt – of the hiring of (well-funded LLM start-up) Inflection’s core team by Microsoft. Show notes for Series 3, Episode 4. Topic 1: Work Intelligence Market Analysis 2024-2029, Out Now! The announcement blog post for the WI Market Analysis report update . Details on how to purchase the report for non-subscribers. Topic 2: Farewell, Workfellow “Workfellow – a sad loss”, Alan’s blog post signaling farewell to one of our favorite start-ups . Topic 3: RAG SLAM Matt’s blog posts, “Models and the RAG trade” and “Generative AI: focus on the consumer and the consumption”. 2020 research paper from Meta/UCL that firs Support the show…

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We Love Ugly Data! The Deep Analysis Podcast - Episode 13 March 2024 34:50

منذ 1 year34:50

34:50

The thirteenth episode of the podcast that we know and love as “We Love Ugly Data!” is out; available everywhere you get your podcasts from and, of course, in video form via YouTube (which we’ve embedded below). As always, it’s three topics in (just a touch over) 30 minutes, and show notes are, as usual, included below the embedded video. Matt and Dan in the chairs this time, in a very IDP focused episode. In this month’s episode: Topic 1: Irrational Exuberance? First, Dan provides an introduction to the IDP Market Analysis that he’s recently completed (and you can purchase copies of right now). The pair then move on to talk about one of the key findings in the report itself, which suggests that the vendors surveyed for the research are very bullish about their projected increases in short-term revenue. Dan balances this against his own market projections and Matt compares start-ups with hungry baby birds (it makes sense in context, trust us). Topic 2: GenAI’s outsized impact Referencing another data point from the IDP report, Dan talks about how GenAI continues to wield a huge influence in the product roadmaps for IDP vendors and points to a couple of recent specific announcements (see show notes below) to illustrate that fact. Matt chips in with a reminder of the fact that metered access to GenAI is coming over the horizon. Topic 3: Data and Research Efficacy Referencing a recent blog post on the subject, Matt and Dan discuss the challenges in gathering and maintaining research data for market analysis reports, ensuring that it is representative and how they go about choosing what is and what isn’t added into the final published analysis. There’s also some insight into who are the real buyers (and readers) of this sort of specialized research. Show notes for Series 3, Episode 3. Topic 1: Irrational Exuberance? Announcing the 2024 IDP Market Report blog post by Dan Purchase the report itself, here. We Love Ugly Data! – The Deep Analysis Podcast: Series 2, Episode 5 previous pod episode where Matt and Dan discuss the history of IDP Topic 2: GenAI’s outsized impact More startups are using GenAI for IDP blog post by Dan Rossum launches its own LLM blog post by Dan Tungsten Automation (Kofax) launches GenAI Copilots for intelligent automation blog post by Dan Topic 3: Data and Research Efficacy Understanding scale and location: Another new lesson from Work Intelligence blog post by Matt Support the show…

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We Love Ugly Data! The Deep Analysis Podcast - Episode 12 February 2024 31:41

منذ 1 year31:41

31:41

The twelfth episode of the podcast that we know and love as “We Love Ugly Data!” is out; available everywhere you get your podcasts from and, of course, in video form via YouTube (which we’ve embedded below). As always, it’s three topics in (just a touch over) 30 minutes, and show notes are, as usual, included below the embedded video. Matt and Alan are in the chairs this time around. In this month’s episode: Topic 1: Don’t Trust in Magic Here, the discussion centers upon Matt’s recent blog post, where he discusses that some organizations expect generative AI to find a role in their working methods without a clear idea up front as to what that might be. During a discussion about how Task and Process Mining can prevent this situation, Alan muses on Free Jazz while Matt worries about the dangers of just giving everybody a hammer to play with. Topic 2: Business Forms & AI Alan talks about the recent news that the business forms industry group BFMA has been folded into the information management association AIIM and how we often overlook how important forms are to how organizations work. Naturally the discussion ends up in a reminder that for many people (customer, works and partners) these forms are the entire interface that they have with important parts of an organization or even their entire experience of working with them. Topic 3: Work & Trust Following a blog post of Matt’s early in January about how the Horizon/Post Office scandal in the UK illustrates how mistrust of workers undermines a lot of what organizations would like to do to improve their ways of working, the pair discuss the topic and remind people that aside from anything else, if you’re in London, you should definably visit The Postal Museum and ride the Mail Rail. Alan goes on to mention situationism because, of course, he does. Show notes for Series 3, Episode 1. Topic 1: Don’t Trust in Magic Matt’s blog post “Don’t Trust in Magic: A new lesson from Work Intelligence” Ethnic Heritage Ensemble Topic 2: Business Forms & AI Alan’s blog post “Business Forms & AI” The announcement of the BFMA joining AIIM Topic 3: Work & Trust Matt’s blog post “Following new horizons through Work Intelligence” The London Postal Museum Guy Dubord Support the show…

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We Love Ugly Data! The Deep Analysis Podcast - Episode 11 January 2024 35:34

منذ 1 year35:34

35:34

The eleventh episode of the podcast we now know and love as "We Love Ugly Data!" is out; it's the first of season 3 and kicks off our 2024! The eleventh episode of the podcast – the first of 2024 and therefore the first of season 3 – that we know and love as “We Love Ugly Data!” is out; available everywhere you get your podcasts from and, of course, in video form via YouTube (which we’ve embedded below). As always, it’s three topics in (just a touch over) 30 minutes, and show notes are, as usual, included below the embedded video. Matt and Alan are in the chairs this time around. In this month’s episode: Topic 1: 2024 is a money year? First up, the pair discuss Alan’s recent 2024 blog opener, in which the changing face of funding, divestitures, and a general subtle change in focus for private equity and venture capital make the year a very interesting prospect. Matt pins the analyst industry with some blame for firms' intense clustering on some use cases, where much of the landscape remains greenfield. Topic 2: Salesforce & Ugly Data Looking back at one of the last sets of vendor announcements of 2023, Matt runs through his analysis of Salesforce’s stated intent to take on unstructured data as a knowledge source for AI, and Alan reflects on how we’re heading into another cycle of discovering this stuff is hard to do well. Topic 3: Time to re-think our life of files? Ahead of the release of the year's first Analyst Briefing and in the midst of its final editing, the pair discuss their thoughts about how files as containers of information aren’t really suitable for most of the purposes we use them for and are frankly really overweight (and not just holiday weight, real weight issues). It also contains Matt’s attempt to drag Forrest Gump into a related analogy, which, in hindsight, was ill-advised. Show notes for Series 3, Episode 1. Topic 1: 2024 is a money year? Alan’s blog post “2024 – A Year of Change for Enterprise Software.” Topic 2: Salesforce & Ugly Data Matt’s blog post “Now They Love Ugly Data Too! Salesforce embraces the unstructured”. Salesforce’s World Tour NYC announcement press release. Topic 3: Time to re-think our life of files? COMING SOON – Analyst Briefing “ECM in 2024 and Beyond – Reframing the Equation” (free registration required for download). The previous “We Love Ugly Data!” episode where Matt and Dan discuss the repetition of “n% of data is unstructured”. Support the show…

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We Love Ugly Data! - The Deep Analysis Podcast - Episode 10 - December 2023 31:43

منذ 1 year31:43

31:43

The tenth episode of the podcast we now know you love as "We Love Ugly Data!" is out! As always, it’s three topics in 30 minutes, but this time, it’s a 2024 Predictions Special – a selection from the full-length Market Trends research paper you can now download – where Matt is joined by Alan and Dan. Show notes are, as usual, included below the embedded video. In this episode, Prediction 1: “Generative AI will face its first cold winter” Matt’s up first, and here is discussed the prediction that right now, Generative AI is facing up to its first cold winter, where customers try to make sense of how this much-hyped technology actually fits into how they actually work. It’s not a litany of negativity by any means; there are some real, tangible ways it’s being used right now (hint: it’s citizen developers again!). Prediction 2: “For the first time in human history, machines will read and process more documents than knowledge workers. Dan’s pitch was that we’re at the tipping point where machine reading of documents will overtake that read by human beings, in large part as the current wave of IDP hits organizations. Prediction 3: “Knowledge Graphs and Data Meshes gain traction” Last up is Alan, and he’s picked out the prediction that Knowledge Graphs and Data Meshes (and Data Fabrics, for that matter) will accelerate in terms of their adoption in organizations as they attempt to coalesce their internal information, not least as it’s one of the significant predicates for the use of AI. Support the show…

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