AI Needs Data, Data Needs Informatica, So AI Investors Also Need Informatica

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AI isn’t designed to produce accurate facts… all of its information must come outside sources.So, data, important enough as it is given increasing needs to track and analyze, becomes even more ...

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I suspect the company wasn’t paying LEXIS, Casetext, or any other vendor to license access to their data. I assume the AI was probably scraping from among lower quality case data that’s freely available on the internet. (There’s plenty of it. Legal opinions are matters of public record. LEXIS et. al. do a ton of organizing, tagging, e","collect":0,"end_time":0,"defaultTopTitle":"seekingalpha.com","property":[],"viewcount":null,"language":"en","relate_stocks":{"INFA":"Informatica Inc.","BK4023":"应用软件"},"translate_title":"AI需要数据，数据需要Informatica，所以AI投资者也需要Informatica","themeId":null,"isJumpTheme":false,"ttsUrl":null,"symbols_score_info":{"INFA":0.9},"content_text":"AI isn’t designed to produce accurate facts… all of its information must come outside sources.So, data, important enough as it is given increasing needs to track and analyze, becomes even more critical if we expect AI to produce real benefits.It’s important to have, store and move data… but we also need to manage it.Informatica is a big gorilla in data management.Business model transformation is leaving Informatica with uninspiring numbers today, but it’s trending the right way and shaping up to be a big-time data winner.   Jonathan Kitchen AI still has a reality problem. We’ve been aware of its hallucinations (false answers) for a long time. Back on June 15, 2023, while at my old job, I addressed an especially shameful episode. I discussed Steven Schwartz, the so-called ChatGPT lawyer. He used the app to look for old cases he could show to a federal court as precedents that supported his client’s position. ChatGPT gave great case summaries and citations in legally proper form. But it turned out the cases did not exist. They were AI hallucinations. The Judge sanctioned (fined) Schwartz for that. Disappointingly, that isn't just an early days thing. Just two months ago, I used ChatGPT to find precedents I needed for a federal case I was litigating. I knew better than to rely on the AI. But I figured the app might help me formulate detailed searches on LEXIS and Casetext. Those are the bona fide legal databases to which I subscribe. It helped… a bit. About a third of the cases ChatGPT found were good. The rest were hallucinations! AI doesn’t just mess up legal research. On October 30, 2024, we learned that “OpenAI’s Transcription Tool Hallucinates. Hospitals Are Using It Anyway.” Hallucinations Don’t Mean AI is Buggy… They Come from Absence of Data As the New York Times put it: The technology, called generative A.I., relies on a complex algorithm that analyzes the way humans put words together on the internet. It does not decide what is true and what is not. (Emphasis supplied.) This is critical! AI isn’t designed to give accurate information. For facts, it must access (hopefully legally) data from elsewhere. Generative AI, for example, is about humanlike conversation based on data. If the AI doesn’t have the data it needs, it will hopefully tell you that. But it might not recognize when it sees data that looks as if it might be relevant but really isn’t. It might, therefore, improvise based on what it sees. That’s what hallucinations are. If you use AI for things within its sphere of data access, you’re golden. Otherwise, good luck… I can easily guess why ChatGPT failed Steven Schwartz and was barely middling for me. I suspect the company wasn’t paying LEXIS, Casetext, or any other vendor to license access to their data. I assume the AI was probably scraping from among lower quality case data that’s freely available on the internet. (There’s plenty of it. Legal opinions are matters of public record. LEXIS et. al. do a ton of organizing, tagging, etc. Absent a license from a good vendor, users see a disorganized mess.) I can’t prove ChatGPT wasn’t paying for legitimate legal case data. But I’ve been on LEXIS since the 1990s. So, I think I have an excellent sense of how and where legal case data comes from. (Lawyer-oriented AI offerings I’ve seen deal with documents, not research.) I point this out to drive home how critical good data is to fully and properly functioning AI. On October 20th and 23rd, I published on data-oriented stocks. I covered Snowflake (SNOW) and Oracle (ORCL) respectively. Both are among the leaders in making available insane and still growing quantities of all kinds of data. But it’s not enough to have data. The world also needs Informatica (NYSE:INFA). This company is a leader in data management. Starting Slowly with Salad: A First Impression of INFA Leaves Much to be Desired The stock is about 10% since its 10/21/21 debut. Over that same time span the Invesco QQQ Trust (QQQ) is up almost 30%. The stock’s valuation isn’t shocking. But it isn’t irresistible either. I’m comparing INFA to companies in the SPDR S&P 500 ETF (SPY) portfolio and the Application Software industry. (I compare INFA data to medians. These aren’t impacted by wild distortions often caused by unusual data items, even in big companies that can dominate weighted averages.) Analyst's computations and summary from data displayed in Seeking Alpha PortfoliosHistorical growth seems uninspiring. Analyst's computations and summary from data displayed in Seeking Alpha PortfoliosFundamentals don’t look great. Analyst's computations and summary from data displayed in Seeking Alpha PortfoliosReturns on equity and assets don’t impress. Debt is high. And interest coverage below 1.00 is a red flag. Forward growth rates look ok. Analyst's computations and summary from data displayed in Seeking Alpha PortfoliosBut this doesn’t seem good enough to offset the other concerns. Ditto the quarterly Revenue and EPS comparisons. Analyst Compilation based on Data from Seeking Alpha Earnings and Financials PresentationsAnalyst Compilation based on Data from Seeking Alpha Earnings and Financials PresentationsSo far, the picture hardly seems to depict an AI linchpin. But we’re just getting started… Moving on to a More Interesting Appetizer: A Taste of INFA History This data management company was founded in 1993. It launched its first product in 1996. That was PowerMart. A more advanced version, PowerCenter, followed in 1998 These offerings centered on a process known as ETL. That stands for..   Extract: This means grabbing data from one or more sources.  Transform: Here, data is prepared, unified so to speak, for use. For example, one database may code Male as “1.” Another may code that as “M.” The process chooses which columns to load. De-duplication is part of the process. Calculated values may be created. (For example, raw company data includes Sales and Net Income. Transformations create Net Margin…. That’s as Net Income divided by Sales.) Etc., etc., etc.  Load: Transformed data is then input into a target database.  INFA monetized this the way software has traditionally been monetized. It sold perpetual licensees. Customers then used the software on their premises. They also paid for ongoing service and support. Lots of companies do ETL. But INFA is a big deal in the field. As of this writing, searching on-line educator Udemy (UDMY) for “Informatica” turned up 984 courses in a variety of languages. But the world evolved. In January 2015, activist investment firm Elliott Management disclosed a large position in INFA. It criticized the company for not having done enough to maximize its value. This spurred a go-private transaction later that year. Today’s INFA stock is the one created when the 2015 private equity buyers launched an IPO in 2021. What’s interesting is what happened during the period of private-equity ownership. According to zdnet.com:  Informatica's emergence from private equity comes as the company has transitioned to a cloud-first business where subscriptions have climbed to nearly half of overall revenue. Informatica's journey into private equity has been a familiar one: A company seeks to transform its core business without the pressure of having to deliver quarterly results to Wall Street… As with most PE financing, the objective was to pivot the company's core business. Namely, shift the revenue pipeline from the classic perpetual license and maintenance model to subscription; rearchitect its cloud offering into a modern cloud-native offering based on microservices; embed AI-powered automation; and finally, as icing on the cake, move its cloud pricing to usage-based.  This is a challenging and gutsy strategy. Zdnet.com further states: What that means is that the company shifted new sales to subscriptions, deemphasized professional services (leave that to the SIs), but that much of the installed base held onto their software and is still paying maintenance. (Author’s Note: SI stands for systems integrator.) This transition, still ongoing, is why the numbers I showed above for INFA look so middling. But let’s drill down. INFA today has three ARR (Annual Recurring Revenue) models. Two, Self-Managed ARR and Maintenance ARR are legacy. They relate to the traditional PowerMart/PowerCeter offerings. INFA is actively pursuing the third, Cloud ARR. Notice the growth rates for each. INFA IRTotal ARR is a snooze by AI standards. But Cloud ARR is a whole different thing! Given Wall Street’s current apathy to INFA, we can wonder… Were the private equity owners too hasty when they launched the IPO in 2021? Should they have held on until the transition progressed further? I’ll defer those questions to business/market philosophers and future historians. I’ll just offer the following to summarize INFA’s journey: INFA IRHenceforth, let’s look forward. A Great Meal: INFA’s Place in Today’s Data and AI Worlds INFA today is more than ETL. The company now focuses on its AI-powered Intelligent Data Management Cloud (IDMC). Perhaps the main item of jargon here is “data integration.” The company defines this as “the process of combining data from different sources into a single, unified view.” That sounds simple on paper – to the uninitiated. But it’s not. Simple batch data integration is when “a group of data is collected and stored until a specified amount is gathered, then that data is processed all at once as a batch. Batch data integration is still widely used today when companies don’t have a need to gather and analyze data in real-time.” That’s giving way to real-time data integration. Here, “integration is triggered every time new data is available, bringing the latency (delay) to almost zero.” ETL is part of this. But now, there’s also ELT. In other words, load can come before transformation. AI and “push-down optimization” let companies continually evaluate tasks, resources and costs. That helps them decide whether to transform data before or after loading. Also, possible is “reverse ETL.” Here, “cleaned and processed data from a source like a data lake or data warehouse is ingested back into a business application like Workday, Marketo or Salesforce. From there, it can be used for business operations and forecasting.” At INFA, AI refers to CLAIRE. Page 11 of the latest 10K tells us its features include…  a search engine to rapidly discover data assets; contextual recommendations to match data to business needs; automation of data management operations spanning integration, quality, governance, protection, and matching; natural language processing to auto-generate business rules for assessing and auto-deploying data quality assessments and reporting measurements; and intelligent and automated matching and merging of related datasets designed to build a single source of truth at the petabyte scale.  CLAIRE GPT, for generative AI, gives users the ability to  learn from feedback from users and continuously adapt to generate more accurate and relevant responses; use natural language to find trusted datasets, explore relevance and understand business context; and generate pipelines, recipes, rules, connections, glossaries, policies, and datasets from simple text.  The big question, obviously, is whether or not INFA is actually good at the things it says it does. I’m not a data professional. So, I can’t give you a first-hand answer. But there are others out there who can critically evaluate INFA relative to competition. Gartner is one such organization. Forrester is another. The presentations below compare many competitors across important characteristics. The best are plotted in the upper right. (The worst are in the lower left.) Informatica is consistently in the most upper right position, or very close to it. If you’re unable to clearly see details from images used in this article, you can get the original presentation deck here from the Informatica site. The Gartner image is slide 12. The Forrester image is slide 13. INFA IRINFA IRIf you search for a list of Informatica competitors, you’re likely to see major database companies and hyperscaler cloud firms among them. If so, don’t panic. Lots of firms provide various forms of data integration. But INFA is the big gorilla. And the big-name companies work with Informatica, not necessarily against it. INFA IRINFA already has a very impressive customer base: INFA IRAnd it's still gaining more traction in the marketplace. INFA IRINFA’s addressable market is large and growing. INFA IRIn sum INFA operates at a vital spot in the overall data, big data, and AI ecosystems. You may not hear much about INFA today (except if/when analysts complain about “pimple on an elephant” imperfections in quarter-to-quarter trends — see below). But a few years down the road, you’ll be hearing a lot more about it. Helping Us Digest INFA’s Numbers When I see middling or worse numbers today, I can’t be bullish unless I see progress on a path forward. For INFA, I do. Here’s a summary of key aspects of INFA’s financials. These go back to 2018, before the company’s late-2021 IPO. Analyst compilation based on data from Seeking Alpha Financials presentationsA doctrinaire spreadsheet-jockey might complain that the company’s gross margin has trended lower. But the world is harsh. Customers have their own profit/spending goals. And the business mix is changing. I’m OK with what I see. Look now at operating margins. Sadly, GAAP accounting rules don’t require companies to report fixed costs versus variable costs. Generally, though, I use Cost of Sales as a rough proxy for variable costs. I treat Selling, General and Administrative and R&D costs as more or less fixed. That makes operating income (Gross Profit minus SG&A and R&D) and Operating Margin very important. INFA IRI love INFA’s operating-margin trend (which we also see in the above table)! That’s what we want to see as an emerging company grows into and beyond its fixed costs. That’s how newer money-losing businesses eventually turn profitable. Also, what’s not to love about the trend in Cash plus Cash Equivalents plus Short-Term Investments! I can’t say the Debt to Capital ratio is lovable. But its downtrend is at least likable. I still hate Interest Coverage being less than 1.00. But it’s getting close to that minimum threshold. INFA has ample cash to continue to cover shortfalls. And it's definitely trending in the right direction. Stock Compensation Expense is big relative to Cash from Operations. As I discussed on 10/20/24 in connection with SNOW, this is part of the emerging company scene. I don’t love this. I think of it as an ordinary next-door neighbor. We don’t socialize. We don’t feud. We just co-exist. I’m not suggesting anyone should happily digest INFA’s here-and-now financials. What’s appealing are the trends. Risk INFA is still going through what it hoped to avoid when it went private in 2015. It’s still selling its transformation to customers. And the revenue/profit models continue to reflect this change. We’re apt to hear more about Wall Street impatience every time INFA publicly guides or reports results. And every such occasion poses risk to the stock. Here’s a recent example from the 10/30/24 earnings call. INFA benefits as established on-premises customers migrate to its new platform. And, of course, it gets customers that are completely new. One analyst asked a question relating to the latest quarter… around migrations versus net new ARR. I mean what many investors like about Informatica is you have both of those kind of pickers. But specifically on the migrations, the disclosure Informatica provides this quarter, on a TTM basis, 24% of new ARR from migrations, very impressive, but I guess a slight downtick versus last quarter….Like I would have expected a net uptick. CFO Mike McLaughlin answered as follows:  Yes, sure. It's quarter-to-quarter variability, Patrick. I mean, your math is right. It wasn't a blowout quarter for migrations. But some will be blowout, some will be a little bit below expectations. But over the year, we continue to see really strong acceleration. And continue to expect a contribution of migrations to the total net new ARR in the cloud in the 25% to 30% range. for the year. And over time, we would expect that to grow to maybe as big as 1/3 of the contribution. But as I've said before, the most important value creator for Informatica shareholder is winning net new, winning new customers, winning new workloads from existing customers, Migration is great, and it's going to provide a very long tail of opportunity to increase the value footprint with our customers and therefore, our shareholder value. But the net new is doing what we expect it to is the strong majority of what we do, and the migration continues to be healthy, but you'll see some quarter-to-quarter variability.  (Emphasis supplied.) Investors here really need to look beyond instances in which analysts get freaky about pimples on elephants. And, as discussed, financial risk is noteworthy. To invest here, you need to be comfortable with the picture I presented above. You’d need to embrace the basement kid that’s finally stepping out into the world at large. Dessert: What to do About INFA Stock As you saw above, the valuation ratios do not impress. The only quantitative points I can make are with the trends in operating margin and cash flows. But those aren’t small potatoes. They open the door to serious consideration of qualitative thoughts. It’s about what the business looks likely to become. (Popular tech names that crashed in the past were burning, rather than generating, cash.) Let me call INFA an AMZN-SNOW type investment case. I suggest reviewing my 7/13/24 writeup of Amazon.com (AMZN) and my admittedly unconventional 10/20/24 Snowflake (SNOW) Buy recommendation. How-to books, web tutorials, and academic programs teach us to justify investments with numbers. Normally, I agree. But the most important aspect of investing by numbers is recognizing when they can’t work. See, for example, my 5/28/24 Nvidia (NVDA) article. As we learned in and around 1999-2001, inapplicability of numbers is NOT a license to chase anything that sounds hip. Instead, it’s a cue to look critically to truly understand what a company’s business can become. I believe INFA passes this latter test. Here’s the stock’s price chart. StockCharts.comThe 10-day exponential moving average (EMA) is above the 50-day EMA. But it’s not so by enough to let me call this a momentum play. The Street’s underwhelming response to the latest earnings report suggests it may move closer to the 50-day line. Chaikin Money Flow (CMF) and the Chaikin Oscillator (CO) give a mixed picture. Both measure which party to trades is more motivated. CMF does it for institutional investors. CO does it for the market in general. The CO tells us buyers are apathetic. Interestingly, though, CMF shows us that institutional investors are more motivated to accumulate stocks. But that may not persist since INFA hasn’t won any guidance-game trophies lately. I’m OK with the chart. I’m not a dogmatic technical analyst. Given the overall story here, I’m satisfied to see that the chart doesn’t show serious Wall Street hate. As I’ve said before, my investment stance depends mainly on whether I think a stock will be better than, in line with, or worse than the market. Here’s how I apply that to the Seeking Alpha rating system:  “Strong Buy” means I see the stock as being better than the market, and I’m bullish about the direction of the market. “Buy” means I see the stock as being better than the market, but am not confident about the market’s near-term direction. “Hold” means I see the stock as moving in line with the market. “Sell” means I see the stock as being worse than the market, but am not confident about the market’s near-term direction. “Strong Sell” means I see the stock as being worse than the market, and I’m bearish about the direction of the market.   Based on this scale, I’m rating INFA as a “Buy” for those who don’t feel a pressing need to trade out soon. 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