Latest news with #dataanalysis
Yahoo
a day ago
- Business
- Yahoo
This artificial intelligence AIM stock trades with an EV-to-EBITDA of just 4x!
Celebrus Technologies (LSE:CLBS) is an AIM stock with operations spanning artificial intelligence (AI), data capture and analysis, and cybersecurity. While AI stocks in the US often command eye-watering valuations, with enterprise value-to-EBITDA (EV-to-EBITDA) multiples regularly north of 30 times, Celebrus trades with extraordinarily low multiples. Celebrus currently trades at an EV-to-EBITDA ratio of just four times — that's far below the sector average and its US-listed peers. For context, global AI and data giants like IBM, Accenture, and Infosys are valued at 15 to 18 times EV-to-EBITDA, while high-growth names in cybersecurity like CrowdStrike and Snowflake fetch multiples as high as 94 times and 115 times, respectively. The sector average sits around 33 times. So why the discount? Recent trading updates offer some clues. On 22 April, Celebrus warned that full-year 2025 (FY25) revenues are expected to come in at $38.6m, down from $40.9m in FY24. The company cited a slowdown in customer decision-making amid an 'increasingly uncertain' global geopolitical environment as the main culprit. Despite the revenue dip, adjusted pre-tax profits are set to rise to $8.7m, up from $7.6m last year, thanks to higher-margin software sales and tight cost controls. That's certainly positive and something that should be accounted for in forecasting for 2026 and 2027. Building on this, there's certainly cause to believe that Celebrus is undervalued. The company is in great shape financially, sitting on $31m in cash and no debt. This provides a solid buffer to weather near-term uncertainty. But this also contributes to that very attractive EV-to-EBITDA ratio, as mentioned above. The net cash position is projected to reach around $54m by 2027. For context, that's around £41m at the current exchange rate and only £27m below the current market cap. I'd add that it can be a rarity to find growth-oriented small-cap stocks with oodles of cash. Typically, these companies have to use debt to fund growth. That's not an issue here. Despite recent operational weakness — Celebrus shares are down over 40% from their 52-week high and have underperformed the FTSE All Share index by 42% in the past six months — analysts remain bullish. The consensus price target is around 460p, implying the stock could be undervalued by as much as 170%. I think Celebrus Technologies may offer rare value in a space where stocks are typically very expensive. Moreover, with a rock-bottom EV-to-EBITDA multiple, strong cash position, and a pivot toward higher-margin, recurring software revenues, it could be a very interesting prospect to consider. As always, risks remain, especially around customer spending and contract transitions. What's more, as an AIM-listed stock, it may simply be going under the radar. It could be better placed with a US listing. However, some investors will argue that the deep discount may more than compensate for the uncertainty. For now, it's a stock I'm going to watch closely. The post This artificial intelligence AIM stock trades with an EV-to-EBITDA of just 4x! appeared first on The Motley Fool UK. More reading 5 Stocks For Trying To Build Wealth After 50 One Top Growth Stock from the Motley Fool James Fox has no position in any of the shares mentioned. The Motley Fool UK has recommended Accenture Plc, CrowdStrike, International Business Machines, and Snowflake. Views expressed on the companies mentioned in this article are those of the writer and therefore may differ from the official recommendations we make in our subscription services such as Share Advisor, Hidden Winners and Pro. Here at The Motley Fool we believe that considering a diverse range of insights makes us better investors. Motley Fool UK 2025 Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Entrepreneur
3 days ago
- Business
- Entrepreneur
Perplexity Rolls Out 'Labs' to Help Users Execute Projects End-to-End
Labs operates on a 10-minute self-supervised cycle, using a combination of web browsing, code execution, data visualisation, and file generation to build project assets You're reading Entrepreneur India, an international franchise of Entrepreneur Media. AI-powered search engine Perplexity has launched a new feature called Labs, expanding its platform from an answer engine to a hands-on project execution tool. Pro subscribers can use Labs to transform ideas into tangible deliverables such as reports, spreadsheets, dashboards, and simple web apps. The launch marks a significant shift in Perplexity's approach from delivering fast, research-backed answers to actively creating structured outputs. Unlike its existing modes, Search and Research (formerly Deep Research), which are designed for quick analysis, Labs is built for tasks requiring extended engagement and multiple steps. Labs operates on a 10-minute self-supervised cycle, using a combination of web browsing, code execution, data visualisation, and file generation to build project assets. These assets are then automatically organised under dedicated tabs—'Assets' for files like CSVs, charts, and documents, and 'App' for interactive tools such as dashboards or mini web apps. The company said the feature is designed to assist with a range of professional and personal tasks, from business planning and data analysis to content creation and workflow automation. Users can begin by selecting a task from their to-do list or browsing project templates provided in the newly launched Projects Gallery. The feature is currently live on web, iOS, and Android, with versions for Mac and Windows expected to follow.
Geeky Gadgets
3 days ago
- Business
- Geeky Gadgets
Say Goodbye to Complex Excel Formulas : Meet the SCAN Function
What if you could solve intricate Excel problems with a single, elegant formula? Imagine replacing a web of complex, error-prone calculations with one streamlined function that handles it all. Enter the SCAN function—a fantastic option for anyone who relies on Excel for advanced analytics. With its ability to process sequential calculations and automate workflows, SCAN transforms how users approach everything from financial modeling to inventory tracking. Whether you're calculating running totals or tackling corkscrew calculations, this tool promises to simplify your work and elevate your efficiency. It's not just a function; it's a paradigm shift for Excel users. In this exploration, Excel Off The Grid uncover how SCAN works, why it's so powerful, and how it integrates seamlessly with Excel's dynamic arrays and the LAMBDA function. You'll learn how to use SCAN for tasks like cumulative totals, iterative financial models, and even combining multiple datasets for advanced analyses. But that's not all—SCAN's ability to handle dependent, step-by-step computations opens doors to possibilities you may not have considered. By the end, you'll see why this single-cell solution is more than just a feature; it's a tool that redefines what's possible in Excel. Could this be the function that transforms your workflow? Mastering Excel's SCAN Function What is the SCAN Function? The SCAN function processes each value in an array by applying a function that combines the current value with the result of the previous calculation. It requires three key arguments to function effectively: Initial Value: The starting point for the calculation, which serves as the base for subsequent operations. The starting point for the calculation, which serves as the base for subsequent operations. Array: The dataset to iterate through, providing the values to be processed sequentially. The dataset to iterate through, providing the values to be processed sequentially. Function: The operation applied at each step, defining how the current value interacts with the previous result. This structure makes SCAN particularly useful for scenarios where each calculation depends on the outcome of the previous step. Examples include cumulative totals, iterative financial models, or any task requiring step-by-step computations. Practical Applications of SCAN The SCAN function is highly versatile and can simplify a wide range of tasks. Its ability to handle sequential calculations makes it a valuable tool for various practical applications: Running Totals: SCAN calculates cumulative sums by iterating through an array and adding the current value to the previous result. This is particularly useful for tracking progressive totals in datasets. SCAN calculates cumulative sums by iterating through an array and adding the current value to the previous result. This is particularly useful for tracking progressive totals in datasets. Corkscrew Calculations: In financial modeling, SCAN can compute closing balances for one period that serve as opening balances for the next. This iterative process is essential for accurate financial projections. In financial modeling, SCAN can compute closing balances for one period that serve as opening balances for the next. This iterative process is essential for accurate financial projections. Sequential Computations: SCAN is ideal for step-by-step calculations, such as monitoring inventory levels, cash flows, or production outputs over time. By automating these processes, SCAN reduces manual effort and ensures consistency in calculations, making it a valuable addition to Excel's toolkit. SCAN Solves Advanced Excel Problems in a Single Cell Watch this video on YouTube. Check out more relevant guides from our extensive collection on Excel functions that you might find useful. Enhancing SCAN with LAMBDA The integration of the LAMBDA function significantly enhances SCAN's flexibility. LAMBDA allows users to define custom functions tailored to specific needs, allowing more complex and adaptable workflows. Within SCAN, placeholders like 'previous' (representing the prior result) and 'value' (representing the current array element) can be used to create highly customized operations. For instance, you can define a LAMBDA function to calculate weighted averages, conditional sums, or other specialized computations. This capability simplifies complex workflows, reduces formula clutter, and allows you to reuse custom functions across multiple scenarios. By combining SCAN with LAMBDA, you can unlock a new level of precision and efficiency in your Excel calculations. Combining Arrays for Advanced Analyses SCAN's ability to process multiple arrays simultaneously adds another layer of functionality. For example, you can combine inflows and outflows into a single dataset for analysis. This feature is particularly valuable in financial and operational contexts, where multiple variables interact dynamically. By structuring data into arrays, SCAN can efficiently handle intricate relationships and dependencies. This capability is especially useful for tasks such as: Analyzing cash flow patterns by combining revenue and expense data. Tracking inventory changes by integrating stock inflows and outflows. Modeling financial scenarios that involve multiple interdependent variables. This ability to manage complex datasets within a single formula streamlines workflows and enhances the accuracy of your analyses. Integrating Built-in Functions and Simplifying Formulas SCAN works seamlessly with Excel's built-in functions, such as SUM, MIN, and MAX, allowing you to enhance its functionality without additional complexity. Additionally, SCAN supports eta reduction, which enables you to reference a function name directly without defining a LAMBDA. For example, instead of creating a custom LAMBDA for summation, you can simply use SUM as the function argument in SCAN. This feature not only reduces formula complexity but also improves readability, making it easier to understand and maintain your calculations. By using built-in functions alongside SCAN, you can achieve powerful results with minimal effort. Understanding SCAN's Limitations While SCAN is a robust and versatile tool, it does have some limitations that users should be aware of. For instance, functions like COUNT may not behave as expected because SCAN only passes two values—the previous result and the current value—into the function. This can lead to unexpected outcomes if the function relies on additional parameters or broader dataset contexts. Additionally, SCAN's reliance on sequential processing means it may not be suitable for tasks requiring non-linear or independent calculations. Understanding these nuances is crucial for effectively integrating SCAN into your workflows and avoiding potential pitfalls. Corkscrew Calculations: A Key Strength One of SCAN's standout capabilities is its ability to handle corkscrew calculations. These involve iterative processes where the result of one period directly influences the next. For example, SCAN can calculate opening balances, movements, and closing balances in a single formula. This iterative capability is indispensable for tasks such as: Financial modeling, where accurate projections depend on sequential calculations. Inventory tracking, where stock levels are updated based on inflows and outflows. Operational planning, where resource allocation depends on prior usage data. By automating these processes, SCAN eliminates the need for manual adjustments and ensures consistency across calculations, making it an invaluable tool for professionals in finance, operations, and beyond. Media Credit: Excel Off The Grid Filed Under: Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
New York Times
3 days ago
- Business
- New York Times
Trump Taps Palantir to Compile Data on Americans
In March, President Trump signed an executive order calling for the federal government to share data across agencies, raising questions over whether he might compile a master list of personal information on Americans that could give him untold surveillance power. Mr. Trump has not publicly talked about the effort since. But behind the scenes, officials have quietly put technological building blocks into place to enable his plan. In particular, they have turned to one company: Palantir, the data analysis and technology firm. The Trump administration has expanded Palantir's work across the federal government in recent months. The company has received more than $113 million in federal government spending since Mr. Trump took office, according to public records, including additional funds from existing contracts as well as new contracts with the Department of Homeland Security and the Pentagon. (This does not include a $795 million contract that the Department of Defense awarded the company last week, which has not been spent.) Representatives of Palantir are also speaking to at least two other agencies — the Social Security Administration and the Internal Revenue Service — about buying its technology, according to six government officials and Palantir employees with knowledge of the discussions. The push has put a key Palantir product called Foundry into at least four federal agencies, including D.H.S. and the Health and Human Services Department. Widely adopting Foundry, which organizes and analyzes data, paves the way for Mr. Trump to easily merge information from different agencies, the government officials said. Creating detailed portraits of Americans based on government data is not just a pipe dream. The Trump administration has already sought access to hundreds of data points on citizens and others through government databases, including their bank account numbers, the amount of their student debt, their medical claims and any disability status. Want all of The Times? Subscribe.
The National
3 days ago
- Business
- The National
The 'data-driven battlefield' powering change in Formula One
In a sport like Formula One, milliseconds make all the difference. Just to underline that, one of the qualifying sessions in the run up to the last Austrian Grand Prix saw less than 0.8 seconds separate the lap times of the fastest 20 cars. It was a record at the time but you can expect that digit to drop significantly in the coming years. This upsurge in competitiveness comes as a direct result of the correct analysis of the choicest chunks gleaned from a massive upsurge in data collection. AI plays its part. In just a few years, this aspect of the sport has become as crucial to F1 's key players as the drivers themselves. Amazon Web Services (AWS) sits centre stage here, having already played a pivotal role in transforming the racing calendar and acting as the sport's digital backbone. The data it collects and shares has impacted everything from car design and race strategy to fan engagement. These days, race cars are fitted with more than 300 sensors, generating around 1.1 million data points per second. Similarly, there are sensors all around the track at strategic points providing more detail (if any were needed). This colossal amount of information is streamed to AWS, where it is processed and analysed in real-time. Thereafter, each team receives all the data about their own two vehicles, but none concerning their rivals. This allows principals to make split-second decisions on everything from strategy and pit stops to energy deployment and tyre selection, all while the race is happening. The whole process is called predictive modelling and, using historical data and current telemetry, AWS's AI machine learning models are able to predict various outcomes, even the best time for a potential overtaking manoeuvre. 'The partnership with AWS enables us to use machine learning and cloud technologies to improve step-by-step in every department," said Ferrari team principal Frederic Vavaseur. "This can be highly beneficial with everything from product improvements to increasing fan engagement.' Crucially, one of the most compelling innovations is the ability of the system to generate graphics that simulate what would have happened if a team had made a different strategic decision – a different tyre compound choice, say – or even assessing the impact of a driver error. Longer term, the information collated can be used to improve car design, with its ability to run thousands of simulations much more quickly than traditional on-track testing would allow – and also at a fraction of the cost. AWS also offers interactive fan experiences like the Real-Time Race Track online tool, which allows fans to design their own F1 circuits, which are then analysed by company's AI systems to produce projected lap times, top speeds, and even viable race stategies. Spectators can also access F1 Insights, a series of real-time on-screen graphics that appear during broadcasts and provide those watching with information about driver performance, car capabilities, and team strategy. Broadcasters are able to get in on the action, too, as the AWS's Track Pulse tool can quickly mine an extensive stream of historical data to pull up facts and statistics to enliven commentary. It's all about what the company calls intelligent storytelling. Ruth Buscombe-Divey is AWS's motorsports ambassador as well as one of F1 TV's most prominent presenters. She says the changes that have happened within the sport recently are game changers for its future. 'If you applied the technologies that were being used to win races five years ago, it's not going to work,' she says, describing what is happening now as a 'data-driven battlefield' where those involved have to keep pushing both technological advances and how exactly to best use the spiralling amount of information being generated. She sees the changes as crucial to the longevity of the sport. Around 750 million people watch F1 over the course of a season, a figure she cites as being a 'great motivation' in keeping up the momentum to make racing more competitive through the greater use of data. Julie Souza leads sports globally for AWS, driving innovation in data not just in F1 but spectator-led content across the board. She says she is often asked if the influx of new information is going to ruin fans' enjoyment of what they're watching. However, she says it's all about spectators watching sport the way they want to watch it and personalising the experience as much as possible. 'It's very easy for people to go, gosh, this is going to make it too heavy for me,' she says with regard to the recent statistical influx. 'However, if we're talking about this data in a way that alienates, we're doing it wrong. 'The whole point is for this information to make people better understand what they're seeing and enhance their appreciation of the exceptional abilities of the people involved.' The next round of the F1 championship takes place on June 1 at the Circuit de Barcelona-Catalunya in Spain.
