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Introduction: Decoding the Giant As an enthusiastic investor, I enjoy cutting through market noise to think and model what the data actually  says about a business's future. Alphabet (Google) is the perfect case study for this because it is such a complex enterprise. My sources include the Q3 2025 Financial Report  and Gemini 3.0 launch materials, cross-referenced with strategic reporting from Bloomberg and DA Davidson. Finally, I will combine this qualitative research with a quantitative data analysis. Two methodological approaches which I use in the forecast Alphabet's trajectory: Strategic Segment Analysis: Modeling the revenue mix shift from Search to Cloud. Probabilistic Forecasting: Using Meta’s Prophet model to predict total revenue trajectory.  Short Summary: Q3 '25 Revenue broke $102.3B, driven by 33.5% Cloud growth and 15% YouTube growth. The Secret Sauce in AI field:   Googles own custom TPUs  can provide a structural cost advantage, defending gross margins compared to competitors dependent on Nvidia GPUs. The Forecast - future revenue structure:   My probabilistic prediction model (using Meta's Prophet) suggests Cloud revenue will exceed $100B (20% of sales)  by 2028 . Fundamentally reshaping the company’s revenue structure and gradually reducing its dependency on the advertising business. This shift may also influence how the stock is valued in the future. 1. The Strategic Thesis: The TPU Cost Advantage Most analysis focuses on the Front End (Gemini vs. ChatGPT). I believe the decisive battlefield is currently moving also to the "Back End" (Compute Cost). Alphabet’s biggest advantage in AI era may be full stack approach and Tensor Processing Units (TPUs). The Competitors: Microsoft and OpenAI rely heavily on purchasing Nvidia GPUs, paying a significant margin premium. Google Advantage: Alphabet designs and runs its own Tensor Processing Units (TPUs). Google Disadvantage: Google Cloud and TPUs are still quite closed ecosystem. There is always risk that Google ends up an isolated island while rest of the world is building apps using Nvidia standards. Why this matters: As Generative AI shifts from training to inference, compute becomes the primary Cost of Goods Sold (COGS). Google’s ability to access compute "at cost" creates a structural margin defense. This allows them to price aggressively to capture enterprise market share while protecting profitability. There is also rumours that Meta would be interested to buy TPUs. According Bloomberg , "..Meta is planning to use Google’s chips in its data centers in 2027." Comparison of Total Cost per Hour for Google TPUs and NVIDIA GPUs. Source: https://www.rohan-paul.com/p/semianalysis-on-google-tpu-vs-nvidia 2. Search & Gemini 3.0: From Chatbots to Agents Search is not melting; it is evolving. With $56.56B in Q3 revenue (+14.5% YoY), the core business challenges the Zero Click bear case. However, the nature of the product is shifting fundamentally with Gemini 3.0. Shift to Agents: Gemini 3.0 is no longer just retrieving text; it is performing actions (reservations, purchases). This allows Google to leverage its massive ecosystem (Search, Android, Chrome, Maps, Youtube) in a way standalone models cannot. "Circle to Search" : Can be an example of defensive moat is Android integration. "Circle to Search" captures commercial intent even when users are on competitor apps (like TikTok), turning passive consumption into active transaction revenue. A user watching a TikTok video can circle a pair of sneakers and instantly be taken to a Google Shopping. Reality Check: While Google is closing the gap, it is still chasing. Gemini’s 73M monthly downloads still trail ChatGPT’s 93M (Source: Bloomberg ). The company must also manage the higher cost structure of generative answers without eroding margins. 3. YouTube: Media Cornerstone, Data Engine and growth driver While Cloud provides infrastructure growth, YouTube provides media growth. Q3 Revenue of $10.26B (+15% YoY) confirms it is a cornerstone asset. Connected TV:  According to Nielsen data, YouTube is winning Connected TV in the U.S. Youtube will provide growth for long time. It is effectively becoming the new Cable TV. Nielsen: The Gauge - Connected TV Key Strategic Wins: Connected TV:   Nielsen data confirms YouTube is the #1 streaming platform on TV screens in the US. This matters because "tv screen" ad inventory commands higher prices (CPMs) than mobile ads. The Hidden Asset (Multimodal Data):   As AI models become multimodal (Video/Audio/Text), YouTube’s library becomes the world's most valuable training dataset. Shorts monetization:   The company confirmed that YouTube Shorts have reached monetization parity with traditional video. The volume headwind has become a revenue tailwind but TikTok is still controlling mobile. 4. The Revenue Mix Shift (2025–2028) To understand what Alphabet Revenue Mix could look in 2028, I first modeled the near-term baseline using a Segment Analysis. Total revenue hides the divergence between the "maturing" Ad business and the booming Cloud business. The 2025 Baseline (My Estimate): Based on current run rates and Q3 data, I estimate 2025 Total Revenue at $379.5 Billion. Segment My Baseline for (2025) Wall Street Run Rate (Based on Q3 2025) Cloud 60,6 60,8 Other 51,6 51,6 Ads 267,3 285 Total 379,5 397 4.1 Forecasting 2028: The "20% Cloud" Scenario Based on current growth velocities (CAGR), I have modeled the revenue mix for the year 2028 to visualize the company's future structure. Total revenue: The Model in short: Advertising:   The model assumes ad revenue growth stabilizing at around 5%. Under this scenario, advertising would account for only 62% of total revenue in 2028. This would represent a significant cooldown compared to today: for context, Q3 delivered a strong +14.5% growth with no signs of stabilization yet. I believe the share of advertising will gradually decline from the current ~75% to below 70%, shifting Alphabet toward becoming more of an infrastructure-driven company. Cloud:   Cloud revenue is modeled at a ~22% compound annual growth rate, a moderation from today’s rapid +33.5% pace. Under this assumption, Cloud would represent 22% of the revenue mix in 2028. Other:   “Other” segments continue to grow at double-digit rates. While this may give them slightly too much credit, the model results in 16.3% of total revenue coming from Other by 2028. The Result: By 2028, Google Cloud will likely exceed $100 Billion in revenue, representing 20% of total sales. Why this matters for investors: Cloud revenue is recurring and sticky. Wall Street typically assigns a higher valuation multiple to Cloud revenue (e.g., 8x-10x Sales) compared to "Ad" revenue (e.g., 4x-5x Sales). As the revenue mix shifts toward Cloud, the company's P/E ratio could structurally expand. 5. Technical Forecast: Validating with Prophet Methodology: Time-Series Modeling with Meta's Prophet. Year Bear Case Base Case Bull Case 2025 111.2 115.1 118.9 2026 440.3 459.7 479.5 2027 466.1 504.9 546.1 2028 496.0 558.5 628.1 To validate my "20% Cloud" thesis, I built a forecasting engine using Meta´s Prophet model . Unlike standard regression, Prophet is designed for business time-series data and explicitly models uncertainty. The Results (2028 prediction): Bear Case : $496,0 Billion Base Case : $558,5 Billion Bull Case : $628,1 Billion The Verdict: Even in the pessimistic "Bear Case" scenario modeled by Prophet, Alphabet’s revenue trajectory supports the infrastructure pivot. The algorithm identifies a strong underlying growth trend that persists despite potential market volatility. 6. Final Verdict: The Valuation Re-Rating The data points to a clear conclusion: Alphabet is successfully executing a pivot. The Base Case (Cloud Pivot) : As the revenue mix shifts to >20% Cloud, Alphabet deserves a higher multiple. Cloud businesses typically trade at 8–10x Sales, while Ad businesses trade at 4–5x Sales. This alone justifies a significant re-rating. The Bull Case (TPU-as-a-Service) : There is a hidden "call option" in the stock. Analysts at DA Davidson estimate that if Google captures just 20% of the external AI chip market (selling TPUs to giants like Meta), the chip business alone could be worth $900 Billion. This would transform Google into the "Intel of the AI Age." Final Thought: Alphabet is not winning the AI race solely because of a chatbot. It is winning because it is the Full Stack AI Infrastructure: It owns the Chips (TPU), the Data (YouTube), and the Cloud. Crucially, this stack is backed by decades of computing heritage and the world's deepest bench of AI talent (DeepMind). It is this unique combination of proprietary infrastructure and institutional knowledge that creates the ultimate moat for the next decade. If you would like to read more about technical solution, here is the case study more technical approach : Case Study: Strategic Revenue Forecasting for Alphabet (2025–2028) Disclaimer: The content provided in this analysis is for educational and informational purposes only. It does not constitute financial, investment, or legal advice. The forecasts and scenarios presented are based on probabilistic modeling and personal assumptions, which may not materialize. Investing in the stock market involves risk, including the loss of principal. Readers should conduct their own due diligence and consult with a certified financial advisor before making any investment decisions. I am not a financial advisor, and I hold no responsibility for any financial losses or damages resulting from the use of this information.

As I reflect on my journey as a Business Intelligence Analyst, I feel incredibly grateful to have had the opportunity to transition from my B2B sales experience into the data world. Transitioning into the data field is no easy task and finding that first role is always a challenge. I took a leap into the world of data, armed with self-taught skills and certifications. At this moment, I feel very lucky to be able to write down these reflections. My deepest thanks go to the incredible people I had the honor of working with —real experts and heroes- in the data field who generously shared their knowledge and made my learning journey much faster. This role was a significant stepping stone in my career and I hope to capture some of my initial learnings here. It’s worth noting that this reflection is written quite soon after my experience, so I’m sure some of the most valuable lessons will become clearer over time. This position offered me the chance to develop valuable technical skills, refine my problem-solving abilities, and gain a deeper understanding of how data can transform business operations. Additionally, I gained great insights into how data projects are executed using agile methods. The Role As a Business Intelligence Analyst in the transportation industry, my role was focused on providing actionable insights and helping stakeholders make informed decisions. I worked closely with cross-functional teams to understand the data needs of stakeholders, using various BI tools to meet those needs. My responsibilities were rooted in understanding business needs, transforming data into usable information, and building tools to improve organizational outcomes. Key responsibilities included: Data Modeling and Report Development : Using Power BI, I created interactive reports and data models that simplified complex datasets and delivered clear insights. Stakeholder Collaboration : Partnered with internal stakeholders to identify requirements, solve specific business challenges, and tailor data solutions to met their needs. Power BI Environment Management : Ensuring a secure and well-structured data environment. I administered Power BI, managed access and developed the Power BI environment more user-friendly directions. Ad-Hoc Analysis : I provided insights into specific business questions, addressing a variety of topics such as analyzing power outages on the railway, tracking train counts by station, and providing new measures, perspectives on HR-data from different point of view. Encouraging Data-Driven Culture : Providing resources to enable data-driven culture such as report catalog, user manuals and offering a clear contact point for data questions. Process Optimization : Collaborating with consultants and data team members to ensure process were going smoothly. A Day in the Life Working in an agile team   brought structure to my day-to-day activities while providing flexibility to adapt to changing priorities. Daily Standups (9:15 AM) : Our team began each day with a standup to align on progress, share updates, and address challenges. Task Management with Jira : Using Jira, I tracked tasks, created new tasks, updated business needs, monitored progress, and ensured alignment with team goals. Documentation in Confluence : Confluence was our go-to platform for documenting processes and data requirements. This was actually very useful tool and it helps to create guidelines in the projects. Weekly and Monthly Planning : Weekly planning sessions helped prioritize tasks, while monthly backlog refinements ensured our work aligned with long-term business objectives. This agile approach enabled us to stay focused, maintain transparency, and adapt to changing demands efficiently. Sometimes maybe even too efficiently. The Skills and Technologies This role offered a solid foundation in the tools and techniques essential for succeeding in the data field. Key technical skills I gained included: Power BI : Creating dashboards, managing the environment, and transforming raw data into actionable insights. SQL & DAX : Querying and analyzing data, writing efficient calculations, and building measures for reports. Snowflake : Leveraged Snowflake to explore data, perform quick analyses with SQL, and understand the structure of data warehouses, including the location and purpose of various tables. Jira and Confluence : Managing tasks and maintaining clear documentation to streamline workflows. Beyond technical skills, I developed an ability to navigate complex problems, collaborate effectively with stakeholders, and stay focused on delivering business value. The Team: Collaboration at Its Best A key highlight of this role was working within a supportive and collaborative team environment. My team included, head of analytics, data architect, data scientist and BI-specialist. While I focused on analytics and reporting, success relied heavily on cross-functional teamwork, information sharing and active collaboration. Consultants : Data Engineers  ensured the availability and quality of data. BI developers  provided expertise to guide and optimize strategies. Stakeholders : Business Stakeholders  shared insights into their specific challenges, helping to shape the solutions we delivered. Working in this collaborative environment helped build a strong sense of teamwork. It taught me how important it is to communicate clearly, stay flexible, and ensure that our efforts were aligned with overall business objectives. Lessons Learned Looking back, I am incredibly grateful for this opportunity. It was a steep learning curve, but it prepared me for future challenges in the data field. Key takeaways include: Adaptability : Transitioning from sales to data analytics required embracing new tools and techniques while staying open to learning. Problem-Solving : Whether it was debugging data errors or addressing stakeholder needs, problem-solving was central to my daily work. Promoting Data Culture : Building a data-driven mindset across the organization was a big challenge and every interactions is meaningful. Active listening. Looking Ahead My first job as a Business Intelligence Analyst was more than just a career milestone. It was the culmination of hard work and self-driven learning. It provided invaluable lessons, not just in the technical and analytical aspects of the role, but also through the collaboration and insights I gained from the talented professionals I had the privilege of working with. This experience has shaped my approach to solving business challenges and strengthened my passion for the world of data analytics. I’m excited about the new opportunity ahead and look forward to continuing my learning and development journey. Thank you to everyone who supported me along the way. I am truly grateful and hopefully I can help someone in the future. This was an incredible experience, and I hope our paths cross again in the future!

Google Data Analytics Certificate Review of the Third Course: Prepare Data For Exploration The course will provide you with a broader understanding of data collection, data structures, data types, and the generation of data. You'll engage in hands-on practices with SQL in a real data warehouse, specifically BigQuery. Key points covered in this course include: Basics of extracting, filtering, and sorting data from databases/warehouses. Assignments will involve both spreadsheets and SQL. Enhanced comprehension of different data types, formats, and considerations regarding bias in data. Exploration of data ethics and data privacy. The course structure effectively presents important and valuable insights into data. Understanding how data is collected, evaluate data quality, and interpreting metadata are crucial for future learnings. You will also understand better what is metadata, that describes various aspects of the data, such as its source, format, and context. Building a solid foundation in data is the starting point for meaningful analysis . It will help you to make informed decisions, uncover patterns, and extract valuable insights. Hands-on practices play an important role in this course. You'll have access to BigQuery, and the exercises will be done with real public data. Before diving into the practices, you'll receive a brief introduction to relational databases and SQL, including concepts like foreign and primary keys. SQL proficiency is a key skill for retrieving, filtering, and manipulating data from databases. Essential skill, especially for aspiring data analysts seeking their first real job. These exercises provide valuable and practically the best and maybe only way to acquire this new skill. So, go ahead and continue on your learning journey. Relational database contains tables that can be connected with common keys. SELECT courseid, course_name, est_time FROM Google_Data_Analytics WHERE courseid = 3 OR course_name = 'Prepare Data For Exploration'; | courseid | course_name | est_time | | 3 | Prepare Data For Exploration | 25 hours |

Google Data Analytics Certificate Review of the Second Course: Ask Questions to Make Data-Driven Decisions This course highlights the crucial skill of asking insightful questions to guide data-driven decision-making. I think the key skills that participants can acquire: Understanding the Significance of Relevant Questions for Stakeholders: Emphasis on the importance of asking the right questions tailored to stakeholders' needs. Appreciation of Stakeholder Importance and Effective Communication: Recognition of the role of stakeholders and the significance of clear communication in the data analytics process. Basic Spreadsheet Formulas and Data Entry: Fundamental skills in spreadsheet formulas, data entry, and organization techniques. Development of Analytical Thinking: Fostering analytical thinking to enhance problem-solving abilities. The course begins by emphasizing the importance of asking questions and understanding stakeholder needs. It provides a deeper understanding of the Data Analyst role, highlighting the extensive collaboration and communication involved with various stakeholders. The concept of SMART questions is introduced as a valuable tool for crafting effective questions. Problem-solving and asking the right questions are essential skills for any data professional, particularly new Data Analysts. Mastering this aspect can significantly make your work easier. One module dedicates itself to data-driven decision-making, exploring various data types and effective data presentation methods. While this module serves as an introductory segment, it lays the groundwork for future, more advanced topics. What I like about this course, is its early emphasis on communication and asking the right questions. These are fundamental elements of every Data Analyst job. When you can understand your stakeholders needs effectively, you can do your job better and bring key insight and value for any business or problem. This course will approximately take 21 hours and is highly recommended for people who are new to the data field and if you don´t have a lot of experience from communication or understanding business related questions this course will be very useful. -Janne

Google Data Analytics Certificate review This is a short review of the first course, which is called Foundations: Data, Data, Everywhere. This certificate includes eight online courses. If you are new to the data field and don't have much experience or understanding of how to start getting familiar with the data world, then this course provides a great introduction. If you have some experience and have worked with data, then this course may not be very informative. The first course provides a great overview of what the Google Data Analytics certificate will include and the kind of skills you will learn. It also explains the most important tools and key skills for a Data Analyst. You will learn key concepts and get an introduction to Excel and even SQL. I think it's great that SQL is introduced early, and students will understand its role. The instructor was very motivating, and the structure of this course was amazing, especially if you are starting to learn from scratch. I believe the key skills you will learn from this course are: Understanding the day-to-day work of a Data Analyst more precisely Understanding the role of data Developing analytical thinking Understanding the basics and importance of data cleaning, data analysis, and data visualization Introduction to Excel, SQL, and Tableau Most importantly, this is a very motivating course for aspiring Data Analysts, and you should get a good start to your studies by completing the course. The course also reveals how Google will approach teaching on Coursera. Basically, every course will include videos, readings, and assignments. There is also the possibility to chat with other students. This course will approximately take 21 hours and is highly recommended for people who are new to the data field. After the course, you should be very motivated to continue your journey. I personally also like Google's way of structuring courses, so I think you will be happy too if you decide to complete the entire certification, which includes eight courses. -Janne

Hello, This is just a test post to find out how this blog feature works but maybe I try to write something. In my blog, I would like to share things that I have learnt about Data Analytics. Do some course reviews which courses I have find valuable and worth of studying. Pros and Cons. Maybe create learning path which I find good to become Data Analyst if you are interested in the field. I could do interviews with more experienced data pros. Explain some visualizations and key findings? IDK. Most useful Youtube channels? What are benefits for me start writing? I would like to write in English so I get a little bit English practice. Some people could find my text helpful when they are thinking about starting to study data analytics. I could get more connections from the field. Writing is always good way to learn things and explaining your thoughts is useful for your own growth. It could be helpful and benefit my learning too. Currently, I am studying Microsoft Power BI Data Analyst Professional Certificate . I use Power BI daily in my current role and this course is very helpful, if you need to learn Power BI. After finishing the course you also get 50% discount from PL-300 Data Analyst exam which is nice bonus to learn and get discount for next. Now add some nice dog picture to the end and that´s all. -Janne