Three Software Powerhouses of AI - Snowflake, Palantir, and Databricks

 Let's break down how Snowflake, Palantir, and Databricks work together in the AI world, using a technology stack analogy and real-world examples.

The AI Technology Stack

Think of building an AI-powered company like building a house. You need a solid foundation, a smart design, and skilled builders.

1. Foundation (Data): Snowflake

   • Layman's Terms: Snowflake is like the concrete foundation of your AI house. It stores all your data in one organised place, making it easy to access and use. It's not just storage; it's like a super-organised library where any information can be found instantly.  
   • Technical Function: Snowflake is a cloud-based data warehouse. It allows companies to store vast amounts of structured and semi-structured data, making it readily available for analysis and AI model training. It handles the messy work of data organisation and access.  
   • Example: Imagine a retail company. Snowflake stores all its sales data, customer information, inventory levels, and even website traffic data. Because it's all in one place and easily accessible, the company can quickly analyse what products are selling well, who their best customers are, and how to optimise their inventory.  

2. Design (Intelligence): Palantir

   • Layman's Terms: Palantir is like the architect of your AI house. It takes the data from Snowflake and uses it to design intelligent systems. It helps you understand what the data means and how to use it to make better decisions. It's like turning raw data into actionable insights.
   • Technical Function: Palantir is an operational platform that connects data, analytics, and operations. It uses AI to analyse data from Snowflake (and other sources) and create visualisations, dashboards, and predictive models that help businesses make better decisions. It focuses on turning data into action.  
   • Example: Using the retail company example, Palantir can take the data from Snowflake and build a model that predicts which customers are most likely to buy a certain product. It can then automate marketing campaigns to target those customers, increasing sales. Or, it can analyse supply chain data to predict potential disruptions and suggest alternative suppliers.  

3. Builders (AI Development): Databricks

   • Layman's Terms: Databricks is like the construction crew for your AI house. They use the data from Snowflake and the designs from Palantir to build and maintain the actual AI systems. They're the experts who know how to put everything together. They keep the AI models up-to-date and running smoothly.
   • Technical Function: Databricks provides a unified analytics platform for data science and machine learning. It allows data scientists to build, train, and deploy AI models at scale. It offers tools for data engineering, model development, and MLOps (machine learning operations).  
   • Example: For our retail company, Databricks would be used to build and train the AI model that predicts customer behaviour. They would use the data in Snowflake and work with the insights provided by Palantir to create a model that is accurate and effective. They would also manage the ongoing maintenance and updates to that model.

Diagram of the Stack

+-----------------+
|   Applications   |  (e.g., Marketing automation, Supply chain optimization)
+-----------------+
|   Palantir      |  (Intelligence Layer - AI-driven decision making)
+-----------------+
|   Snowflake     |  (Data Layer - Unified data storage and access)
+-----------------+
|   Databricks    |  (AI Development Layer - Model building, training, deployment)
+-----------------+

Example Flow

1. The retail company stores all its data (sales, customers, inventory, etc.) in Snowflake.  
2. Databricks uses this data to build an AI model that predicts which customers are likely to buy a new product.
3. Palantir takes the output of this model and uses it to create targeted marketing campaigns.
4. The results of these campaigns (new sales, customer engagement) are then stored back in Snowflake, and the process begins again, allowing the AI models to continuously learn and improve.

In short, Snowflake provides the data, Palantir provides the intelligence, and Databricks provides the tools to build and deploy the AI systems that drive the AI-native enterprise. They are the essential components for companies looking to leverage AI effectively

Elon Musk Effect on Twitters Business and Brand Equity

 

Effect on Share Price, Twitter Business, Employees, CEO and Exec Team

• Elon Musk who leads Tesla and Space X announced buying Twitter for $44 Bn supported by venture capital firm Andreessen Horowitz, the crypto exchange  Binance, and Oracles CEO Larry Ellison. This helped the falling share price to rise again. 
• On July 10 he announces to pull out because of a lack of clarity on users (primarily bots) and financials and since then the price has fallen back to the same price of $33 when the deal was announced to acquire.
• Prior to this deal, the share price was in decline from early 2021.
• It is likely that it now goes into litigation and worst for Musk will be to pay $1Bn in the penalty, but it has a detrimental effect on Twitter's business, employees (morale), and more importantly, its brand equity will take a severe hit, resulting in further decline.
• Twitter CEO and his executive team will not survive this fiasco.

Weekend Reading

Weekend Reading

• What history tells you about post-pandemic booms - read here 
• Voyager Digital: 18,000% Revenue Growth And Better Interest Than Coinbase - more here 
• Funding Friday: Pattern Alphabet cards for exploring nature - more here
• Making large-scale, functional, electronic textiles - more here

Report - Sensis and the Australian Search and Directories Market

I have undertaken this report to explore, learn and analyse the local online search and directories market to understand the developments that are taking place in this area. More than 18 players, from small and medium enterprises (SMEs) to large corporations trying to get some share of this market.

The main focus of this report is on Sensis and its competitors and how it can reinvent itself in a rapidly changing local market.

I would like to thank Mark Rimmer from Rave About It and Meg Tsiamis from dLook for providing a lot of invaluable information, insights and help while preparing this report. Read it here or download it from here.