The hottest Business Processes Substack posts right now

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Issue #13 - Defining the Data Operating Model

The Data Ecosystem • 359 implied HN points • 07 Jul 24
💼 Business Data Strategy Organizational Structure Team Dynamics Business Processes Collaboration
  1. A Data Operating Model is key for turning data strategy into action. It outlines how the organization works to achieve its goals using data.
  2. Without a proper Data Operating Model, companies face problems like data silos and short-term thinking. This impacts collaboration and the quality of data solutions.
  3. Successful operating models need to adapt as teams grow and change. They should cover not just team structure but also day-to-day tasks, delivery methods, and oversight.

Beyond Big Tech: The Reality Of Data Engineering Outside Silicon Valley

SeattleDataGuy’s Newsletter • 847 implied HN points • 14 Dec 24
🕹 Technology Data Engineering Big Tech Infrastructure Data Systems Business Processes
  1. Working in big tech offers many advantages like better tools and a strong focus on data. This environment makes it easier to get work done quickly and efficiently.
  2. Many companies outside big tech struggle with data because it's not their main focus. They often use a mix of different tools that don't work well together, leading to confusion.
  3. Without a strong data leader, companies may find it hard to prioritize data spending. If data isn't tied to profits, it's tougher to justify investing time and money into it.

AI's missing middle

The Uncertainty Mindset (soon to become tbd) • 2 HN points • 18 Sep 24
🕹 Technology AI Applications Startups Business Processes
  1. There is a big opportunity for AI startups to create tools specifically for medium-sized businesses in traditional industries. These businesses could greatly benefit from AI solutions that fit into their existing processes.
  2. Many current AI startups focus on becoming major companies but ignore the needs of smaller markets. By targeting medium-sized firms, startups can develop practical AI applications that have immediate positive impacts.
  3. To succeed, startups need better funding and industry knowledge. Creating focused incubators could help bridge these gaps, allowing more startups to thrive and innovate in the AI space for medium-sized businesses.

The Case For An AI Productivity Suite

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 12 Apr 24
🕹 Technology AI Tools Automation Productivity Data Management Business Processes
  1. An AI productivity suite helps people and businesses work more efficiently by combining tools for tasks like data analysis and automation.
  2. It allows users to automate regular tasks, freeing them to focus on more important work, and offers easy customization through no-code options.
  3. These suites also promote teamwork by improving communication and sharing among team members, leading to better project outcomes.

AI Agents: You Can't Have an Orchestra Without A Conductor

Theology • 3 implied HN points • 26 Jan 25
🕹 Technology AI Automation Business Processes Software Integration
  1. To effectively use AI agents in a business, you need a 'Conductor' to coordinate them. Just like an orchestra needs a conductor to keep everything in sync, businesses need someone to ensure AI agents work well together.
  2. Having multiple AI agents can get messy without proper management. You need defined rules and processes so these agents know their roles and responsibilities to avoid chaos.
  3. Using AI can be complicated and can incur costs you might not expect. It's important to be able to track and manage these costs separately to understand if you're really saving money compared to hiring people.
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