A good data strategy doesn't need to be full of tools or complicated terms. Keep it simple and clear so everyone understands it.
You should make data easy to access based on how your team and customers currently work. Don't ask them to change their habits; instead, integrate the data into their preferred tools.
Your data strategy will always need updates and improvements. Think of it as a living document that evolves to meet the needs of your business and customers.
If your data product isn't making money, it's really just an internal tool. It's important to focus on projects that add real value.
Having a good Business Intelligence team can often bring more benefits than trying to make fancy data products. Simple tools can lead to effective data use.
More data engineers can improve your data platform, but just adding analysts might not directly make your data team better. It's all about how the team fits with the organization.
Data lakes can be convenient but often lead to problems when trying to manage the data effectively. Keeping things simple with familiar tools can help make the data more useful.
Using Dagster and DuckDB allows you to process data efficiently without complicated setups. You can do key tasks like aggregation and data cleaning right in your data flow.
It's important to consider memory limits and choose the right file formats, like Parquet, for better processing. This way, you can keep your data pipeline running smoothly and avoid needless costs.
The corner store model focuses on personal relationships and tailored solutions, while the wholesaler model is more about scale and efficiency. It's important to know what type of service you need for your business.
Consulting firms can operate like either a corner store or a wholesaler, but they can't do both well at the same time. Understanding which approach fits your needs can save you money and frustration.
Often, businesses think they need the efficiency of a wholesaler, but what they really need is the personal touch and problem-solving skills of a corner store. A personalized approach can lead to better outcomes.
The cost of a data team is not just about salaries, but also includes overhead costs like payroll, insurance, and miscellaneous perks, making it a significant investment for a company.
Investing in software tools and systems for data teams can quickly add up, with expenses for cloud warehouses, data pipelines, and usage-based billing models increasing the overall cost significantly over time.
Misalignment within the company around analytical focus, metric definitions, and problem areas can lead to increased costs for data teams, as they spend resources on work that might not directly contribute to improving business operations.
Open Source software allows users to distribute, extend, and alter software for their own use, serving as the backbone of modern software.
The business model of open-core, where core offerings come in open-source and managed versions, presents a unique approach to leveraging the community for distribution and product development.
The dynamics of open-source and open-core business models in the data space highlight the complexities and challenges of transitioning from open-source to building a sustainable business, emphasizing the importance of scale, product category, and community engagement.
Mutual embiggening is a sound business principle where a business grows as its customers grow, creating a win-win situation for all involved.
The general purposes of a data team are to improve business operations objectively, provide a mechanism for monetization, and signal competence to external observers.
Many companies in the data industry have moved towards a usage-based billing model, lacking alignment between customer usage and the value they derive, missing out on the potential for mutual embiggening.