Analytics should be handled like an assembly line to make it more efficient and accessible. This means creating standard processes to measure and track important business metrics.
Most companies need to focus on basic descriptive analytics, which involves identifying and measuring key metrics. These metrics will help businesses understand what drives their success.
Having well-defined metrics is essential before deeper analysis can happen. Insights from data come from well-measured processes, allowing teams to explore and understand their business better.
dbt Labs has bought Transform, and more companies in the data field might be sold or closed soon. This could lead to big changes in the industry.
Data teams are seen as a 2nd order need for businesses, meaning they aren't absolutely necessary. Companies may cut these teams first when they need to save money.
To get the best value from tools, data practitioners should focus on essential needs rather than extra features. This means keeping an eye on what really matters in the data ecosystem.
Working in data often feels lonely, since a lot of the work is done solo on a computer, but there's magic in that solitude.
Events and communities bring people together, making these lonely moments feel connected and meaningful, especially in the data field.
The joy of working with data comes from the love of the craft itself, not just the outcomes or recognition, and that passion can survive even in tough times.
The dbt meta tag helps document important info about data models. It's a simple way to keep track of data governance like ownership and sensitivity.
Many companies have used the dbt meta tag to enhance their products. Some of these companies have received significant venture capital funding because of these improvements.
Documenting tools and their funding related to the dbt meta tag can inspire others. It shows how small features can lead to big opportunities.
Data quality is all about how useful the data is for the specific task at hand. What is considered high quality in one situation might not be in another.
There are several key aspects of data quality, including accuracy, completeness, consistency, and uniqueness. Each of these factors helps to determine how reliable the data is.
Improving data quality involves preventing errors, detecting them when they occur, and repairing them. It's about making sure the data is accurate and useful over time.