Data glossary

Using automated tools and processes to streamline data integration, especially in environments with a large volume of data.

This is the average amount a customer spends per transaction at a retail company. It is calculated by dividing the total revenue by the number of orders. A higher AOV means that customers spend more per purchase.

AOV = total revenue / total number of orders

In 2023, the average is just under €75, which is €4 less than in 2022*.
*(Shoptet, January 2024) 

Other related e-commerce metrics: CAC.

This is the total cost that a company will spend to acquire a new customer. It includes the cost of marketing, advertising and sales. A lower CAC means the company is more efficient in acquiring new customers.   Related e-commerce indicators are AOV.

CAC = amount spent on marketing / number of new customers.

In the e-commerce or retail industry, the average CAC can range from $10 to $30*. However, for more specialized markets or luxury goods, CAC can be significantly higher, sometimes exceeding $100 per customer.
*(Polar Shopify)

Other related e-commerce metrics: AOV.

Using statistical methods, machine learning and other analytical tools to uncover patterns, trends and information in data.

What is Data Maturity?

The Data Maturity evaluates a company's level of competence in working with data. It focuses on how effectively a company collects, processes and uses data to support its decision-making. It is about the extent to which data is integrated into the company's processes, what technologies and strategies it uses, and whether employees are able to work with the data.

Simply put, the Data Maturity shows how data-driven and ready a company is to use data for strategic growth and competitiveness.
 

In order to better understand how companies in the Czech Republic work with data, we at Revolt BI have created a data maturity survey. The survey focuses on the retail, healthcare and manufacturing sectors and includes companies with a turnover of over CZK 500 million per year.

The benefit for each participating company is the opportunity to benchmark themselves against the market, better understand their strengths and weaknesses and get first tips to increase data maturity. This in itself is a step in the right direction on the road to becoming a data-driven company.

Each year we help several companies with their overall data transformation and we believe this survey will help us have an even more global impact.

What is dbt?

dbt, or data build tool, is an open-source tool with a command-line interface that helps organizations build, test, and maintain their data infrastructure. It's kind of like Looker PDTs, but much more than that. It has similarities to Airflow, but is built for a completely different user. dbt is its own unique thing.

The dbt tool allows users to define their data models using SQL and then uses those models to generate optimized SQL code that can be run against a data warehouse or other data storage system. This allows users to build a maintainable and scalable data infrastructure that can be easily updated and expanded over time.

In addition to generating SQL code, dbt also provides a number of features that make working with data easier. These features include the ability to manage dependencies between data models, run tests to ensure data integrity, and track the development line to understand how data has changed over time.

dbt and no-vendor lock: dbt fits well into the modern BI stack and works with products such as Snowflake, Keboola, BigQuery, Looker, Fivetran, Redshift and Mode. With dbt code (stored in Git repositories), developed code can be moved to another platform at any time.

For more information on the business use of dbt, visit our website here.

An Advanced Feature.

Time Travel is a powerful feature in Snowflake that allows users to access historical data at any point within a defined retention period. This capability is particularly valuable for data recovery, auditing, and rolling back changes. It’s available in the Enterprise edition and higher, adding to the cost but providing significant benefits for data governance and management.

TCO in the IT field means the complete cost of investments and their follow-up operation. 
Including not only the purchase price of the technology but also the fees associated with operation/maintenance or the costs required to train users.

5 Whys
A form of root-cause analysis that entails asking “why” (at least) five times until the underlying causes of an outcome are understood.

1. Why did the project fail?
Answer: The project failed due to inadequate planning and lack of resources.

2. Why was there inadequate planning?
Answer: The project team did not have a clear understanding of the scope of the project and did not allocate enough time and resources to complete it.

3. Why did the project team not have a clear understanding of the scope of the project?
Answer: The project team did not receive adequate training and guidance from senior management.

4. Why did the project team not receive adequate training and guidance from senior management?
Answer: Senior management did not provide the necessary support and resources to ensure the project team had the necessary skills and knowledge to complete the project.

5. Why did senior management not provide the necessary support and resources?
Answer: Senior management did not prioritize the project and did not allocate enough resources to ensure its success.