Brief Summary
This video explains the role of a data analyst in answering business questions using data. It describes how data analysts bridge the gap between raw data and business decisions, especially in modern big companies. The video also covers the importance of data architects, data engineers, BI developers, data scientists, and ML engineers in building a scalable data system.
- Data analysts answer business questions using data.
- Modern data teams consist of data architects, data engineers, BI developers, data scientists, and ML engineers.
- Scalable data systems are crucial for big companies to automate data flow and decision-making.
Intro
The video introduces the role of a data analyst as someone who answers business questions using data, acting as a bridge between raw data and real-world business decisions. Managers and stakeholders need to make smart decisions by asking critical questions, but without data, they often rely on opinions and outdated information. Data analysts gather data from various sources like databases, spreadsheets, and APIs, then clean, structure, and transform it to answer business questions. They turn data into visual reports to communicate insights to managers, enabling them to make informed decisions.
Data Analyst Skills
The video highlights the essential skills a data analyst needs, including querying databases using SQL, mastering spreadsheet software like Excel for data cleaning and calculations, and data visualization skills using tools like PowerBI or Tableau to create charts and visuals. Effective communication skills are crucial for understanding business questions from managers and presenting findings in a compelling narrative. Mastering these skills enables data analysts to assist companies in making smart decisions.
Scaling Data for Big Companies
The video addresses the challenges of manually extracting and analyzing data in large companies that generate big data. This process can be time-consuming and inefficient, leading to delays in answering critical business questions. To scale data operations, companies hire data architects to design scalable data systems and data engineers to build data pipelines. These pipelines automate the movement of data from various sources to a structured data system, making it easier for data analysts to access and analyze data quickly.
Data Architect and Data Engineer
The roles of data architects and data engineers in building a scalable data system are explained. Data architects design the blueprint of the system, organizing data into multiple layers such as bronze (raw data), silver (clean data), and gold (structured and optimized data). Data engineers build data pipelines to automatically move data from various sources to these layers, ensuring data is cleaned, structured, and optimized for quick analysis. This setup streamlines the data analyst's work, allowing them to focus on answering business questions efficiently.
Business Intelligence (BI) Developer
The video discusses the role of a Business Intelligence (BI) developer in automating and scaling data reporting. When certain reports deliver significant value and are needed regularly, the BI developer steps in to automate the process. They build a secure, accessible environment where interactive dashboards and reports are hosted. These dashboards are connected to the gold layer of the data system, ensuring fresh data is automatically updated. This automation eliminates the need for data analysts to generate the same reports manually every day, saving time and reducing stress.
Data Scientist and ML Engineer
The video introduces the roles of data scientists and machine learning (ML) engineers in addressing future-oriented business questions. Data scientists build and train models to predict future outcomes, enabling managers to make proactive decisions. ML engineers then deploy these models into production environments, making the results accessible through applications or dashboards. This setup ensures that everyone in the company benefits from advanced predictions, supporting better decision-making and strategic planning.
Modern Data Team Structure
The video summarizes the structure of a modern data team, highlighting the collaborative relationships between different roles. Data architects work with data engineers to build the data system, data analysts collaborate with BI developers to create reporting systems, and data scientists partner with ML engineers to develop advanced analytical systems. The team includes roles focused on design and discovery (data architects, data analysts, data scientists) and roles focused on building and deployment (data engineers, BI developers, ML engineers), creating a comprehensive and scalable data ecosystem.
