Review tasks and tools for data analysts
Data analysts discover and present insights in data. In an enterprise, analysts work with data that has been prepared and stored by the data engineer. A data team working together can surface insights from large volumes of data and enable the business to regularly make data-informed decisions.
The data analyst role can be broad. Analysts may sit in the business, with domain expertise, and focus on producing actionable reports for decision makers quickly. This individual may be referred to as a Power BI data analyst.
Alternatively, an analyst may sit in the IT department, and be responsible for more complex data modeling, with a focus on creating scalable, reusable assets. This individual may be referred to as an enterprise data analyst. The enterprise data analyst may provide technical assistance to the Power BI data analyst.
Note
Depending on the size and makeup of your organization, data analyst roles may look different. There may be distinct roles for data visualization, data modeling, development, and/or administration and governance of the analytics platform. In a different organization, there might be a single person managing all analyst responsbilities.
Data analyst tasks
Data analysts collect and transform data to generate insights. In addition to digging into and analyzing data, analysts must be able to present data to stakeholders in a way that enables decision making.
Analyst tasks throughout the analytics process include:
- Collect and clean data for analysis
- Ingest, transform, and model data in a reporting tool
- Generate data products, dashboards and reports
- Communicate findings with relevant stakeholders
- Monitor usage of analytics solution
- Gather and incorporate feedback and additional requirements
Note
See Understand concepts of data analytics for more information on the analytics process.
Data analyst tools
There are hundreds of tools that analysts may use to clean and analyze data. Here, we'll focus on Microsoft tools used in an enterprise analytics solution.
Data analysts often use tools like Microsoft Excel, Power BI, and Azure Synapse Analytics to build analytics solutions. Excel may be used for one-off analysis, but when it comes to enterprise analytics solutions analysts are most often querying data from sources like Azure SQL databases or Azure Synapse Analytics.
The Power BI data analyst and the enterprise analyst will likely use similar tools and have similar skill sets, with one major distinction. An enterprise data analyst will be working with data at scale, and is more likely to be working with tools that can handle larger data.
For example, it isn't uncommon for companies today to have petabytes of data across the organization. Analyzing and extracting insights from such massive amounts of data used to take hours, if not days. By using tools like Azure Synapse analytics, this processing time can be brought down to minutes. Massive amounts of data are likely to be handled by the enterprise data analyst.
In addition to technical knowledge of analytics tools, it's critical that analysts have foundational knowledge of relational databases, basic statistics, and data visualization.
Note
Learn more about foundational knowledge at Explore core data concepts.
The final skill set that makes for a great analyst are soft skills that enable decision making including:
- Communication
- Process management
- Problem solving
- Collaboration and team work
- Creativity
Successful data analysts need a combination of technical and soft skills to solve problems and deliver actionable insights.