A data analyst plays multiple roles on a data team. Data analysts are usually a part of product and marketing teams and have an in-depth understanding of the business. Their role is to analyze data and provide insights to the business. Data analysts are responsible for identifying which business problems matter and what’s feasible to solve them. Analysts are also responsible for communicating findings to the business and ensuring that operations teams aren‘t singularly focused on execution. Data analysts on my analytics teams often have a hard time explaining what they do to business stakeholders. Furthermore, Its often difficult to explain to the data team why what they do is important to the company.
Typically, data analysts work on data teams of fewer than 5 members and at companies sized from 100-250 people. These analysts play multiple roles on the team, which consist of the following categories:
- Business analysis - analyzing the business using SQL / Python
- Engineering - Writing data transformations
- Product Management - Cross functional coordination on data
These roles require different skill sets that make up the data analyst's job. Identifying the options that are available to you and where you wish to pursue your career can be as simple as considering how you spend your time as an analyst. The future of data engineering and data analysis is equally exciting.
Business analysts are usually part of a product or sales team and have an in-depth understanding of stakeholder challenges and constraints. Their expertise allows them to identify which business problems matter and what isn’t feasible to solve. Analysts understand what’s feasible from a business perspective, helping prioritize and focus efforts. They can also communicate effectively across functions and reduce coordination costs. Data analysts, on the other hand, are responsible for building models, performing experiments, working cross-functionally, and sometimes even spiking on more technical projects. These skills allow them to gain the trust of their stakeholders, improving communication across teams and reducing coordination costs. Data analysts are less likely to suggest solutions that are out of touch with business realities. They are also instrumental in helping teams step away from the daily grind and look at opportunities outside of their current scope.
If you spend all your time doing analysis instead of writing code, you might be spending too much time in Excel and not enough time in SQL. You could also be spending too much time on short-term projects with an urgent deadline. If you find yourself doing any of those things, talk to your manager about it. If you feel like you’re becoming a business analyst, then you probably should be.
Data analysts are responsible for writing queries that extract information from databases. These queries may be written in SQL, Python, R, or any other language supported by the database system. They may also be written using tools like Tableau, Qlikview, or PowerBI. Data analysts need to understand the structure of the database and how to query it efficiently. They must also understand the data types available in the database and how to convert them appropriately. In addition, they should understand the basics of relational algebra and SQL. Finally, they need to understand how to write effective queries and how to interpret the results.
Analysts need to understand the business problem they are solving, and then focus on delivering high-quality insights. They should not get caught up in the minutiae of coding and development. Instead, they should think about what the end goal is, and then design the best solution possible.
Data analysts often work in a matrix or pseudo matrix organization, reporting to the data lead but with dotted lines back to the business. Even when working on a centralized data team, there may be multiple non-data teams using the results of the work produced by data analysts to make decisions. This creates a situation where data analysts must manage their workloads. Scope their projects, balance competing demands, communicate across functions to set expectations, and keep stakeholders informed about progress. Engineers usually have project managers who can help with all these tasks, allowing them time to focus solely on the code and architecture while data analysts manage their workloads and scope their projects. Embedded data analysts excel at knowing the next model to build, the dataset to explore, or the business question to answer. These responsibilities can also add a great deal of administrative overhead – more meetings, documentation, and emails, all of them taking away from the core job description of an analyst – analyzing.
Data analytics is a broad term that encompasses many different roles within organizations. Some examples include Data scientists, data engineers, data architects, data analysts, data quality professionals, data curators, data scientists, data visualization specialists, data scientists, data engineers, and data architects. Each role requires a unique skill set, and each role will likely have a slightly different focus. For example, a data scientist may spend more time exploring data and less time presenting findings. On the other hand, a data architect may spend more time designing systems and less time analyzing data. Regardless of what role you play, there are certain skills you should master if you hope to succeed at your current position or advance to a higher level.
Data Analysis is a broad term that covers many different jobs. To perform analysis well and efficiently requires engineering skills, project management skills, and business analysis skills. A Data Analyst must understand the problem at hand, what questions need answering, and how to answer them. He/she should also be aware of the limitations of the available data and the best ways to get around those limitations. Finally, he/she needs to be familiar with the business processes and goals of the organization.
Where to next?
Data analysts are often asked to move around within an organization. Some companies will hire you directly out of college while others may ask you to start at entry-level positions and then train you to become a senior analyst. If you're lucky enough to get hired straight out of school, your salary will likely increase over time. You'll also gain experience working with different types of data sets and technologies. Here are some options beyond data that might make sense to a data analyst:
- Business-Ops: If you love working with numbers and data, then you might consider looking at jobs that require a strong quantitative skillset. Examples of jobs that require a strong quant background include Finance, Quantitative Research, Data Science, or Business Analytics. These jobs often require a bachelor's degree in math or statistics, along with several years of experience.
- Data Science: Data science is not just about statistics. Data scientists need to understand the business context and goals of the problem at hand. They also need to understand the technology stack and tools available to solve the problem. If you are looking for someone who knows how to write code, then this isn’t the role for you. A data scientist needs to be able to think through the problem and come up with the best solution.
- Analytics or Data Engineer: Data engineers are responsible for creating and maintaining databases, data warehouses, and other systems that store and analyze large amounts of data. These systems are often used to help companies make strategic decisions about their products and services. A data engineer may also be called an information architect, database administrator, or data scientist.
- Product Manager: Product management is a job that requires collaboration across multiple functions. It involves working closely with engineers, designers, marketers, salespeople, and other stakeholders to create products that solve problems and delight customers. As a product manager, you will be responsible for creating and maintaining a roadmap for your team. You will also be responsible for defining features and requirements, ensuring that the product meets those requirements, and managing the development lifecycle.
- Data Leader: Data leaders will be responsible for leading teams of data scientists, data engineers and other data members. They will also oversee the development and deployment of data products and services across the enterprise.
Data analysts are responsible for collecting, cleaning, analyzing, and reporting information about the data stored in databases. Data analysts must understand the structure of the database, the types of queries that can be run against the database, and the limitations of the database itself. A data analyst must also be familiar with the language used to query the database and the syntax required to write reports using the database. Data analysts may also need to be familiar with other software packages, such as Excel or SQL Server.