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data analytics
What is data analytics?
Data analytics is the practice of interpreting data to uncover patterns, trends, and insights that can drive better decisions or generate predictions. The process of analyzing data involves using various techniques and tools to clean, manipulate, and analyze raw data, turning it into valuable information.
April 21, 2025
5-minute read
April 21, 2025
5-minute read
Data analytics basics
In today’s world, data is everywhere. However, most data starts out as “raw,” meaning it may be disorganized, incomplete, or otherwise unable to provide meaningful insights for a business. Raw data only becomes useful when someone with data analytics skills gathers, processes, and manipulates the data to get it ready for analysis. Then, the analyst can use the refined data to draw conclusions and make predictions, which help drive more informed decision-making and better outcomes for the business.
For example, an e-commerce company might analyze customer purchasing behavior to personalize recommendations and boost sales by improving the user experience. Businesses that effectively leverage data analytics can turn data into a competitive advantage, making data analysis a valuable function for any business and a crucial skill set for many professionals.
For example, an e-commerce company might analyze customer purchasing behavior to personalize recommendations and boost sales by improving the user experience. Businesses that effectively leverage data analytics can turn data into a competitive advantage, making data analysis a valuable function for any business and a crucial skill set for many professionals.
What is a data analyst?
A data analyst is a professional who collects, manipulates, and analyzes data to help their business or organization make data-supported decisions. An analyst’s work involves breaking down complex data sets, identifying key trends, and communicating findings. Essential traits of a data analyst include:
- Analytical thinking, such as breaking down complex problems into actionable solutions.
- Technical expertise, including proficiency in data analysis tools.
- Statistical knowledge, from basic concepts like averages and medians, through more advanced concepts like correlation, regression, and variance.
- Communication skills, such as presenting findings through visualizations and reports.
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Data analyst vs. data scientist
If you’re looking around at data-related job listings, you may see titles like “data analyst” and “data scientist.” While these titles are similar in that they both work with data, their roles and focus areas differ significantly.
Data analysts mostly interpret existing data to address current business challenges. They use data analysis tools to create reports, generate insights, and provide recommendations. Their work is often rooted in descriptive and diagnostic analysis, explaining what happened and why.
Data scientists, on the other hand, tend to work on more advanced data analytics practices. They may build complex algorithms and use advanced methods like machine learning to predict future trends or model potential outcomes. Their work often involves more programming languages and requires a strong foundation in mathematics and computer science.
For example, a data analyst might evaluate sales data to identify top-performing products or friction points within the sales funnel, while a data scientist might develop a predictive model to help forecast future demand.
Data analysts mostly interpret existing data to address current business challenges. They use data analysis tools to create reports, generate insights, and provide recommendations. Their work is often rooted in descriptive and diagnostic analysis, explaining what happened and why.
Data scientists, on the other hand, tend to work on more advanced data analytics practices. They may build complex algorithms and use advanced methods like machine learning to predict future trends or model potential outcomes. Their work often involves more programming languages and requires a strong foundation in mathematics and computer science.
For example, a data analyst might evaluate sales data to identify top-performing products or friction points within the sales funnel, while a data scientist might develop a predictive model to help forecast future demand.
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Although they are commonly mixed up, the roles of a data scientist and a data analyst are very different.
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Understanding business context is key in data analysis, and a non-traditional background can offer valuable perspective when starting your career.
How data analytics works
A fundamental approach to data analytics has six key steps. These stages often follow an iterative process, meaning that you might go back to a previous stage as you learn more or uncover new information.
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Ask: Whether you’re being asked to undertake an analysis, or you’re initiating it yourself, start by defining the business problem and the questions to be answered. This stage is about understanding the business needs, objectives, and any constraints. The ask step may include:
- Identifying stakeholders and their needs.
- Formulating clear and specific questions.
- Defining project goals and scope.
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Determining the type of analysis needed.
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Prepare: This stage focuses on gathering and organizing the data needed for analysis. It involves identifying data sources, collecting the data, and ensuring it's in a usable format. The prepare step may include:
- Identifying relevant data sources (databases, spreadsheets, APIs, etc.).
- Collecting data from those various sources.
- Assessing data quality and completeness.
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Evaluating any biases that may impact accuracy, reliability, or relevance.
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Process: This stage involves cleaning and transforming the raw data into a format suitable for analysis. The process step may involve:
- Addressing data issues you identified in the “prepare” step.
- Organizing and structuring data for analysis.
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Filtering and selecting relevant data.
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Analyze: This is when the core data analysis takes place. It involves using various techniques and tools to explore the data to identify patterns, trends, and relationships, as well as draw conclusions. The analyze step may include:
- Examining the data using online spreadsheet software like Google Sheets or Microsoft Excel, or using analytical languages such as SQL structured query language (SQL).
- Applying advanced statistical analysis techniques such as statistical regression or machine learning.
- Using data visualization tools to explore data in the form of graphs or charts.
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Identifying trends, patterns, and correlations to draw conclusions and develop hypotheses.
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Share: A skilled analyst will communicate findings clearly and effectively to stakeholders, making conclusions easy to understand, even when informed by a complicated analysis. The share step may include:
- Creating written reports detailing findings, recommendations, and caveats.
- Generating supplementary data visualizations (like charts, graphs, and dashboards).
- Showcasing results in a presentation to stakeholders.
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Conducting follow-up analyses based on feedback or questions.
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Act: This final stage involves putting the insights gained from the analysis into action. It's about using the findings to make informed decisions, implement changes, and solve the original problem or answer the initial question. This may include:
- Implementing recommendations based on the analysis.
- Making decisions based on the data provided by the analysis.
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Creating a dashboard to monitor key metrics or trends.
The stages above will be a helpful framework for answering business questions large and small, but it’s important to be flexible. Not all analytical questions need the same level of depth, and it’s easy to get caught up in creating the perfect analysis that answers every question while the analytical process is only as long as it needs to be.
How to become a data analyst
Breaking into the field of data analytics can seem daunting, especially if you’ve already worked or studied in seemingly unrelated fields. You might be worried that you don’t have the right experience, but the reality is that successful data analysts come from diverse fields and may often have non-traditional backgrounds.
To become a data analyst, you’ll need a combination of training, familiarity with analytical tools, and, ideally, some hands-on experience with real or fake data. While there are many paths into the field, most involve these steps:
To become a data analyst, you’ll need a combination of training, familiarity with analytical tools, and, ideally, some hands-on experience with real or fake data. While there are many paths into the field, most involve these steps:
- Structured education: Training and certificate courses in data analytics can teach you job-ready skills like understanding data structures, manipulating data, and interpreting results. In many cases, these training programs may be all you need to land your first data role, but they can also be a supplement to any existing related or unrelated education you may have received.
- Learning data analysis tools: You’ll also need proficiency in the current software programs used for data organization and visualization, such as standard spreadsheet software like Google Sheets or Excel, and programming languages like SQL or Python.
- Building a portfolio: You can showcase your abilities by working on personal projects, analyzing public datasets or contributing to existing businesses and non-profits. This practical work helps you establish a tangible record of your skills.
- Networking and gaining experience: Networking helps you build connections in the field and may open doors to internships or entry-level roles. Attend industry events, join online communities, and seek opportunities to apply your skills in real-world settings.
Learn more
If you’ve ever wondered how to become a data analyst, the Google Data Analytics Certificate can be your springboard to a new career in data. Our certificate programs are created by subject matter experts at Google and designed to teach you the skills you’ll need to stand out among employers. Through readings, quizzes, and hands-on projects, you'll learn to organize and analyze large datasets, create compelling data visualizations, and solve real-world business challenges.
Alternatively, if you’ve already mastered the basics of data analytics, you might want to consider some of our advanced content, such as Google Advanced Data Analytics Certificate or Google Business Intelligence Certificate.
Alternatively, if you’ve already mastered the basics of data analytics, you might want to consider some of our advanced content, such as Google Advanced Data Analytics Certificate or Google Business Intelligence Certificate.
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