What is data analysis?
Data analysis is the process of inspecting, cleaning, transforming, and interpreting data to discover useful information, draw conclusions, and support decision-making. While data analysis is most commonly associated with business intelligence use cases, it has an important role to play across industries and business types.
In this guide, we’ll walk through all the steps of common data analysis workflows, why data analysis skills are important for everyone to practice, and how you can start leveling up your data analytics abilities.
Why is data analysis important?
Data analysis is important for determining strategies and taking actions based on actual evidence. Data analysis skills help businesses and their employees make better decisions by collecting data, uncovering patterns and trends, and sharing these insights across their organization.
What are the stages of data analysis?
When working on data analysis projects, data practitioners take a number of steps to thoroughly uncover insights and communicate them to others at their organization. The data analysis process typically spans the following stages:
1. Data collection
Data sources can vary widely. Whether it’s a spreadsheet, database, or file, the data source often depends on the type of data you want to use to answer the business question at hand. For instance, survey data might be stored in a spreadsheet, whereas data about website visitors could live in relational databases. Sales data might be found in a CRM, but customer satisfaction data might live in a specialized customer surveying tool, while product usage data might live in another dashboard entirely.
Before beginning analysis, data practitioners need to ensure that they have access to all the data they need to answer their questions, and ensure the data is correct. In some instances, they may discover that they have partial or incomplete data, which impacts which questions they can answer, the methods they choose, and how they represent uncertainty in their analysis.
2. Data cleaning and data wrangling
Once analysts have the raw data, they likely need to clean it to fix or remove incorrect, incomplete, or duplicated data. Data cleaning (also called preprocessing) can be done manually in spreadsheets for smaller datasets, but often data analysts use Python, SQL, or R for automated and reproducible cleaning workflows, powered by code.
Some examples of issues that are addressed during the data cleaning process include:
Missing values such as blank or null fields appearing in the dataset that might require imputation and wrangling (e.g., if missing values are entered as -999).
Duplicate entries when the same record appears more than once.
Inconsistent formatting, such as different date formats or inconsistent labeling (e.g., “USA” vs. “United States”) that require standardization to ensure consistent units and formats.
Parsing errors, where fields are read in as the incorrect type (e.g., a product code is read in as a number, when it is actually categorical data).
Additionally, data analysts often need to ensure their data is properly formatted through data wrangling. What is data wrangling exactly? Data wrangling involves getting data into the right shape for further analysis. For example, data wrangling may involve joining data tables deriving new columns, or pivoting data from wide to long format.
3. Exploratory data analysis (EDA)
The next step is to conduct an exploratory data analysis. Exploratory data analysis (often referred to as EDA) is used to dig deeper into the data, looking at the basic structure and patterns in the dataset.
By using a tool like Observable Canvases for exploratory data analysis, data analysts can perform visual data analysis, which merges data visualization and analysis. One of the benefits of visual data analysis is that it allows analysts to more easily discover trends and outliers through the use of charts, tables, and graphics.
By making the data visual, analysts can uncover patterns and anomalies that inspire questions and exploration paths that haven’t previously been considered.
4. Data modeling and data visualization
Once data analysts have the path they want to explore further, they model the data to generate deeper insights or forward-looking predictions. This could include predictive modeling, which involves forecasting based on historical data.
This stage also may include data visualization. As in exploratory data analysis, visualizations help analysts uncover trends and valuable insights that allow stakeholders to make more informed decisions.
5. Interpretation and communication
One of the most important parts of the data analysis process is the interpretation and communication of outcomes or insights to stakeholders and decision makers. While data teams will often package their findings in a report or dashboard that is shared with stakeholders, there are many benefits to bringing stakeholders into the data analysis process more thoroughly through collaborative analytics.
Data visualizations, such as charts and dashboards, help communicate insights to stakeholders in a number of ways. Data visualizations are critical for communicating findings as they can:
Increase interpretability by making the data and analysis pipeline, including with complex data datasets, easier to understand.
Accelerate the discovery of insights by visually demonstrating outcomes and issues.
Improve decision-making by creating shared meaning between team members and stakeholders.
How can you improve the data analysis process?
Incorporate collaborative analytics. Collaborative analytics is an approach to data analysis that engages stakeholders throughout a project. Data analysts who practice collaborative analytics bring engineers, domain experts, and business managers into the entire data lifecycle. This approach leads to stronger outcomes because it incorporates diverse viewpoints and areas of expertise into the process. It also minimizes unnecessary pings, and facilitates faster, more informed decision-making.
Use visualizations throughout the data analysis workflow. Visual data analysis keeps changes to the data front-and-center throughout the analysis process. This helps teams catch mistakes earlier on, discover unexpected insights that might otherwise remain hidden in code, and encourage participation from non-coding stakeholders.
Focus on end-user experience. Large amounts of data can be necessary for communicating insights, but doing so may come at a performance cost. Focus on improving the end-user experience by choosing a data app provider that includes performance optimizations. Consider how your users will access the report or dashboard and take into account how their device type might impact the way you present data. For example, if users may access insights from a mobile device, consider using charts that are suited to mobile experiences.
Work smarter with AI for data analysis. Take advantage of innovations in generative AI to accelerate parts of the workflow. By using specialized AI for data analysis workflows, you can more easily wrangle data, clean data, and highlight insights. Tools like Observable Canvases make it easy to integrate AI into your workflow, without needing to jump between tools or chats.
By understanding the data analysis process, and all the steps involved — from collecting and cleaning data, to exploring and sharing insights — professionals across modern businesses can operate more efficiently, and make more strategic decisions.
Sign up for early access to Observable Canvases to start exploring your data within a real-time, collaborative whiteboard.