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Data analysis is the process of collecting, cleaning, exploring, interpreting, and communicating data to answer questions and support decision-making.
The process starts with a question.
For example:
Analysts then identify the relevant data, check its quality, explore patterns, apply appropriate analytical methods, and communicate what the results mean.
The goal isn't simply to produce more dashboards. It's to turn data into information people can act on.
Organizations use data analysis to understand what happened, why it happened, and what they should investigate or do next.
Data analysis helps companies track important metrics and understand how performance changes over time.
This may include:
Dashboards can provide visibility, while deeper analysis helps explain why a metric moved.
Companies can analyze customer behavior to identify patterns across:
These insights can influence product, marketing, pricing, and customer-success decisions.
Product teams use data analysis to understand how people interact with digital products.
Common analyses include:
A Product Manager might know that activation has declined. Data analysis can help determine where the decline occurs and which users are most affected.
Marketing teams use data to evaluate:
The goal is to understand where marketing investment is producing meaningful business outcomes.
Data analysis can support budgeting, forecasting, revenue analysis, expense monitoring, profitability analysis, and financial planning.
Finance teams may combine operational data with financial information to understand what is driving changes in company performance.
Sales teams can use analysis to understand:
This helps leaders identify bottlenecks and understand which parts of the sales process deserve attention.
Operations teams use data to find inefficiencies and improve processes.
Examples include analyzing:
Data analysis is essential for evaluating experiments.
Analysts can help teams define metrics, assess sample sizes, interpret results, and determine whether an observed difference is meaningful.
Data analysis combines technical ability with business judgment.
SQL is one of the most important skills in modern analytics.
Analysts use SQL to:
The deeper the role goes into warehouse data, the more important strong SQL becomes.
Raw data is rarely ready for analysis.
Analysts need to identify:
Poor data quality can make an accurate-looking analysis completely misleading.
Exploratory analysis helps analysts understand a dataset before drawing conclusions.
It may involve examining distributions, relationships, trends, anomalies, and segments to determine which questions deserve deeper investigation.
Analysts should understand enough statistics to interpret data responsibly.
Useful concepts include:
The required depth depends on the role.
Visualization turns data into information people can understand quickly.
Analysts use charts, dashboards, and reports to highlight:
Good visualization isn't about fitting as many charts as possible onto a dashboard. It makes the important information easier to see.
Dashboards give teams recurring visibility into important metrics.
Analysts may build them using Tableau, Power BI, Looker, Metabase, or similar platforms.
Technical ability only gets an analyst so far.
They also need to understand the business question behind the request.
If someone asks for “revenue by customer,” a strong analyst should understand why the stakeholder needs the number and whether revenue, bookings, recurring revenue, or another metric actually answers the question.
Companies often know that something changed before they know why.
Root-cause analysis helps break a problem into segments, time periods, channels, cohorts, products, geographies, or other dimensions until the likely explanation becomes clearer.
Cohort analysis groups users or customers around a shared characteristic, often when they signed up or purchased.
It's commonly used to analyze:
Funnels measure progression through a sequence of steps.
Examples include:
Visitor → signup → activation → paid customer
or:
Product page → cart → checkout → purchase
Analyzing where people leave the process can identify opportunities for improvement.
Analysts help teams evaluate A/B tests and other experiments by defining appropriate metrics and determining whether the observed results support a decision.
An analysis isn't finished when the query runs.
Analysts need to communicate:
What happened?
Why does it matter?
What should the team investigate or do next?
That makes written and verbal communication a core analytical competency.
The modern analytics stack usually combines databases, warehouses, programming languages, spreadsheets, visualization platforms, and transformation tools.
SQL remains the foundation of many analytics workflows.
It allows analysts to retrieve and manipulate information directly from relational databases and cloud data warehouses.
Spreadsheets remain useful for:
Strong analysts often move comfortably between warehouse-scale SQL analysis and simple spreadsheet work.
Python can extend what analysts can do beyond SQL and spreadsheets.
Libraries such as pandas, NumPy, Matplotlib, and SciPy support data manipulation, analysis, visualization, and statistics.
R is widely used for statistics, research, visualization, and advanced analytical work.
It's particularly common in organizations or industries where statistical analysis plays a major role.
Tableau helps analysts create interactive dashboards and visual reports from multiple data sources.
Power BI is widely used for business intelligence, reporting, dashboards, data modeling, and Microsoft-centered analytics environments.
Looker connects business intelligence with centralized data models, allowing organizations to create more consistent metrics across reports and teams.
Snowflake is a cloud data platform frequently used to store and query the datasets analysts work with.
Google BigQuery and Amazon Redshift are other common cloud warehouses used to centralize and analyze large datasets.
dbt helps teams transform raw warehouse data into cleaner, documented, reusable datasets.
Analysts working in modern analytics teams may collaborate closely with Analytics Engineers using dbt.
Jupyter notebooks provide an interactive environment for Python-based analysis, exploration, visualization, and documentation.
Data analysis rarely starts with a perfectly prepared spreadsheet.
A typical modern workflow might look like this:
Data analysis sits between the technical data infrastructure and the business decisions built on top of it.
Data analysis capabilities appear across several roles.
A Data Analyst uses SQL, spreadsheets, visualization tools, statistics, and business knowledge to answer questions and communicate insights.
A BI Analyst generally focuses more heavily on recurring reporting, dashboards, metric definitions, and business-intelligence systems.
Product Analysts specialize in analyzing digital-product behavior.
They may work heavily with funnels, cohorts, activation, feature adoption, retention, and experimentation.
Marketing Analysts use data to evaluate customer acquisition, campaign performance, attribution, conversion, channel performance, and marketing ROI.
Financial Analysts use many analytical techniques while focusing more heavily on budgets, forecasts, financial performance, profitability, and investment decisions.
A Business Analyst may use data analysis alongside requirements gathering, process analysis, stakeholder management, and business-system improvement.
Analytics Engineers work between Data Engineering and Data Analysis.
They typically focus on transforming warehouse data into reliable, documented datasets that analysts and business teams can use.
Data Scientists use many of the same foundations but usually go deeper into statistics, predictive modeling, machine learning, experimentation, or advanced analytical methods.
Product Managers also use data-analysis skills to evaluate customer behavior, product adoption, experiments, retention, and product performance.
The two disciplines share tools and methods, but their typical goals differ.
Data analysis usually focuses on understanding business data and answering questions such as:
Data science often extends further into:
A company trying to understand why customer churn increased may need a Data Analyst.
A company building a model to predict which customers will churn may need a Data Scientist.
Data analysis and BI also overlap.
Business intelligence generally focuses more on standardized metrics, dashboards, recurring reporting, and organizational visibility.
Data analysis often involves more open-ended investigation.
A BI dashboard might tell you that conversion fell from 8% to 6%.
A deeper analysis might investigate which customer segments, acquisition channels, products, or funnel steps caused the decline.
Many organizations need both capabilities.
SQL, data cleaning, statistics, visualization, business thinking, dashboard development, exploratory analysis, and communication are among the most important competencies.
Companies use it to measure performance, understand customers, analyze products, evaluate marketing, improve operations, support financial decisions, and investigate business problems.
Not always.
Many analyses can be completed with SQL, Excel, Google Sheets, Tableau, or Power BI.
Python or R becomes more valuable as analytical complexity increases.
SQL is one of the most valuable skills for analysts working with structured business data.
Roles centered primarily around spreadsheets may require less SQL, but most modern Data Analyst positions benefit substantially from it.
Tableau, Power BI, Looker, Metabase, Excel, Google Sheets, and Python visualization libraries are common choices.
The terms are frequently used interchangeably.
“Data analytics” can sometimes describe the broader organizational practice of using data, while “data analysis” refers more specifically to the process of examining data to answer questions.
Data Analysts, BI Analysts, Product Analysts, Marketing Analysts, Financial Analysts, Business Analysts, Analytics Engineers, Data Scientists, and Product Managers all use data analysis to different degrees.
Understanding the analytical skills your business needs helps you determine which kind of data professional should own the work.
If you need someone dedicated to querying data, building dashboards, investigating business questions, and communicating actionable insights, South can help you hire Data Analysts in Latin America.
Schedule a call and find remote data talent in Latin America with South.
