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Who Is a Data Analyst?
A data analyst is a professional whose job involves retrieving, organizing, and analyzing information from various sources to help an organization achieve business goals. Data analysts use logic, statistical techniques and special types of computer programming languages to turn structured, unstructured, and semi-structured data into information that can be used to make decisions.
Key Takeaways
- Data analysts turn data into meaningful information that can be used to guide decision-making.
- They can help an organization cut costs by identifying inefficiencies, increase revenue by uncovering trends, and mitigate risks by detecting anomalies and forecasting outcomes.
- To be effective, data analysts need to have a strong background in statistics, as well as programming and communication skills.
- Many analysts earn good salaries because data analytics is playing an increasingly important role in almost every sector of the economy.
- While some employers still prioritize formal degrees, especially for roles that might evolve into data science positions, certified candidates with demonstrable hands-on experience can often bridge this gap.
What Does a Data Analyst Do?
Data analysts use logic, statistical techniques, and data-focused programming languages to turn numbers into information that an organization can use to improve workflow and business processes.
In small to mid-size organizations, data analysts may be responsible for everything from data quality to preparing reports for internal and external stakeholders.
In larger organizations, data analysts often have specific areas of expertise in data science, such as data engineering, data architecture, or data visualization.
Data Analyst Types
While job positions for data analysts are often categorized by industry, they can also be categorized by their focus.
For example, descriptive analysts use historical data to answer specific questions about past events, and diagnostic analysts use historical data to try and understand why a past event occurred.
In contrast, predictive analysts use data to project what might happen in the future, while prescriptive analysts use data to recommend actions that will reduce risk and optimize outcomes.
It’s important to note that these four types of data analytics are not mutually exclusive. Data analysts will typically use a combination of approaches depending on their area of specialization and the specific tasks or problems they are being asked to address.
Areas of specialization can also be influenced by an organization’s regulatory compliance burden. In such cases, an organization may require analysts to have both domain expertise and knowledge of applicable data privacy requirements to ensure data practices meet all regulatory standards.
Data Analyst Responsibilities
Data analysts often wear many hats, depending on the size of their company, the purpose of their analysis, and the industry they work in. Ultimately, their most important responsibility is to distinguish between what data is important and what data should be given less weight. This requires domain knowledge. Without this insight, it can be challenging to differentiate between normal fluctuations and significant anomalies that might warrant further investigation.
Data Analyst Responsibilities by Domain
| Domain | Data analyst responsibilities | Key tasks | Tools used |
|---|---|---|---|
| Business analytics | Examine business processes, operations, and strategies to identify opportunities for improvement. | Analyze sales, revenue, and market trends;
Develop business forecasts and recommendations; Collaborate with stakeholders to align analysis with business goals. |
Excel, Tableau, Power BI, SQL, CRM platforms |
| Financial analytics | Work with financial data to evaluate investments, budgets, and overall financial performance. | Create financial models to assess profitability and risk;
Monitor budgets and forecast future financial outcomes; Analyze stock performance and market trends. |
Excel (with advanced functions), Python, SAS (Statistical Analysis System), Bloomberg Terminal |
| Marketing analytics | Evaluate marketing campaigns, consumer behavior, and market trends to improve marketing strategies. | Analyze campaign ROI and conversion rates;
Identify target audience behaviors and preferences; Conduct competitive analysis and market research. |
Google Analytics, Adobe Analytics, Tableau, SQL |
| Operations analytics | Analyze operational processes to improve efficiency and productivity. | Monitor supply chain performance and logistics;
Identify bottlenecks and inefficiencies in workflows; Recommend process optimizations. |
ERP systems, Python, Excel, business intelligence tools |
| Healthcare analytics | Work with healthcare data to improve patient outcomes, reduce costs, and streamline operations. | Analyze patient care data and medical records;
Identify trends in disease outbreaks or treatment effectiveness; Optimize resource allocation in hospitals or clinics. |
SAS, R, Tableau, EHR systems |
| Data Quality analytics | Ensure the accuracy, completeness, and reliability of data used within an organization. | Clean and validate datasets;
Conduct data audits and ensure compliance with data governance policies; Identify and correct inconsistencies in data. |
SQL, Python, Excel |
| Product analytics | Evaluate product performance and user behavior to guide product development and improvements. | Analyze user feedback and product usage data;
Identify areas for feature enhancement or bug fixes; Collaborate with product managers and developers. |
Mixpanel, Amplitude, SQL, Python |
| Quantitative (Quant) analytics | Create and use mathematical models to analyze financial markets. | Develop algorithms for trading systems;
Model risk and forecast stock prices; Use advanced mathematics and machine learning techniques. |
MATLAB, Python, R, C++ |
| Research analytics | Conduct in-depth research and analysis for academic, economic, or scientific purposes. | Compile and analyze data for studies or reports;
Prepare detailed findings for publication; Use statistical techniques to validate hypotheses. |
SPSS (Statistical Package for the Social Sciences), SAS, R, Excel |
| Sports analytics | Analyze sports data to improve team performance or enhance the fan experience. | Track player statistics and performance;
Analyze game strategies and opponent trends; Use data trends to plan new ways of enhancing engagement with fans. |
R, Python, SQL, specialized sports analytics software |
Data Analyst Tools and Techniques
Data analysts use a combination of tools and techniques to work with different types of data and data storage architectures.
The choice of tool or technique is typically based on the specific needs of the analysis, the type of data that is being handled, and whether the data is stored in a relational database management system (RDMS), data warehouse, or data lake.
Here are some of the basic tools and techniques that every data analyst who is job hunting should include in their resume.
Basic Tools and Techniques Used in Data Analytics
| Feature | Tool or technique | Purpose |
|---|---|---|
| Data-focused programming languages | Python (Pandas, NumPy) | Data manipulation, analysis, and cleaning |
| R | Statistical computing and visualization | |
| SQL | Database querying and management | |
| Data visualization | Tableau | Creating interactive dashboards and reports |
| Power BI | Business intelligence and data visualization | |
| Excel (advanced) | Data analysis, charting, and reporting | |
| Data analysis techniques | Statistical analysis | Hypothesis testing and regression analytics |
| Data mining | Pattern discovery and knowledge extraction | |
| Machine learning (basic) | Applying algorithms for predictive modeling | |
| Big data (optional) | Hadoop, Spark | Processing and analyzing large datasets |
| Data pre-processing | Python (Pandas), Excel, OpenRefine | Handling missing or duplicate values and outliers |
| Data collection | APIs, surveys, web scraping | Gathering data from various sources |
| Machine learning and predictive analytics | Scikit-learn, TensorFlow | Automate repetitive tasks |
| Exploratory data analysis (EDA) | Descriptive statistics, correlation analysis, and visualizations |
Identify patterns, trends, and relationships within the data |
Tips to Become a Data Analyst
To become a data analyst, potential job candidates need to have a strong background in statistics. Many analytics jobs require candidates to have a bachelor’s degree in a relevant field like data science or business intelligence. Data analysts should also have experience with query languages like SQL, AI coding tools, and data-focused programming languages like Python and R.
Proficiency in data visualization tools like Tableau and Power BI may not always be a requirement, but they can be useful tools for sharing analysis and building a portfolio that showcases a candidate’s skills. Continuous learning is important in this evolving field, so earning relevant certifications for data analytics and developing soft skills for communication and critical thinking are equally important:
5 Top Industries to Work as a Data Analyst
In the past, when data analysts were looking for work, they often looked for jobs in the insurance industry. This industry, which has traditionally relied heavily on data to assess risks, has often been at the forefront of new data analysis techniques and technologies to improve actuarial modeling and fraud detection.
While big data has expanded the need for data analysts in other fields, the insurance sector still remains an attractive choice for entry-level jobs. Four other popular industries for entry-level jobs in data analysis include information technology (IT), healthcare, e-commerce, and marketing.
Data Analyst Pros & Cons
While data analysts are currently in high demand, this career path has some challenges as well as advantages:
- Data analysts can earn competitive salaries, even at entry-level positions
- The ability to use data to solve real-world problems can be highly satisfying
- This field offers lots of opportunities for specialization and working remotely
- Translating complex data findings into actionable insights for non-technical stakeholders can be challenging
- The expectation to produce actionable insights under tight deadlines can be stressful
- This field is changing rapidly, so data analysts continuously need to spend time learning new tools, technologies, and methodologies
The Bottom Line
In recent years, data has become an important business asset in many industries. In fact, data is so valuable today that it is often referred to as the “new oil.” In this analogy, data analysts, by definition, are the refineries that turn data into useful products.
From identifying customer trends to predicting market fluctuations, data analysts have the skills required to uncover hidden patterns and insights and explain them in a way that organizations can use to make informed decisions, improve efficiency, gain a competitive advantage, and drive innovation.
