Organizations are highly relying on the outcomes of data analysis to make critical business decisions. Data Analysts have the efficacy to deal with massive amounts of data and perform statistical analysis to derive key performing factors. Typically, Data Analysis comprises various stages where necessary analyses are carried out to uncover solutions to business problems and help create custom solutions for different use cases.
This article provides a comprehensive overview of the role and responsibilities of Data Analysts. It also explains the importance of Data Analysis imperative to other roles and processes involved while performing Data Analysis.
Table of Contents
What is Data Analysis?
Data Analysis is a process of exploring humongous datasets to identify patterns, discover correlations within features, and derive useful insights for making impactful business decisions. Understanding the importance of Data Analysis, several companies have a team of analysts who assist in decision-making, optimize operations, and design effective marketing strategies.
Who is the Data Analyst?
A Data Analyst collects, purifies, and examines data sets to aid in problem-solving. Here’s how to set out on the road to becoming one. In order to provide an answer or find a solution, a data analyst gathers, purifies, and analyses data sets.
In order for stakeholders to understand data and use it to make strategic business decisions, the data analyst acts as a gatekeeper for the organization’s data. A bachelor’s or master’s degree in analytics, computer modeling, science, or math is needed for this technical position.
What are the Responsibilities of Data Analysts?
A data analyst is a person whose role is to collect and analyze data in order to address a particular issue. In addition to spending a lot of time with data, the role also requires communicating results.
Many data analysts work as follows on a daily basis:
- Gather data: Analysts frequently do their own data collection. This might entail conducting surveys, monitoring website visitor demographics, or purchasing datasets from data collection experts.
- Clean data:Duplicates, errors, and outliers could be present in raw data. In order to prevent inaccurate or skewed interpretations, cleaning the data refers to maintaining the quality of data in a spreadsheet or through a programming language.
- Model data: This entails developing and designing a database’s structural elements. You can decide which data types to store and collect, how to relate different data categories to one another, and how the data will actually look.
- Interpret data: Finding patterns or trends in the data will help you interpret the data and provide an answer to the question at hand.
- Present: An important aspect of your job will be to convey the findings of your research. You accomplish this by assembling visual aids like graphs and charts, producing written reports, and delivering information to interested parties.
What are the Skills Required by Data Analysts?
Data Analysis is a process, which requires a combination of soft skills and technical skills. To excel in this discipline, one may need natural flair in various tools and a will to grasp the importance of detailing in business operations. Some standard tools used by data analysts are as below:
- Excel: MS Excel is a necessary tool to perform Data Analysis. It converts raw data into readable format. Excel comes with a wide range of features from sorting, conditional formatting, and pivot tables. This tool helps in extracting significance from a large detailed dataset.
- Python: The application of the Python programming language has no constraints. It has gained popularity in data analysis due to the massive range of libraries, including pandas and NumPy. Python packages are helpful in Data Manipulation, conducting complex mathematical functions, and Data Visualization. Recent developments with libraries like Dtale and autoviz have helped Data Analysts to automate EDA processes for optimizing results.
- R: It is a programming language for statistical and graphical analysis. R is an open-source tool specifically designed for Data Mining and statistical tests. It also comes bundled with Machine Learning algorithms and probably is the best tool to make beautiful graphs and charts for Data Visualization.
- Database: Data Analysis begins with collecting and organizing data, which can be done by understanding DBMS. Data is collected from a variety of sources; managing them requires an understanding of MySQL and Apache Hadoop framework depending on the complexity of the data.
- Visualization Tools: A core aspect of Data Analysis is the ability to visualize data having dynamic features. Data Visualization can be performed using Python libraries or Data Analytics tools; it allows users to create interactive dashboards and storyboards.
- Machine Learning Algorithms: The tenets of Data Analysis underpin ML concepts to perform Supervised or Unsupervised Learning. This process involves minimal human intervention to predict underlying patterns scaling Data Analysis with precision.
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What are the Roles of a Data Analyst?
Data Analysis entails finding key metrics and creating reports to build the foundation for business ideas. This analysis investigates outcomes from the collection of raw data to generate insights and involves various stages as discussed below:
Key Data Analyst Roles: Data Collection
The primary task of analysis is to frame business problems followed by collecting data from multiple resources. Data collected can be qualitative (text) or quantitative (numerical) and arranged in a structured or unstructured format. There can be various types of data, and devising a strategy for collecting data can be carried out using the following methods:
Data scraping, also known as Web Scraping, is the process of importing data from a target website to a local system. The spectrum of collecting data by scraping is infinite and is optimized by objectives laid by organizations.
Web Scraping requires a URL to get the complete HTML code of the desired website and can be customized to extract exclusive or specific data of interest. Advancement in scraping tools has automated tasks featuring real-time Data Migration with zero data loss. The extraction of complex data is simplified by using scraping tools, as it identifies incoming schema and maps it into destination schema.
Database Management System handles multitudes of data in a structured or unstructured format. Information is accumulated from different sources into databases and fetched using a query language to collect data with essential Data Analysis features.
Big Data Technology
With advancements in technology, a massive volume of data ingestion is handled using the Hadoop framework. Several companies opt for Data Warehousing and Apache Hive integrating SQL interface on top of Hadoop serving Big Data storage, query, and analysis.
Key Data Analyst Roles: Data Processing
Data is collected in raw format are usually unordered and have missing values, making it difficult for analysis. Data Processing involves cleansing, scanning duplications, and outliers, and preparing organized data. It consists of the use of the below tools and techniques:
Data collected from various sources consists of null values, outliers, and data duplications. This issue can be eradicated by treating data with the concerned domain expertise to prepare data for analysis.
The Data Transformation process consists of mapping collected data to a destination format. The transformation process deals with simple and complex data and is solved using python scripts or ETL tools.
Key Data Analyst Roles: Data Exploration and Analysis
Data created after transformation reaches the next vital step of exploring and analyzing data. The purpose of Exploratory Data Analysis (EDA) is to visualize data by selecting charts and plots representing the outcomes of business decisions. To serve this purpose, the following modes of analysis are performed:
Statistics help in summarizing characteristics of data by graphical representation using inferential analysis. This analysis also involves estimation and constructing a hypothesis to find key metrics, thereby enabling organizations to succeed.
Data Exploration is also associated with Visual Analytics and interactive dashboards, enabling decision-makers to comprehend insights effectively. There are various Business Intelligence tools like Tableau and Power BI to quickly carry out visualizations of information.
Data Analysis is incomplete unless Machine Learning techniques are applied to unravel future outcomes. Predictive Modeling features the distribution of clusters, forecasting, and time-series events. Organizations utilize predictive algorithms to sideline the risk factors and optimize their marketing campaigns successfully.
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- Transformations: Hevo provides preload transformations through Python code. It also allows you to run transformation code for each event in the pipelines you set up. You need to edit the event object’s properties received in the transform method as a parameter to carry out the transformation. Hevo also offers drag and drop transformations like Date and Control Functions, JSON, and Event Manipulation to name a few. These can be configured and tested before putting them to use.
- Connectors: Hevo supports 100+ integrations to SaaS platforms, files, databases, analytics, and BI tools. It supports various destinations including Google BigQuery, Amazon Redshift, Snowflake Data Warehouses; Amazon S3 Data Lakes; and MySQL, MongoDB, TokuDB, DynamoDB, PostgreSQL databases to name a few.
- Real-Time Data Transfer: Hevo provides real-time data migration, so you can have analysis-ready data always.
- 100% Complete & Accurate Data Transfer: Hevo’s robust infrastructure ensures reliable data transfer with zero data loss.
- Scalable Infrastructure: Hevo has in-built integrations for 100+ sources like Google Analytics, that can help you scale your data infrastructure as required.
- 24/7 Live Support: The Hevo team is available round the clock to extend exceptional support to you through chat, email, and support calls.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Live Monitoring: Hevo allows you to monitor the data flow so you can check where your data is at a particular point in time.
Key Data Analyst Roles: Data Interpretation
The final step of Data Analysis involves interpreting results with stakeholders. Interpretation requires accuracy and attention to detail to help organizations make data-driven decisions. Data Analysis includes the following practices:
Interpretation is unbiased and sensible if logical questions are raised to make better decisions. Critical Thinking includes systematically gathering and examining relevant evidence using appropriate procedures. Skepticism compels them to assess all evidence critically, whether it corroborates or contradicts their predetermined expectations.
Every individual approaches a problem with experience. However, bringing people and their ideas together speeds up this Data Interpretation process. Data Analysts collaborate closely with data scientists, database developers, and many other departments of organizations. The success of interpretation depends on communication with peers and the ability to work with people.
The most significant time spent in Data Interpretation is producing internal and client reports. These reports help management summarize areas for improvement and identify success strategies. Reporting should delve deeper into a business context to give concrete plans for the ultimate growth of the organization.
What is the Average Salary of a Data Analyst?
According to job listing website Glassdoor, the average base salary for a data analyst in the United States in December 2022 is $62,382. The US Bureau of Labor Statistics reports that the median annual wage for a data analyst is $82,326 while Robert Half, a human resources consulting firm, lists a midpoint salary of $106,500.
Despite the wide range, the average annual salary for all occupations in the US is $58,260, so each of these salaries is considerably higher.
Data Analysis vs Data Science vs Business Analysis
Data Analysis is a subset of Data Science concerned with examining, cleansing, transforming, and modeling data to derive conclusions. Data Analysts tailor their work by performing descriptive, diagnostic, predictive, and prescriptive analyses.
In contrast, Data Science solves business problems by building result-oriented Machine Learning and Artificial Intelligence models to connect dots found after Data Analysis. While Data Analysis is concentrated on the historical context, Data Science involves modeling to aid analytical solutions, making it a multi-disciplinary domain.
On the other hand, Business Analysis is mostly related to creating reports using drag and drop tools that directly align with organizations’ business objectives. According to Gartner, Business Analytics is defined as “solutions used to build analysis models and simulations to create scenarios, understand realities, and predict future states.” This role requires a combination of verbal and analytical skills to enhance communication among departments and stakeholders.
This article talks about the role and responsibilities of Data Analysts. It gives a brief overview of Data Analysis and the significance of Data Analysis compared to other roles. Furthermore, the entire Data Analysis process is described in detail to understand various functions performed by Data Analysts.
Data Analysts play a vital role in business processes by contributing insights generated from raw data using their programming skills. This analysis is represented through dashboards to help identify KPIs and report the strategies that would allow organizations to take further crucial steps for business growth.
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