Explore this Data Analyst Career guide to understand the skills, jobs, career growth, salary, etc., and how to become a Data Analyst:
In the fast and competitive global business world today there is no room for incorrect, misinformed wrong decisions and strategies. This trend has enhanced and brought the role of data analysts to prominence.
Multiple applications are running in silos in an organization and there is no dearth of information available.
Data Analysts primarily scan the scattered business information keeping in mind the organization’s mission and goal. With their skill and insight, they help the organization make informed decisions that will lead to commercial growth, production efficiency, product evolution, and increased profit margin, resulting in a competitive edge.
What You Will Learn:
- What Is Data Analysis
- How To Become A Data Analyst
- Data Mining VS Data Scientist VS Data Analysis
- Frequently Asked Questions
What Is Data Analysis
The picture below shows the scattered business information of organizations that are primarily scanned by Data Analysts.
Data Analysis is a process of managing and interpreting data. It includes capturing, organizing, inspecting, cleaning, and transforming data. The goal is to segregate useful information, draw meaning, understand trends, and present the insight to support decision-making.
Types Of Data Analysis
Data Analysis techniques vary from domain to domain and the approach can vary depending on the perspective from business, science to social science.
The major data analysis approaches are:
- Data Mining
- Business Intelligence
- Statistical Analysis
- Predictive Analytics
- Text Analytics
Data Analysis Process
The main step of Data Analysis include:
- Define the purpose
- Manage and control data source
- Improve data quality
- Analyze data
- Communicate the observation for informed decision making
Recommended Reading =>> Popular Data Analysis tools for your business
How To Become A Data Analyst
Data is merely facts, information, and figures. Data Analyst structures organize analysis, interpret and share the data giving it context and meaning in sync with organizations’ needs. This context helps the decision-makers to make an informed decision in achieving the business goals.
So the primary step to becoming a data analyst is to understand the meaning, purpose, and significance of data in an organization. Once this is understood, the next step should be in getting expertise in data management.
And the last step will be in analyzing the data and presenting the insight in the most impressive, convincing, and effective way to the decision-making team.
The path to becoming a Data Analyst is:
- Step 1: Get a bachelor’s degree in any field preferably in Commerce/Information Technology/Computer Science or Statistics.
- Step 2: Try to get some on-field experience in any domain to understand the importance and role of data and the challenges in capturing and managing it efficiently.
- Step 3: Try to get a master’s degree or certificate program preferably in data science, data analytics, or big data management.
- Step 4: Consider getting a certificate or learning some data analytical tools like Xplenty, Zoho Analytics, R_Programming, etc.
- Step 5: Learn and improve communication skills to present the data analysis results to help the organization in improving its efficiency/gain a competitive edge/diversify, etc.
Roles And Responsibilities
Let us understand the various roles and responsibilities of a Data Analyst.
#1) Managing Data
- Managing data source, frequency of data capturing, security, metadata, and data environment.
- Managing and controlling data storage from creation, correction, and updates to deletion.
- Managing security and accessibility by defining users and user roles.
- Ensuring data quality and reliability.
- Evaluating and accepting or rejecting data sets.
- Safeguard confidential data and information as per agreement.
- Ensuring data integrity, standardization, and normalization.
#2) Manage communication and Reports
- Publish all reports and analyses as per the schedule and frequency.
- Discuss and finalize the report content that needs to be shared with different resources with the data warehouse team and others.
- Ensuring the validity and correctness of all reports.
#3) Key role in decision-making and direction
- Take part in software requirements and up-gradation requirements for analysis tools.
- Evaluating and suggesting changes and new requirements in production systems.
- Be part of the technical discussion related to managing data in terms of database design, storage, data mining, data cleansing, archiving, etc.
#4) Training end-users in understanding reports and dashboards.
Listed below are a few desired technical and soft skills to become a data analyst:
- Ability to understand business direction and objectives
- Cause and affect-based on data
- Contribute effectively to decision making
- Attention to detail
- Critical thinking and problem solving
- Communication and Presentation skills
- Data Visualization
- Data Base Management – Querying (SQL), Design, Security, etc.
- Data Warehousing
- Statistics and Statistical programming languages
- Project management
- Domain knowledge
- Knowledge of Tools – Reporting and Data Analysis Tools and application
Business Value Added
Data Analysts add value to the business by:
#1) Improving the Decision-Making process and highlighting innovative business insights
Sharing timely data and information with all departments enables and empowers all departments and encourages them to work collaboratively. Swifter decisions based on real-time information and highlighting internal inefficiency, latest market trends, etc. improve customer experience, increased brand loyalty, increased profit margins, and diversification.
#2) Streaming Internal Processes and Boosting Productivity and Sales
Unifying and integrating multiple data sources into one platform and enabling data analysis by each department empowers them. This process makes policy and improvement initiatives a participatory decision process with a higher rate of success.
Ensuring timely and efficient maintenance of production facilities reduces downtime and maintenance costs alongside increasing production volume and costs.
#3) Improving Financial Efficiency and interdepartmental coordination
Transparency with key players regarding potential cash flow issues, out-of-sync interdepartmental activities, and suggesting simple steps to improve them and recover from loss, improving sales, customer loyalty, and employee satisfaction.
Aligning promotional activities with marketing activities, the marketing department with the customer life cycle, the production cycle with marketing activities, and inventory with the production cycle is a few simple insights highlighted by data analysts.
#4) Reducing IT Involvement and dependency on information access
Decentralization and reducing data access monopoly with a single department is the first step towards progress. Computer literacy, ease of information access, and availability of quality real-time information is the biggest contribution of data analysts to success.
Data Analysis Success story
#1) Netflix: “There are 33 million different versions of Netflix” Joris Evers, Director of Global Communications”
Data analysts conducted sophisticated analysis and helped in developing a model for targeted advertising based on peak demand time and individual customer-based buying patterns. This improved their sales and marketing and enabled them to give customers an enriching buying experience and satisfaction.
#2) UPS: ” UPS chief information and engineering officer for logistics giant UPS, Juan Perez is placing analytics and insight at the heart of business operations, he says – “Big data at UPS takes many forms because of all the types of information we collect, We’re excited about the opportunity of using big data to solve practical business problems. We’ve already had some good experience of using data and analytics and we’re very keen to do more.”
Data Analysts helped in streamlining and optimizing the logistics and operations. Delivery routes, delivery time, and efficiency improvement enabled to cut cost, and time and build customer loyalty.
#3) Volvo: “Data was leveraged to run detailed analyses of assembly line machinery to identify repeat faults and track problems down to the lowest level. This has resulted in bottom-up problem solving where issues can be ticked off one by one.”
Data Analysts identified patterns of downtime and failures. Process efficiency and root cause for delays, downtime, and cost were identified and improvements were suggested that improved the turnaround time.
#4) Health and Lifestyle:
Data Analysts’ role in today’s world experience of the pandemic is undeniable. Data sets of patients, clinical trials, scientific advice, and recovery statistics have paved the way to quick drug discovery, improve the healthcare system, and global coordination, and made the world a safer place.
#5) Schneider Electric: “Benefits include the ability to automatically tune machine hyperparameters which helped operators increase efficiency by 10 to 20 percent in just two days.”
Data Analysts studied the enormous data collected from meters, grids, etc., and optimized and planned power generation based on consumption and demand. The strategy was developed to reduce costs and lower carbon emissions.
#6) Walgreens: “Data is critical for everything that we do at Walgreens, and with that data, the customers are telling us what they buy and what they need. “Andy Kettle well: Vice President Inventory and Analytics, Walgreens
Data Analysts analyzed the real-time customer data and helped in formulating marketing and operation strategy to build brand loyalty and customer satisfaction.
Data Analyst Salary
The salary structure and growth prospects of a data analyst have seen a tremendous change with the realization and importance of being data-intelligent in business management. Their profiles, roles, and responsibility also have become more intense.
As per the survey by Payscale,
- With an experience of about 10 years, the salary increment is about 20%.
- Some specific skill sets also add to the salary.
- Experience in Tools like Tableau can boost the salary by 8%.
- Knowledge of programming language and expertise in query language like SQL can add a 5% increment.
- Salary also varies based on location and company.
- Apple, Amazon, Facebook, etc. pay their data analyst more than average and the expectation and performance expected are also higher.
- The below-listed profile-wise salary is based on data from Indeed.com and salary comparison site PayScale
- Junior data analyst: $58,090
- Data analyst: $75,307
- Senior data analyst: $97,348
- Data analytics manager: $89,287
- Financial analyst: $72,281
- Healthcare analyst: $63,411
- Insurance claims analyst: $51,033
- Marketing analyst: $65,848
- Systems analyst: $78,212
- Data scientist: $122,511
- Data analytics consultant: $77,365
- Chief data officer: $178,606
Job And Career Growth
The Career growth of a data analyst post their first job can be managed as per interest.
The three basic branches are:
- Continue in the same profile to a senior role.
- Branch to the specialist role-Develop domain-specific data expertise like Insurance, Health, Finance, Marketing, etc.
- Branch to Data Scientists role-Manage complex concepts such as building algorithms, machine learning, and data modeling. They have expertise in programming languages such as R and Python.
Data Mining VS Data Scientist VS Data Analysis
Refer to the below table for understanding the differences:
|Data Mining||Data Analysis||Data Scientist|
|Objective is to recognize the pattern in stored data||Raw data is organized and maintained with a purpose||Need to understand Business objective and the implications of the data|
|Interpretation and analysis of extracted data is NOT expected||Data interpretation and insights are facilitated post data analysis||Need to have critical-thinking skills, mathematical and statistical knowledge for an innovative and efficient solution|
|Clean and quality data is expected||Data analysis and cleaning is done and formatted meaningful data is presented||Focus on developing data modeling process, algorithms and predictive models and methods to extract the required data from scattered business information|
Frequently Asked Questions
Q #1) How to Become a Data Analyst?
Answer: Entry-level requirement is a bachelor-level degree. A Master’s degree in data science, and business analytics, will help in career growth. Knowledge of data analysis tools or data warehouses or data mining will help in increment in salary.
Q #2) What are the skills required for a Data Analyst?
Answer: The key skills that help in performing a data analysis are:
- Critical thinking
- Presentation skills
- Data visualization
- Knowledge of technical skills like SQL, R, Python, etc.
- Statistical analysis skills
Q #3) Does a data analyst’s job require coding skills?
Answer: Data analysts need not be experts in coding. They should have some experience in technical skills like SQL, data analysis software, database management software, etc. Their strength lies in statistical and predictive analysis and presentation.
Q #4) What are the issues and challenges faced by a data analyst?
Answer: Data quality is the biggest uphill task. Identifying data issues like duplicity, spelling error, redundant data, etc. is a very challenging and time-consuming task. The data resource reliability, real-time data maintenance, extraction, and nonavailability of required data are other difficulties that a data analyst has to face.
Q#5) What is the main difference between data analysts and data scientists?
Answer: Data analyst interprets trends and insights from existing data and the data scientist concentrate on building models and approaches to capture data to be analyzed by the data analyst.
Suggested reading =>> Qualitative vs Quantitative Data Analysis
Today, business decisions powered by data insights play a key role in being successful and maintaining a competitive edge. Data Analysts have a major role to play and are responsible to give organizations this competitive edge. They analyze corporate data from a scientific point and statistical point of view and predict trends and insights.
With their statistical knowledge of correlation analysis, they identify core data factors that impact key business factors like employee and machine performance and inefficiency.
Being aware of technologies like a data warehouse, visualization, database management, etc. they prioritize data collection and maintain data quality. They contribute to improving customer experience and building brand loyalty by analyzing real-time customer data.
They can build their career as specialists in different domains like health care, insurance, manufacturing, etc., or become data scientists or consultants in data analytics.
Data analysts are the organization’s gatekeepers managing the entry, exit, and access to real-time data so people across the business can use it to make strategic decisions.