Data Analyst Job Description

DATA ANALYST FAST STATS

 Data Analyst: understanding the job

What is a data analyst?

A data analyst is someone who gathers and analyzes data for a client, business or organization. Because their work is needed in all types of businesses and organizations, data analyst jobs are spread over a diverse range of fields. They are needed in software development companies to determine product efficiency, in banks to monitor customer account data, in pharmaceutical companies to investigate medicinal use demographics, in social media companies to pick apart user engagement trends, and even in higher learning institutions to study student enrollment patterns.

No matter what field it’s in the data analyst job description is simple. They gather a bunch of technical data, make it easy to understand, and hand it over to managers or executives along with some insights about how it can improve their businesses or organizations. They can specialize in certain areas of a business this changing their title to things like operations analyst, financial analyst, marketing analyst, database analyst, business intelligence analyst etc.

Data analysts use the following technical and soft skills on a daily basis.

| data analytics | critical thinking and problem solving | time management | 

| interpersonal skills | statistics | mathematical skills | database management | 

| programming language skills | communication |

Top data analyst skills

What data analysts do

Data analyst jobs center around developing, using, and improving systems for gathering data. It comprises the most important portion of any data analyst job description. Data analysts first develop a system of collecting data that fits a particular client, business or organizational need. This typically involves using various data collection and storage software and sometimes requires creating customized IT infrastructure with data engineers, web developers, and software developers. Data analysts then arrange that data into a report for client, management, or executive consumption.

It's no wonder that the relevance of so much data can be difficult for clients or executives to understand. They don’t get to know data in nearly as much detail as their analysts, which is exactly where the ‘‘analyst’’ portion of the data analyst job description comes in. Interpreting data and gleaning insights from the patterns they reveal is the most critical aspect of all data analyst jobs, for those analyzing data from beverage distributors to those working with healthcare providers.

''A mix of technical, interpersonal and communication skills is integral to the data analyst position.''

Analysts have to really understand the context of a client’s or business’s product or service and the market it operates within in order to come up with applicable insights. This is the data analyst's opportunity to gain business and market awareness from their analysis. An experienced and savvy data analyst can use that awareness to gain an advantage over an entry level data analyst when interfacing with clients, when receiving project assignments and when being placed within an organization.

What great data analysts do

1. Great data analysts create value.

After collecting data and analyzing it, great data analysts gain insights in the patterns and trends that help executives and managers make growth-based, profit-increasing decisions that add value to their companies and organizations.

2. Great data analysts can be generalists and specialists.

Great data analysts can fit into any business or organization, quickly learn the idiosyncrasies that make their products or services unique or successful, and construct efficient methodology and processes of data collection and analysis. They also have the ability to become an expert in a specific field or industry, carrying their expertise into building data collection systems to play a data analyst and data scientist role if necessary. This ability moves a great data analyst beyond an entry level data analyst.

3. Great analysts connect teams and executives.

A great data analyst communicates with the right employees on the ground to formulate the most relevant research questions and collect data that executives can use to improve or grow their business. That means that great data analysts encourage executives and their teams to get together behind data-driven concepts to increase productivity, quality and profitability.

Typical day as a data analyst

Whether an entry level data analyst or an experienced one, the data analyst’s day shares the same responsibilities and tasks. The day may begin by completing design of a data collection system for a project a client commissioned. That type of work is largely technical so it involves communicating with software and web developers as well as data scientists and engineers to find the right system for the job at hand.

When that task is accomplished, a data analyst may be in touch with a client or different departments within an organization to develop a set of research questions from which a new data collection initiative will begin. From there the data analyst may work alone for a while to assess the information they gathered -- for example on inventory processing and storage procedures or delivery times and schedules -- and develop a statistical model for data collection on a large scale.

The day may then shift to writing and producing reports, requiring completely different data analyst skills then the mathematical and technical work they were busy with before. Data analytics jobs require such shifts, from IT work to writing and making charts that represent data in ways that are easy to understand. The last thing anyone would expect to be necessary in data analytics jobs may come next: making a presentation to executives or clients that makes a bunch of numbers stimulating and relevant and highlights insights about those numbers. The data analyst would have spent significant time before the presentation analyzing and identifying relevant patterns in the data that executives or clients could use to grow and improve their business or organization.

 Data analyst basic duties

• Manage data and data storage and assess incoming data for quality and accuracy
• Audit data for compromised or corrupted items
• Use statistical programs (Excel, SPSS, etc.), databases with different programming languages (SQL, etc.) and customize software using programming languages (Python, XML, Javascripts, etc.) to process and organize data for analytical purposes
• Find patterns and trends in data and follow project context to interpret them for client or executive use or benefit
• Use presentation and data processing software to enhance visual understanding of statistical patterns and trends
• Consult with executives or management about data findings and how they may be advantageous or harmful to growth, efficiency quality or profitability
• Work with developers and engineers to construct customized data collection methodology
• Produce scientifically sound and visually presentable reports and dashboards that analyze data trends and patterns

Becoming a data analyst

Data analyst job requirements

Education

A bachelor’s degree in computer science, mathematics, statistics or a related field is required in all data analytics jobs. Master’s degrees in the same fields may be preferred for more senior-level analyst positions.

Certification

Data analytics certification is not required for entry level positions, however, it is sometimes preferred for senior data analyst positions and can be a way to advance a career in data analytics. There are many different certifications, some are specific to industry or skill and others are more general like the Certified Analyst Professional credential (CAP).

Experience

Some experience is necessary to compete for almost all data analyst positions. Two to three years of experience in data mining, analytics or some related field is typically sufficient. This experience could come in part from a data analyst internship or other relevant analytical work.

Skills

Requisite data analyst skills are a good mix of technical and soft skills because they have to deal with data and the people who use it but may not fully understand all the details about it. Those skills include data mining and analytics, critical thinking and problem solving, time management, interpersonal skills,statistical and mathematical skills,database and programming language skills, and communication.

Ideal data analyst resume

The ideal data analyst resume lists 3-5 years experience in data mining and analysis and a bachelor’s or master’s degree in mathematics, statistics or computer science. Candidates considering a more senior level position will benefit from having a big data certification on their resume that fits their experience and field. Candidates for entry level data analyst jobs and junior data analytics jobs will benefit from highlighting experience in a data analyst internship or some other analyst training experience to make their resume stand out or data analyst cover letter more attractive than the rest.

''...data analyst job growth is at 27%, an unusually high rate, indicating a rapidly expanding field''

The soft skills listed in previous sections are valuable supplements to the ideal data analyst resume that one may include in a data analyst cover letter or in answers to data analyst interview questions. When responding to data analyst interview questions it is always advantageous to fill in assets that don’t show up on a data analyst cover letter or resume. Experiences building customized data collection and analytics systems and even training other analysts to use the system are good talking points when responding to data analyst interview questions. Combining skills, education and experience is the way to build the ideal resume and discuss the professional value one can bring to an organization.

 Data analyst salary

The U.S. Bureau of Labor and Statistics (BLS) reports the median operations research analyst salary (directly comparable to data analyst salary) in 2017 to be $81,390.  Senior data analyst salary is typically higher than average while entry level data analyst jobs and junior positions can have salaries that fall below the average. The BLS projects job growth at an unusually high rate of 27% between 2016-2026, indicating a rapidly expanding field in the job marketplace.

Is data analysis the right job for me?

If you’re considering a data analyst position and you haven’t studied computer science, mathematics, statistics or a related field, it is important to have 1) a keen interest in studying data, 2) the technical mathematical skills to compile and analyze the statistics, patterns and trends found within it and 3) the programming literacy to custom design data collection and analysis systems to fit client or business needs.

It’s a pretty technically specific path that usually starts with undergraduate studies and an interest in computers, math and studying data. That is not to say that one cannot learn to be a data analyst through a combination of data analysis coursework and practical experience in, for example, a data analyst internship. Typical entry level data analyst jobs require a significant level of expertise and knowledge and some practical experience collecting and analyzing data preferably with exposure to the data collection design process.

''Fortunately, salaries and potential for upward mobility are fairly high... as you build relationships with clients and executives, potential for promotion rises''

If the requisite expertise is there, perhaps the next thing to consider is whether or not the flow of the typical work day as a data analyst is right for you. Though much time is spent in front of a computer and working with numbers, there is a significant portion of the job that expects you to communicate in person and in writing with colleagues and stakeholders. That could mean collaborating with tech teams to design and maintain data collection systems and presenting findings and insights based on your analysis of those findings to clients and executives.

A mix of technical, interpersonal and communication skills is integral to the data analyst position. Fortunately salaries are fairly high and potential for upward mobility is quite high as one may move from a junior data analyst to a senior analyst who interfaces more with clients and executives. As technical expertise builds, one may move to data scientist or engineer positions within an organization. As relationships are built with clients and executives, the potential for promotion to higher level positions rises.

Top data analyst skills

Data mining and analytics - database design; data modeling and segmentation; comfort with data collection optimization software

Critical thinking and problem solving - developing customized solutions to client or organizational specific data collection requirements; contextualized pattern spotting and trend identification suited to client or business needs

Time management - working under deadline to produce periodic reporting and analysis for presentation to executives and clients; prioritizing data collection software design or analytics or report preparation as needed to meet client or executive needs

Interpersonal skills - forming relationships with clients and executives to whom technical data must be made clear and easy to understand; working well in teams with web developers or software developers or computer programmers to design and maintain data collection methodologies

Statistical and mathematical skills - organizing data using statistical and mathematical software (Excel, SPSS, SAS, etc.); calculating statistical interpretations and projections

Database and programming language skills - programming in Javascript, ETL and XML frameworks etc.; SQL experience

Communication - producing written reports with presentation-ready visualizations (charts, diagrams, videos etc.); interpreting data and presenting it orally in ways that make it easy for executives and clients  to understand and use to make decisions to help improve their businesses

Similar or related job titles

• Software engineer
• Operations analyst
• Financial analyst
• Marketing analyst
• Data scientist
• Data architect
• Database administrator
• Database developer
• Data manager
• Software developer