Organizations in all industries increasingly rely on data to make critical business decisions—which new products to develop, new markets to enter, new investments to make, and new (or existing) customers to target. They also use data to identify inefficiencies and other business problems that need to be addressed.
In these organizations, the job of the data analyst is to assign a numerical value to these important business functions so performance can be assessed and compared over time. But the job involves more than just looking at numbers: An analyst also needs to know how to use data to enable an organization to make more informed decisions.
These roles are in high demand. IBM has estimated that there will be more than 2.7 million job openings for professionals with data skills by 2020, with nearly 40 percent of jobs in advanced data analytics requiring a master’s degree or higher. Entry-level data analysts receive an average starting annual salary in the $60,000 range; however, success in the role can lead to senior positions with salaries approach $100,000.
If the role of a data analyst sounds like a good fit for you, here’s what you need to know.
What is Analytics?
Analytics brings together theory and practice to identify and communicate data-driven insights that allow managers, stakeholders, and other executives in an organization to make more informed decisions. Experienced data analysts consider their work in a larger context, within their organization and in consideration of various external factors. Analysts are also able to account for the competitive environment, internal and external business interests, and the absence of certain data sets in the data-based recommendations that they make to stakeholders.
A Master of Professional Studies in Analytics prepares students for a career as a data analyst by covering the concepts of probability theory, statistical modeling, data visualization, predictive analytics, and risk management in the context of a business environment. In addition, a master’s degree in analytics equips students with the programming languages, database languages, and software programs that are vital to the day-to-day work of a data analyst.
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Types of Data Analytics
Four types of data analytics build on each other to bring increasing value to an organization.
- Descriptive analytics examines what happened in the past: Monthly revenue, quarterly sales, yearly website traffic, and so on. These types of findings allow an organization to spot trends.
- Diagnostic analytics considers why something happened by comparing descriptive data sets to identify dependencies and patterns. This helps an organization determine the cause of a positive or negative outcome.
- Predictive analytics seeks to determine likely outcomes by detecting tendencies in descriptive and diagnostic analyses. This allows an organization to take proactive action—like reaching out to a customer who is unlikely to renew a contract, for example.
- Prescriptive analytics attempts to identify what business action to take. While this type of analysis brings significant value in the ability to address potential problems or stay ahead of industry trends, it often requires the use of complex algorithms and advanced technology such as machine learning.
In a 2016 survey of more than 2,000 business executives, the consultancy PwC found that organizations find descriptive analytics to be insufficient for informed, data-driven decision making. As such, diagnostic and predictive analytics are increasingly important to organizations.
Key Responsibilities of a Data Analyst
The answer to the question “What does a data analyst do?” will vary depending on the type of organization and the extent to which a business has adopted data-driven decision-making practices. Generally speaking, though, the responsibilities of a data analyst typically include the following:
- Designing and maintaining data systems and databases; this includes fixing coding errors and other data-related problems.
- Mining data from primary and secondary sources, then reorganizing said data in a format that can be easily read by either human or machine.
- Using statistical tools to interpret data sets, paying particular attention to trends and patterns that could be valuable for diagnostic and predictive analytics efforts.
- Demonstrating the significance of their work in the context of local, national, and global trends that impact both their organization and industry.
- Preparing reports for executive leadership that effectively communicate trends, patterns, and predictions using relevant data.
- Collaborating with programmers, engineers, and organizational leaders to identify opportunities for process improvements, recommend system modifications, and develop policies for data governance.
- Creating appropriate documentation that allows stakeholders to understand the steps of the data analysis process and duplicate or replicate the analysis if necessary.
Most Valuable Skills for Data Analysts
Technical skills include knowledge of database languages such as SQL, R, or Python; spreadsheet tools such as Microsoft Excel or Google Sheets; and data visualization software such as Tableau or Qlik. Mathematical and statistical skills are also valuable to help gather, measure, organize, and analyze data.
(Learn more: Project Management Skills for Data Analysts)
Leadership skills prepare a data analyst to complete decision-making and problem-solving tasks. These abilities allow analysts to think strategically about the information that will help stakeholders make data-driven business decisions and to communicate the value of this information effectively. For example, project managers rely on data analysts to track the most important metrics for their projects, to diagnose problems that may be occurring, and to predict how different courses of action could address a problem.
Data Analysis vs. Data Science vs. Business Analysis
- The data analyst serves as a gatekeeper for an organization’s data so stakeholders can understand data and use it to make strategic business decisions. It is a technical role that requires an undergraduate degree or master’s degree in analytics, computer modeling, science, or math.
- The business analyst serves in a strategic role focused on using the information that a data analyst uncovers to identify problems and propose solutions. These analysts typically earn a degree in a major such as business administration, economics, or finance.
- The data scientist takes the data visualizations created by data analysts a step further, sifting through the data to identify weaknesses, trends, or opportunities for an organization. This role also requires a background in math or computer science, along with some study or insight into human behavior to help make informed predictions.
At startups and other small organizations, it is not uncommon for a data analyst to take on some of the predictive modeling or decision-making responsibilities that may otherwise be assigned to a data scientist.
What Do Data Analysts Make?
The average annual salary of a data analyst can range from approximately $57,000 (PayScale) to $68,000 (Glassdoor and Indeed). According to the sites’ job listings, roles at financial and technology firms tend to pay higher than average.
(Learn more: How to Increase Your Salary as a Data Analyst)
The data analyst role is also a natural stepping stone for more senior data-driven jobs. According to PayScale, data analysts move on to roles such as senior data analyst, data scientist, analytics manager, and business analyst. These roles come with substantial increases in pay, too. According to IBM’s estimates, the annual salary of data scientists will start at nearly $95,000, while analytics managers will make nearly $106,000 per year.
If you’re interested in building a career in analytics, take the first step by downloading our free, comprehensive guide below.