Organizations across all industries rely increasingly on data to make critical business decisions, such as which new goods to develop, which new markets to enter, which new investments to make, and which new (or existing) customers to target. They also use data to identify inefficiencies and other critical business problems.
In these organizations, the data analyst’s job is to assign these vital business functions a numerical value so that performance can be assessed and compared across time. But an analyst must be able to use data to assist a firm in making better decisions; their job is more than just looking at numbers.
These jobs are highly sought-after. According to IBM, more than 2.7 million employment openings will be for people with data capabilities by 2020. Approximately 40% of those positions require a master’s degree in advanced data analytics. The average starting pay for data analysts at entry-level roles is around $60,000. However, with success in the field, earnings for senior positions might approach $135,000.
Here is what you should know if you think you’d make a competent data analyst.
Analytics brings theory and practice together to identify and communicate data-driven insights that assist managers, stakeholders, and other executives in an organization make more informed decisions. Data analysts with experience consider the larger context of their work, the internal operations of their company, and many external influences. Analysts can also view the competitive environment, internal and external corporate interests, and the lack of specific data sets in the data-based recommendations they offer to stakeholders.
The topics of probability theory, statistical modeling, data visualization, predictive analytics, and risk management are covered in a Master of Professional Studies in Analytics program to prepare students for a career as data analysts. Additionally, a master’s degree in analytics provides students with the programming, database, and software tools necessary for a data analyst’s day-to-day tasks.
Data analytics types
The value that four different forms of data analytics may provide a business grows with time.
Descriptive analytics looks at previous events, such as monthly sales, quarterly revenue, yearly website visits, etc. An organization can identify trends thanks to these kinds of studies.
Diagnostic analytics compare descriptive data sets to find connections and trends to determine why something occurred. This aids an organization in identifying what led to a favorable or unfavorable result.
By identifying patterns in descriptive and diagnostic analyses, predictive analytics aims to predict expected outcomes. This enables a company to take proactive measures, such as contacting a client who is not likely to renew a contract.
Prescriptive analytics aims to determine the best course of action for a firm. While the capacity to solve future issues or stay ahead of industry trends is a significant benefit of this type of analysis, it frequently necessitates using sophisticated technologies, including machine learning.
The firm PwC discovered that enterprises believe descriptive analytics insufficient for informed, data-driven decision-making in a 2016 survey of more than 2,000 corporate executives. As a result, firms place more and more value on diagnostic and predictive analytics.
Principal Tasks of a Data Analyst
Depending on the organization and how much a company has embraced data-driven decision-making procedures, the answer to the question “What does a data analyst do?” will change. But generally speaking, a data analyst’s duties usually consist of the following:
- Building, maintaining, and fixing databases and data systems, including fixing coding problems and other data-related problems.
- I rearranged the data to make it readable by both people and machines once information was extracted from primary and secondary sources.
- Using statistical analysis to look for trends and patterns that could be useful for efforts at diagnostic and prescriptive analytics
- They are underlining the significance of their work in the context of local, societal, and global changes that impact their industry and business.
- It generates executive leadership reports communicating trends, patterns, and projections using relevant data.
Finding opportunities for process improvement, suggesting system modifications, and producing data governance policies require collaboration with programmers, engineers, and corporate leaders.
Creating the appropriate records so that interested parties may follow the data analysis process steps and, if necessary, repeat or reproduce the analysis
Most Important Qualifications for Data Analysts
A practical data analyst combines leadership abilities with technical expertise.
Technical abilities include proficiency with spreadsheet programs like Microsoft Excel or Google Sheets, database languages like SQL, R, or Python, and data visualization tools like Tableau or Qlik. It is also helpful to have mathematical and statistical knowledge to collect, measure, organize, and analyze data.
A data analyst’s ability to lead others will help them make decisions and solve problems. These skills enable analysts to successfully explain the value of this information to stakeholders and think strategically about the information that will support them in making data-driven business choices. For instance, project managers rely on data analysts to monitor the most important project KPIs, identify potential issues, and forecast how various solutions can approach a problem.
Data science versus business analysis versus data analysis
The way the three positions use data determines how a data analyst’s job differs from that of a business analyst or a data scientist.
The data analyst serves as the organization’s data gatekeeper, ensuring stakeholders can comprehend and use the data to make intelligent business choices. For this technical position, you must have a bachelor’s or master’s degree in analytics, computer modeling, science, or math.
The business analyst plays a strategic role in leveraging the data that a data analyst unearths to pinpoint issues and provide fixes. Typically, these analysts have business administration, economics, or finance degrees.
The data scientist goes beyond what data analysts have already visualized by digging through the data to find a company’s flaws, trends, or opportunities. This position also needs a background in math or computer science and some research or understanding of human behavior to create accurate forecasts.
It is usual for a data analyst to take on some of the predictive modeling or decision-making duties that may usually be delegated to a data scientist at startups and other small businesses.
How Much Do Data Analysts Earn?
A data analyst’s annual pay might average range from about $60,000 to $138,000. Jobs in financial and technological companies typically pay more than the national average, according to the job listings on the websites.
The data analyst position is a natural stepping stone for more senior data-driven positions. Data analysts can advance to positions like senior data analyst, data scientist, analytics manager, and business analyst, according to PayScale. The remuneration for these positions has also increased significantly. IBM predicts that the starting compensation for data scientists will be close to $95,000 and that the starting salary for analytics managers will be close to $106,000.