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Data Analyst

What Is a Data Analyst?

Ready to start your career in Data? Find out more about BrainStation's Data Analytics Course

Data Analysts are data professionals who gather, order, and examine data and information to find trends and make actionable reports for companies or other organizations. Data Analysts must sift through massive amounts of data and – while considering the specific needs of the company that they’re working for – determine key data sets. Data Analysts then use different tools to create visualizations that make these insights easy for non-data professionals to understand and act upon as they make major decisions on the company’s direction.

Data Analysts are employed in virtually all industries and all types of companies, from healthcare providers to banks to retail stores to restaurant chains. Data Analysts bring valuable insights to any employer who wants to know more about the needs of their consumer or end-user, to evaluate how their current products or processes are faring, and to find opportunities to expand on what’s working well.

No matter the industry they work in, Data Analysts should expect to spend considerable time developing systems for collecting data, organizing that data into data sets, and compiling their findings into visually compelling reports that can help improve the way a company is operating.

Potentially, Data Analysts could be included in any part of the analysis process. In a Data Analyst role, you could be involved in everything from setting up an analytics system to presenting insights based on the data you collect. Otherwise, the specific job duties of a Data Analyst will vary considerably based on the industry and the company. Although we’ve been hearing more and more about the importance of data over recent decades, businesses have actually been using some form of Data Analytics as far back as the 1800s, when Frederick Winslow Taylor began running time management exercises.

Another 19th-century example relates to the U.S. census. To collect the necessary data, analyze said data, and create a report in 1880 took seven years. To correct that, Herman Hollerith in 1890 invented the “tabulating machine,” which took punch cards and systematically processed their data. Thanks to this innovation, the 1890 census was complete in just 18 months for far less money.

Still, the real rise of data analysis occurred in the late 1970s and early ‘80s with the development of relational database management systems (RDBMS) that gave users the ability to write Sequel (SQL) to retrieve data from a database. That empowered Data Analysts to analyze on-demand and the convenience made database use more and more popular. Ultimately, cheaper, faster data collection and cheaper, faster data storage/retrieval technology led to the explosion of big data.

In short, Data Analysts collect and analyze large amounts of data and information to find trends and actionable insights. They work to:

Know Your Audience

Understand your users' needs and identify opportunities.

Evaluate Products and Processes

Measure and maximize efficiency and effectiveness.

Eliminate Guesswork

Make informed decisions to set realistic targets and objectives.

Optimize Marketing ROI

Track and analyze marketing campaigns to improve performance.

Data Analysts vs Data Scientists

Data Analysts typically:

  • Are more junior
  • Have a focus on business intelligence
  • Use Excel instead of programming languages
  • Look for meaningful insights from data
  • Optimize existing products and processes

Data Scientists typically:

  • Are more senior
  • Aim to predict the future
  • Use Python and other programming languages
  • Develop new ideas and products
  • Are involved with strategic planning

Find out more about the differences between Data Analysts and Data Scientists.

What Does a Data Analyst Do?

Data analysts collect, organize, and interpret data and information to create actionable insights for companies. To accomplish this, Data Analysts must collect large amounts of data, sift through it, and assemble key sets of data based on the organization’s desired metrics or goals. Analysts then often transform those key datasets into dashboards for different departments within the organization, presenting their insights in ways that can be used to inform activities and decision-making.

Data Analysts work in everything from political campaign management and finance to mining and epidemiology. But to give an example: imagine a corporate website that uses content marketing for lead generation. Tracking the conversion rates of visitors into customers yields data that lets a Digital Marketer follow a potential customer from their arrival at a blog post or other landing page all the way through to their signing up for a newsletter or even purchasing a product. Seeing what happens at each step helps the Marketer understand what content is working, why it’s working, and hopefully expand on that success.

Data Analysts’ specific tasks vary wildly from industry to industry, company to company. Generally speaking, though, as a Data Analyst, you can expect to perform some or all of the following tasks and responsibilities:

Research

As a Data Analyst, you will be responsible for researching your company and your industry to identify opportunities for growth and areas for improved efficiency and productivity.

Data Gathering

Data Analysts must gather data requirements, beginning with determining what you hope to accomplish and arriving at a clear sense of what information you need.

Data Collection

Data Analysts must collect usable data and information, either from existing sources or by developing new channels for information gathering.

Data Cleaning

Data Analysts must reformat data for consistency, removing duplicate entries and null sets, and so on. In very large datasets, this task is too onerous to complete by hand and requires the use of purpose-built tools and software.

Algorithm Creation

You will have to create and apply algorithms to run automation tools, allowing you to understand, interpret, and reach solid conclusions about what the data shows.

Data Modeling

Another requirement for Data Analysts is to model and analyze data to identify significant patterns and trends and interpret their meaning.

Presenting Your Findings

Once data analysis is complete, Data Analysts present their findings to other members of the organization, digested and packaged in a way they can easily grasp. This can include creating visualizations or dashboards for other members of the organization to refer to.

This diverse range of actions can be generalized by four fundamental categories: understanding the data, analyzing the data, building and managing databases, and communicating the data to others.

In the most recent BrainStation Digital Skills Survey, most Data Analyst respondents said they spend the largest amount of time wrangling raw data and cleaning it up. The primary use for this data? Optimizing existing platforms and products, as well as the development of new ideas, products, and services.

When BrainStation further correlated these responses to major job titles, an interesting discrepancy between Data Analysts and Data Scientists emerged: the majority of Business Analyst and Data Analyst respondents indicated that they tend to focus more on the former (optimizing existing platforms and products). Data Scientists, on the other hand, hew primarily toward the development of new ideas, products, and services, where strategic planning comes to the fore—possibly a result of differences in experience, knowledge levels, or degree of specialization.

Types of Data Analysis

These are a few of the most prominent data analysis types:

Text Analysis

Looks for patterns in large sets of written information (e.g. customer feedback surveys or social media posts).

Statistical Analysis

Examines characteristics of a numerical data set to find trends and correlations between data points.

Diagnostic Analysis

Drills deeper into insights from statistical analysis to determine causes and why correlations exist.

Predictive Analysis

Extrapolates or projects figures beyond the parameters of the existing dataset to forecast future outcomes.

Prescriptive Analysis

Draws on insights gained and uses them to determine the best course of action in a given situation.

Benefits of Data Analysis

Data analysis is important in business to help your organization identify and define problems, and to organize and interpret data sets to provide actionable insights and solutions. In addition, the relative speed and ease with which data can now be leveraged means that virtually every organization can optimize their operations and investments, allowing them to:

  • Make informed decisions
  • Set realistic targets
  • Predict consumer behavior

There are a wide range of enterprise-level analytics tools available to businesses at virtually every scale. Google Analytics is a great example. The basic tools are free, and their insights can be used to recalibrate and dramatically improve the performance of your business website.

How Does Data Analytics Help Businesses Make Decisions?

Data analytics can help businesses make decisions by compiling data from across an organization, giving it insight into the performance of sales, marketing, product development, and more. This gives organizations the opportunity to track the performance of various initiatives and investments in context, allowing it to make better business decisions.

Data analysis can also allow your organization to dig deeper into each business function and department, ensuring you’re maximizing ROI. Let’s take a closer look at the impact data analysis can have on digital marketing initiatives:

Social Media Marketing

What is your target audience talking about? How do they interact with your company? Do they share or like the content your business is posting? Coupling your social media presence with good data analysis can extract more value from your online profile.

Email Marketing

Using analytics, email can double as an information-gathering tool by helping you determine which subject lines get the most opens, and what kinds of messages see the highest rates of success. You can also test which times of day or days of the week your audience is most likely to see and open emails. Data analytics ensures that you aren’t just sending your messages out into the universe and hoping for the best; it gives you the information you need to continuously improve.

Target Demographics

Data can help define your target audience, including understanding where they are and how to reach them. Rather than wasting advertising dollars to reach a broad audience, data analytics allows you to more precisely target your ad spending to tailor your content to the right people for maximum impact on a leaner budget.

The benefits of analytics aren’t limited to marketing. Tracking expenditures over time, for example, can provide insight into where your organization’s biggest expenses are—and provide clues on how to run a more efficient operation. Anywhere data can be gathered, analyzed, and compared against key performance indicators, it can be used to effect change and improve outcomes.

Data Analyst Salary Ranges

While salaries for Data Analysts can vary greatly by industry and region, the average salary for a Data Analyst in the U.S. is $73,673.

Range of average salaries for Data Analysts:

  • Entry level: $69,827
  • Intermediate level: $78,026
  • Senior Data Analysts: $87, 211

Demand for Data Analysts

Demand for Data Analysts and for data skill is surging. According to a report from McKinsey, the United States faces a shortage of up to 190,000 people with Data Analyst skills.

What’s more, the World Economic Forum (WEF) found that by 2022, 85% of companies will have adopted big data and analytics technologies. WEF also found that 96% of companies were planning or likely to plan to hire new permanent staff with relevant skills to fill roles related to data analytics.

Unsurprisingly, the role has been called one of the most in-demand jobs by LinkedIn, Glassdoor, the US Bureau of Labor, and Robert Half, among others.

Reasons to Become a Data Analyst

First, let’s consider the growth of big data. About 2.5 quintillion bytes of data are created daily, and as companies are ever-more diligent about collecting and utilizing that data, they have come calling for Data Analysts.

In fact, there’s more demand for Data Analysts than there are qualified candidates to meet that demand.

That’s because companies are wisely investing big in data. According to one a recent study by Fortune Business Insights, the global big data technology market size is expected to soar from $41.33 billion in 2019 to $116 billion by the end of 2027. It’s predicted that the increasing integration of AI and machine learning will lead that market growth, with industries including retail, manufacturing, IT and telecom, government, healthcare, and utility all expected to dramatically ramp up their investments in data.

Indeed, a Dresner study found big-data analytics was being adopted in huge numbers in telecommunications (95 percent adoption), insurance (83 percent), and advertising (77 percent), especially. It’s likely that’s still not enough – a McKinsey study, for example, found that if large retailers were maximizing the potential of data, they could increase their operating margin by more than 60 percent.

The widespread demand for data analysis talent has created a situation where it’s not only easier to get a Data Analyst job, but you’ll likely have a healthy salary as well. Many Data Analysts bring home well over $70,000 – it’s even possible in an entry-level role at the right company. If you’ve got significant experience under your belt, you might fetch more than $100,000.

It’s also a position that could be considered upwardly mobile. Data Analysts have the opportunity to make high-level business decisions and work closely with senior decision-makers, and it’s not uncommon to see Data Analysts move into managerial or more specialized positions.

There’s also the fact that many Data Analyst responsibilities could actually be met working from home if a company offers that kind of flexibility.

Data Analyst Education

When it comes to the educational background of a Data Analyst, there seems to be a wide degree of variance. Many people who want to become a Data Analyst are also coming into the field after completing a bootcamp, online course, or certification program to quickly immerse themselves in the field.

If they have a bachelor’s degree or master’s degree, it tends to be that – unlike Data Scientists – Data Analysts often have a background in business, economics, and the social sciences, where it’s rarer to see them come from a mathematics, statistics, or computer science background. In fact, a Data Scientist is almost 10 times as likely to possess a Ph.D. than a Data Analyst, and Data Scientists are twice as likely to hold a graduate degree. There are still people getting into the data science field after attending a data science bootcamp, as well.

That’s just one difference between the oft-confused professions. Let's take a look at related data jobs and how they differ.

What Tools Do Data Analysts Use?

Data Analysts use a number of different tools to collect and analyze data, and then to visualize and present insights for non-data professionals to understand.

Excel is the most widely used analysis tool, although that is changing. According to the BrainStation Digital Skills Survey, 66 percent of data professionals cited it as their most-used tool in 2020, down from 81 percent in 2019.

There are a number of potential reasons for the reduction in the number of Excel users, including the potential for accidental data loss in spreadsheets, as well as the inability to share data and information in real-time.

A close runner-up in terms of use by Data Analysts is SQL, the industry-standard query language used in database management, which is routinely used by 48 percent of respondents. Big-data number-cruncher Python is the most widely used tool for statistical programming, with 45 percent of respondents relying on it on a regular basis (coming in next at 15 percent, is R, another important player in this category).

We’ve also reached the point where more data professionals work in the cloud than don’t. Only 36 percent of respondents said they work without a cloud computing platform. Amazon Web Services is the most popular cloud platform here, used by 43 percent of survey respondents, with Google Cloud following at 24 percent.

Spark is the dominant processing framework—used to write applications across a range of operators for large-scale data processing—with 43 percent of professionals naming it their framework of choice; Hadoop, in second, claims a further 24 percent.

Tableau is by far the most widely used visualization tools, with 60 percent of Data Analysts using it. Other popular visualization tools include Matplotlib and ggplot2—Python and R’s respective visualization packages.

Overview of most popular tools for Data Analysts:

Excel
According to the BrainStation Digital Skills Survey, 66 percent of data professionals cited it as their most-used tool.

SQL
The industry-standard query language used in database management, which is routinely used by 48 percent of respondents.

Python
The most widely used tool for statistical programming.

Spark
The most dominant processing framework, used to write applications across a range of operators for large-scale data processing.

Tableau
The most widely used data visualization tool.

Data Analyst Career Path

Data Analyst is an excellent entry point into the world of Data Science; it can be an entry-level position, depending on the level of expertise required. New Data Analysts typically enter the field straight out of school – with a degree in statistics, mathematics, computer science, or similar – or transition into data analysis from a related field like business, economics, or even the social sciences, typically by upgrading their skills mid-career through a data analytics course.

Common Data Analyst Jobs

Here are some related data professions and how these roles differ from what you might see in a Data Analyst job description.

  • Data Scientist. Data Scientists estimate the unknown by writing algorithms, asking questions, and building statistical models. While both are responsible for analyzing data, a Data Scientist will have to do some coding (and know some programming languages). Data Scientists can arrange undefined sets of data using multiple tools at the same time, and build their own automation systems and frameworks. Where Data Analysts examine large data sets to identify trends and patterns, develop charts, and use data visualization to help companies make more informed, strategic decisions, Data Scientists design and construct new processes for data modeling and production using prototypes, algorithms, predictive models, and custom analysis.

  • Data Engineer. Data Engineers typically are people who have a master’s degree in a field relating to statistics, mathematics, or computer science, or they’ve accumulated significant experience as a Data Analyst. Data Engineers need a rock-solid technical background with the ability to create and integrate APIs. They also need to understand data pipelining and performance optimization.

  • Business Analyst. Business Analysts do use data to make strategic decisions, but their duties tend to be more focused on assessing business processes for efficiency and cost, communicating what they’ve learned to high-level executives and key stakeholders, and finally offering strategic recommendations to improve processes, procedures, and performance. In simpler terms, their responsibility is to analyze data to develop recommendations that address clear business needs.

  • Product Manager. Product Managers oversee a product from ideation through to the entire product lifecycle. Although Product Managers are also expected to have a solid understanding of data analysis, once the product has matured to a certain level, that’s typically where a Data Analyst would come in to provide insights that would continue to grow and improve the product. They might make recommendations to the Product Manager, who have to consider a range of factors that a Data Analyst might not. Some Data Analysts might look to become Product Managers after gaining a few years of experience.

  • Quantitative Analyst. Quantitative Analysts are in high demand, especially in financial firms. Quantitative Analysts use data analytical skills to seek out potential financial investment opportunities or risk management problems. They often also venture out on their own, creating trading models to predict the prices of stocks, exchange rates, and commodities. Some even go on to open their own firms.

  • Operations Analyst. Usually found in large companies, Operations Analysts focus on the internal processes of a business. The scope of their job could cover internal reporting systems, product manufacturing and distribution, and the general streamlining of business operations. Business savvy and technical knowledge of the systems they’re working with are both must-have qualities for Operation Analysts, who work in virtually every type of business, from grocery chains to the military.

  • Marketing Analyst. Data analytics plays a crucial role in digital marketing – without a proper system of analytics in place, it would be easy for companies to waste a lot of time, money, and resources on campaigns that won’t actually drive traffic. Marketing Analysts often use tools like Google Analytics, custom reporting tools, and other third-party sites to analyze traffic from websites and social media and spot trends. Performing these functions only requires a basic understanding of data analytics, but those professionals with a higher-level understanding could be set up for a very nice career.

One reason Data Analysts need to be effective communicators is that they work with a diverse cross-section of people. The wide variety of Data Analyst roles and responsibilities means you’ll be collaborating with people across many different departments, including executives, salespeople, marketers, managers, clients, and even users. You’ll also collaborate closely with people in more technical roles, including Data Scientists, Data Architects, Database Developers, and Machine Learning Engineers.

To communicate clearly and efficiently with that diverse bunch, Data Analysts have to be adept at both handling highly technical discussions and also understanding when and how to simplify their message for non-technical audiences – after all, not everyone is well-versed in data analysis. Since Data Analysts need to persuade stakeholders that their insights are worth acting upon, they should be confident and compelling speakers with a knack for presenting information.

Although it certainly does require an elusive mix of technical skills and communication skills to secure a great career in data, there are many reasons why it’s worth it to become a Data Analyst.

What Skills Do You Need to Be a Data Analyst?

A successful Data Analyst needs to possess a mix of technical skills and more intangible qualities.

On the more technical end of the spectrum, Data Analysts do need to master SQL – the formal language used to query a set of structured data – and they should be familiar with statistical programming tools like R and Python, and of course, Excel is a must. A good Data Analyst should be able to use a program like Tableau to produce clear, esthetically impressive visualizations. And it’s hard to excel as a Data Analyst if you don’t have a head for math, including concepts like statistical regression and mathematical modeling.

However, it’s perhaps the “soft skills” or intangible qualities that really separate a good Data Analyst from a great one. For instance, a Data Analyst needs to have great business sense. A Data Analyst’s job is to understand a company’s needs and challenges and conjure smart solutions. To put it more simply, a top-tier Data Analyst sees things others don’t.

Communication is also crucial to flourish as a Data Analyst. If you can’t effectively present your insights to others in a compelling, accessible way, it will be hard to build a compelling business case that what you’ve unearthed has merit. To that same point, a good Data Analyst hasn’t just mastered the technical tools required to create beautiful visualizations -- they must also possess an eye for design. Understanding how to present your information in a clean but eye-catching way isn’t necessarily something that comes easily to everyone, but it’s crucial to thrive as a Data Analyst. Technical skills all aspiring Data Analysts should develop:

SQL
The industry-standard query language used in database management, which is routinely used by 48 percent of respondents.

Excel
According to the BrainStation Digital Skills Survey, 66 percent of data professionals cited Excel as their most-used tool.

Statistical Programming
R or Python are used to run statistical functions, including regression analysis, linear and nonlinear modeling, statistical tests, and more.

Data Visualization
Data Analysts using programs like Tableau, PowerBI, Bokeh, and more to produce easy-to-read chord diagrams, heat maps, scatter plots, and more.

Find out more about what skills you need to become a Data Analyst.

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