What Does a Data Analyst Do? A Full Breakdown.

If you love puzzles and have a soft spot for math, a data analyst role might prove to be the right career choice for you. Over the last few decades, the amount of consumer, product, and industry information that companies need to sort through and analyze has skyrocketed, pulling the demand for talented data professionals along with it. 

But what is a data analyst? And what do data analysts do, exactly? In simple terms, these information wranglers collect, organize, and analyze information to produce actionable insights on industry trends for their employers. When they do their jobs well, companies in all sectors — not just the tech industry — stand to benefit. 

The research supports that point. In 2019, researchers from McKinsey noted in a whitepaper that out of the studied businesses, those who implemented data and analytics teams tended to perform better than those who lagged in incorporating such technology. Respondents from the highest-performing organizations were three times as likely to say that their “data and analytics initiatives have contributed at least 20 percent to earnings before interest and taxes (EBIT) over the past three years.” 

These numbers are compelling. To borrow a quote from organizational theorist Geoffrey Moore, “Without big data analytics, companies are blind and deaf, wandering out onto the Web like deer on a freeway.” 

Customers that visit a company’s website leave a vast store of useful data, even if they never complete a purchase or interact with employees. If businesses can distill meaning out of the information they collect, they can gain a deeper and more thorough understanding of their customers’ behaviors, spending patterns, and market needs. In some cases, they may even have a better chance of optimizing their business strategy for market success.

In April of 2019, researchers for Deloitte surveyed over 1,000 executives about their perspectives on the growing role of data analytics in business and found that the majority believed analytics to be on par with long-established business drivers such as product innovation, risk management, and managing growth expectations. 

A full 58 percent of those surveyed said that their organizations leverage analytics to identify business process improvements, while 55 percent do so to understand and improve the consumer experience, and 53 percent apply insights gleaned from analytics to guide their business strategy.

Chart showing the top reasons companies use data analytics

But analytics alone can’t prompt success; business needs talent for that. As Victor Nilson, Senior VP of Big Data for AT&T, once commented for McKinsey, “Talent is everything, right? You have to have the data, and, clearly, AT&T has a rich wealth of data. But without talent, it’s meaningless. Talent is the differentiator. The right talent will go find the right technologies; the right talent will go solve the problems out there.”

So, let’s get into it — what, exactly, does a data analyst do?

What Does a Data Analyst Do?

Simply put, data analysts take all of the data that companies receive and use it to determine customer and sales trends, forecast market changes, and obtain other useful insights that can help employers make strategic business decisions. These data wranglers further use the information a company collects to identify problems, improve growth, or even increase customer satisfaction and reach. They can code, collect raw data, analyze information, parse useful information from white noise, and note significant patterns or trends, as well as source information from their company’s website, databases, and other consumer touchpoints.

Due to the nature of their role, data analysts are often also involved in shaping and constructing the complex databases that empower companies to manage and analyze data adeptly. After forecasting trends and identifying useful information, they frequently present their findings to colleagues who are involved in developing products or planning marketing campaigns to ensure that their conclusions are heard and considered.  

It’s clear that data analytics professionals are crucial to companies in the tech industry. After all, some of the world’s largest tech companies — think Google, Facebook, and Amazon — have redefined their respective fields through their use and processing of data about their customers. 

But businesses in tech aren’t the only ones that benefit from data analytics. Skilled data analysts are in high demand in nearly every industry, including investment banks, private equity firms, healthcare and health insurance providers, retail, insurance companies, and marketing companies. 

A Day In the Life

Now that we’ve laid out a high-level view of what a data analyst is, we can zoom in on what data analysts do on a day-to-day basis. Interestingly, while analysts are in demand across a wide range of industries, they tend to perform a similar roster of information management and analytical tasks. Below, we’ve highlighted a few of the most common tasks you’ll need to tackle as a data professional. 

Mining Data

If you want to become a data analyst, you’ll need to know how to mine. Data mining is one of the core components of a data analyst’s job. Just as miners sift through soil and rock to find gold and other useful minerals, data miners sort through the “mountains” of pure data companies generate in the hopes of uncovering useful information.

Want an example? Turn to the humble loyalty card at your local grocery or department store. These cards provide the information that data analysts need to identify customer trends, gauge the appeal of promotions, and note which products are most in-demand. Other practical uses of data mining include detecting spam email and directing it to the “Junk” folder, identifying potentially fraudulent credit card transactions, and determining which types of marketing campaigns are most useful for different segments of a company’s database. Data analysts use software to manage, store, and assess the data that comes in, and gather relevant information into a final, layperson-friendly presentation.

Cleaning Data

Not all data offers readily reliable information. Before data analysts can conduct meaningful analysis, they need to clean their data. During this process, analysts strive to correct data marred by spelling or syntax errors, bring in information from a different or previous model, and identify and correct duplicated or missing information. 

Incorrect, irrelevant, duplicate, and corrupted data must be removed from the set before it misleads analysts to flawed or inaccurate conclusions. Cleaning data produces standard, uniform datasets that are comprised of relevant information.

As data researcher Rephael Sweary wrote in an article for Forbes, “Without [clean data], leadership can’t trust they’re making sound strategic decisions. Once an organization has a dirty data problem, the mess that follows isn’t pretty. Poor data quality inevitably leads to dissatisfied customers, poor order to cash, and inability to forecast earnings.”

As you might guess from Sweary’s conclusions, cleaning data is one of the most important responsibilities a data analyst holds. Research backs this truism; survey results published in 2018 by Gartner indicate that poor data quality results in an average of $15 million worth of lost revenue each year. 

Recent research from Forrester Consulting provides a case study of this (PDF, 395 KB) in the marketing industry. According to Forrester, wasted media spend is the highest-ranked negative result of low-quality data, with roughly 21 cents of every media dollar spent coming to naught. This waste, in turn, led to inaccurate targeting and lost customers. Data cleaning is inarguably vital to making the most of business resources.

The biggest consequences of poor data quality

Locating Trends and Patterns in Datasets

Once data analysts mine and clean information, they use it to identify potentially useful consumer trends and patterns (PDF, 1.2 MB). Big Data has changed the game when it comes to predicting future market trends and determining optimal strategic decisions. 

Rather than relying on faulty instincts or vague feelings to assess how consumers are responding to, say, an ad campaign, business leaders can receive feedback near-instantaneously and use their data analysts’ findings to revise or further their project strategy. 

With modern online marketing, data analysts can track and understand every aspect of how customers respond to different types of advertising. They can also understand which customers respond well or poorly to different marketing styles, helping businesses to target specific ads to specific audiences. 

Data analysts don’t cap their capabilities in analyzing past data, either; they can also locate promising trends, areas of interest, and cultural “tipping points” that can direct a company’s attention towards higher profitability.

Generating Data Reports

Let’s get one point straight: Data reporting is different from data analysis. Many of the statistics and data that we encounter in daily life — think graphs, charts, statistics, etc. — are data reports, rather than strict analyses. What data analysts do when they generate reports is provide brief snapshots of significant trends and patterns. 

Think of election poll information as an example; when you pull up a real-time polling data chart, you can see at a glance which candidate is drawing in the most votes. What you don’t see is the underlying data driving those numbers. Without further analysis and information, the average person wouldn’t have the detailed insights they would need to understand the results of an upcoming election. Data reports offer a quick and insightful snapshot, not a comprehensive explanation. 

That said, clear and accurate data reporting is an essential part of the data science profession. Data reporting can identify critical questions and pinpoint important items of concern. Many business leaders, managers, marketers, and others that you would interact with as a data analyst do not have the technical skillset to mine and understand the data themselves. 

When you produce easily understandable charts, graphs, and reports, business leaders in non-technical fields can grasp the results of your data science work and put it into practice. Being able to report information in a way that is understandable to non-specialist professionals is crucial to a successful career as a data analyst. 

Creating or Maintaining Databases

Not every data analyst will administer or manage a database, but having an understanding of the principles that underlie database management is nevertheless essential. First, let’s distinguish between the two roles: A database administrator is charged with maintaining and developing reliable systems that can receive, manage, and report data, whereas a data analyst takes that information from the database to identify trends and draw further conclusions. 

When data analysts understand how to navigate, organize, and find the datasets they use, they are better able to understand the information they receive, identify issues and problems, and make previously unnoted connections. Having a thorough technical grounding in database creation and management can make you more marketable as a data analyst.

Tools Data Analysts Use Regularly

  • Excel — Sure, it’s an incredibly common program, but it’s an industry staple for a reason. Microsoft’s spreadsheet program offers data analysts a means to report and share data across a broad user base. With it, you can produce easily understandable data reports from simple Excel tables or graphs.
  • Python — This widely-used programming language is designed to be easy to read and integrates efficiently and effectively across different operating systems.
  • SQL/NoSQL — SQL, or Structured Query Language, is a standard programming language that has been used to build, manage, maintain, and query relational databases. NoSQL databases, on the other hand, use JSON documents to store information in non-relational databases. NoSQL tends to be more flexible, scalable, and approachable for companies with massive datasets. 
  • Tableau — Tableau software contains an array of tools that you can use to identify outliers, view underlying information, or create new views of existing data.
  • Hadoop — This open-source framework offers the ability to store and manage large amounts of data and process it quickly, relying on a distributed computing model.

Data analysts use high-level technical skills to identify trends, understand customers and the public and, in many ways, predict the future. A solid technical grounding can lead to tremendous success in this growing and in-demand field. 

There are several educational paths that you can take to a career in data analysis. While some choose to earn a four-year university degree, career changers or those with additional responsibilities at home may find alternative options like boot camps a better path. These intensive learning opportunities offer the technical knowledge needed to forge a path in data science.

Now that you know what a data analyst does, which path will you choose to take? Explore your educational options to find out more about the career possibilities available to aspiring data analysts. 

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