The job role of a data analyst in today’s tech market is broadly defined.
In general, experts talk about data analysts as people who collect data and use that data to provide insights. They are “translators” of raw numbers and data points into digestible information that helps to direct companies and move markets.
Let's look at some of the major factors in what the data analyst does. One factor is the data environment – data analysts who are only working with Hadoop clusters are going to work differently than data analysts who are using high-powered artificial intelligence and machine learning platforms. Data analysts who are using a relational database will use SQL queries to route information. Others will use other types of analysis tools including fancy ERP software that takes business data assets and puts them where they need to be.
As far as core skill sets, data analysts need to be good with numbers. They need good verbal and written communication skills and critical thinking skills. They need mathematical expertise and acumen. (Think all analytics jobs are being automated? Think again: No, Data Analytics Bots Aren’t Going to Steal Your Job Anytime Soon.)
The Data Analyst: A Day in the Life
A piece by Ashley Brooks at Rasmussen College goes into the typical day in the life of a data analyst. You have reporting, which also varies according to the technology tools that you’re using. Brooks also talks about “spotting” patterns in data – another take on this refining of raw or unfiltered data into more useful formats and receptacles.
As Brooks points out, there’s also an infrastructure element here – data analysts may be deeply involved in using middleware to port data into and out of central data warehouse environments. In addition to SQL, data analysts may use Microsoft Excel, Google Analytics, Tableau or other tools.
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A Big Data World
When you really look at what a data analyst’s job is like, especially now in the new artificial intelligence age, it’s really a very diverse landscape, and that requires a range of skills.
“A data analyst is constantly learning every single day,” says Johnny Hilgers, EVP, business analytics at Spring Venture Group. “Optimizing data into actionable information to meet business objectives is an art and a science. To build a reliable and accurate model, hundreds of questions must be answered before anything is put into production.”
Some of those questions, Hilgers says, involve considering what algorithm or combination of algorithms is right for a task, what attributes will be the most helpful for feature selection, what attributes can provide the most lift in terms of feature engineering, what attributes should be left out, and what evaluation metrics the project is optimizing for.
Dr. Sonja Jones, in describing the data analyst role, points out how specifics differ. Jones is senior sales engineer at Bay Dynamics and cyber analytics and college professor for Carnegie Mellon University.
“A data analyst collects data for all divisions of a company,” Jones says. “Some areas … are sales, market research, logistics, supply chain, transportation, human resources, information technology and cybersecurity, to name a few. The data analyst’s job is then to take the data, interpret it, understand it and help companies make better business decisions based on the results. This can fall into many areas such as business intelligence, big data, data warehousing, analysis, data mining, predictive analysis, etc. … analytics is used for many use cases across various vertical markets such as finance, healthcare, retail, telecommunications, manufacturing, entertainment … data analysts have to have very strong business and technical skills as well as very good communications skills.”
The “Data Translator”
Again and again, we keep coming back to the idea that a data analyst is fundamentally responsible for taking data and making it digestible, for translating data assets into insights.
“A data analyst has to be able to analyze all the data that a business has on hand, and use that data to then gain scientific information to help companies make better decisions,” says Sepehr Shoarinejad, the president of big data analytics company Koridor. “That being said, they have to be able to convert the information that they get into plain English solutions for businesses to execute on. For example, a data analyst will use the data of all product transactions for a company and be able to help them decide which products are not selling and which products they are losing money on because of inventory shortages.”
“The job of a data analyst is to translate complex numbers and information into plain English for other team members to interpret and use it to improve various aspects of the business,” adds Clare Watson, operations director at Zolv. “This can be anything from sales figures or market research to logistical information. The purpose of having a data analyst is for the company to make better informed decisions in how they conduct their business.”
The Data Analyst’s Evolving Role
Some who are on the front lines of the massive changes going on in our IT world are remarking on how this is changing the data analyst’s job. Where just a few years ago, predictive analytics and its ilk were new and untested areas, now a proliferation of “smart tools” has led to a very different context.
“The work that data analysts and data teams do today has become unrecognizable compared to five years ago,” says Harry Glaser, CEO and co-founder of Periscope Data. “As the size and complexity of data volumes increase and make a bigger impact on IT, I’ve seen the key people involved in the data workflows move from dedicated specialists – including data integrators, scientists, modelers and business analysts – to cross-functional data teams that blend all of their skills together … today’s data analysts can’t survive if they just deliver basic business metrics and performance indicators. They now have to work collaboratively to create sophisticated analyses that allow a business to predict the future success of the business.”
Glaser gives a concrete example of the expectations put on data analysts in today’s market.
“Traditionally, business analysts might be focused on measuring straight-forward business metrics like customer churn, but they would have rarely been tasked with predicting future churn,” Glaser says. “Those tasks would fall to data scientists with more experience and different backgrounds entirely. But today, market pressure is forcing data analysts to tackle natural-language processing, model construction and predictive analytics … As a result, languages like Python and R are becoming more commonplace for analysts.”
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“In previous generations, a data analyst could only account for the data they were given,” says Nate Masterson, CMO of Maple Holistics, a company dedicated to cruelty-free, natural, and sustainable personal care products. “However, the question is less ‘What does the data say?’ and more ‘What data should be looked at?’ nowadays in the wake of the information age. Remember those math word problems we all hated in school? That’s data analytics backward. A data analyst's job is to translate numbers into words. The problem data analysts are trying to answer is ‘Why are the numbers the way that they are?’ They look at data points and try to put them together as part of the bigger picture. They also work to streamline and make things more effective/efficient and create tomorrow’s wins out of yesterday’s losses.”
All of this illustrates how incredibly useful and varied data analyst roles are as data becomes more and more important to nearly any kind of business. Young career professionals can use this insight to cultivate the skills and experience needed to make themselves indispensable to a future employer.