Data analyst: a key role for data-driven business decisions

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Help organisations understand the current state of the business by interpreting a wide range of data



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26 September 2019 | 0

Data analysts work with data to help their organisations make better business decisions. Using techniques from a range of disciplines, including computer programming, mathematics, and statistics, data analysts draw conclusions from data to describe, predict, and improve business performance. They form the core of any analytics team and tend to be generalists versed in the methods of mathematical and statistical analysis.

The rising demand for data analysts

The data analyst role is in high demand, as organisations are growing their analytics capabilities at a rapid clip. In April, IDC forecast revenues for big data and business analytics solutions would hit $189.1 billion (€173 billion) this year and would see double-digit growth through 2022.

“The rate of change in business and in technology, it’s just never been more accelerated, particularly in analytics,” says Rita Sallam, distinguished VP analyst at Gartner. “As organisations digitally transform, as they add digital processes across all of their business, including the products themselves, data and analytics are becoming increasingly important.”




While organisations have spent the past few years focused on data science, machine learning, and artificial intelligence (AI), the pendulum may be swinging back to analytics, says Caroline Carruthers, director at consulting firm Carruthers and Jackson, former chief data officer of Network Rail.

“We almost took our eye off the analytics ball because a lot of people got excited about machine learning and AI and suddenly went, ‘Ooh, we have to do all these wonderfully whizzy-bang things.’ We forgot that actually there is a tremendous amount of value organisations get from analytics,” Carruthers says. “We’re starting to move back to how can we really drive analytics throughout our organisations.”

Data analyst vs. data scientist

While data analysts and data scientists may be commingled on the same data analytics teams, their roles differ considerably.

Data analysts seek to describe the current state of reality for their organisations by translating data into information accessible to the business. They collect, analyse, and report on data to meet business needs. The role includes identifying new sources of data and methods to improve data collection, analysis and reporting. Data scientists, on the other hand, are often engaged in long-term research and prediction, while data analysts seek to support business leaders in making tactical decisions through reporting and ad hoc queries.

Hillary Green-Lerman, lead data scientist at Looker, says the difference between data analysts and data scientists comes down to timescale. Data analysts are seeking to describe the current state and data scientists are seeking to predict and/or understand the future. A data analyst might help an organisation better understand how its customers use its product — what works and does not work for them. A data scientist might use the insights generated from that work to help design a new product that anticipates new customer needs.

“A lot of folks think of data analysts as just junior data scientists; someone who wants to be a data scientist when they grow up,” says Green-Lerman. “Sometimes it’s true, but I actually find that a really good analyst has a different skillset than a data scientist.”

Data analyst role

Data analysts mostly work with an organisation’s structured data. They create reports, dashboards, and other visualisations on data associated with customers, business processes, market economics, and more to provide insights to senior management and business leaders in support of decision-making efforts. Data analysts work with all manner of data, including inventories, logistics and transportation costs, market research, profit margins, sales figures, and so on. They use this data to help the business estimate market share, price products, time sales, optimise transportation costs, and the like.

Data analyst responsibilities

Data analysts seek to understand the questions the business needs to answer and determine whether those questions can be answered by data. They must understand the technical issues associated with collecting data, analysing data, and reporting. They must be able to recognise trends and patterns. According to Workable, key data analyst responsibilities include:

  • Analysing data using statistical techniques and providing reports
  • Developing and implementing databases and data collection systems
  • Acquiring data from primary and secondary sources and maintain data systems
  • Identifying, analysing, and interpreting trends or patterns in complex data sets
  • Filtering and cleaning data
  • Working with management to prioritise business and information needs
  • Locating and defining new process improvement opportunities

Data analyst salary

According to data from Robert Half’s 2020 Technology and IT Salary Guide, the average salary for data analysts/report writers in the US, based on experience, breaks down as follows:

  • 25th percentile: $83,750 (€76,600)
  • 50th percentile: $100,250 (€91,700)
  • 75th percentile: $118,750 (€108,650)
  • 95th percentile: $142,500 (€130,350)

Employment search engine Indeed, on the other hand, puts the average data analyst salary in the US. at $65,150 (€59,600) per year. Indeed notes that data analysts can typically earn the most in non-traditional tech areas.

Top 5 cities for data analyst salaries (adjusted for cost of living)

Rank Location Average salary Average salary adjusted
for cost of living
1 San Antonio, TX $83,017 $87,666
2 Des Moines, IA $77,983 $82,272
3 St. Louis, MO $69,750 $75,457
4 Dallas, TX $67,625 $66,999
5 Atlanta, GA $65,000 $66,657

Data analyst skills

According to Indeed data, the following are the most in-demand tech skills for data analysts:

  1. Machine learning
  2. Scripting
  3. SQL
  4. Stata
  5. Microsoft Excel
  6. Tableau
  7. Python
  8. R
  9. Microsoft SQL Server
  10. SAS

While machine learning tops the list, Indeed notes that only 3% of data analyst job postings include it. Green-Lerman explains that machine learning remains the domain of data scientists.

“What I’ve started doing is referring to data science as the broad area of all things data and then dividing that into a machine learning scientist and a data analyst,” Green-Lerman says.

Green-Lerman says she believes organisations will increasingly post data scientist job listings as “data scientist, machine learning” and data analyst job listings as “data scientist, analytics.”

“The reason I think that sort of phrasing is happening is a salary issue,” she says. “Data analysts are generally paid a lot less, but someone who’s doing your analytics work probably needs similar education levels as someone doing your machine learning work.”

In addition to analytical and mathematical skills, and facility with languages such as SQL, communication skills are essential. Data analysts frequently need to engage with the business to understand business objectives and gather requirements.

Landing a data analyst job

Green-Lerman says an eclectic mix of skills and experience is key to getting noticed when applying for data analyst positions, though facility with SQL and statistical analysis is a requirement.

“The things that I generally am looking for on my team are good communicators and writers. Everything on your resume should look professional and be spelled correctly because a big thing that analysts do is write reports. I usually want folks who have some experience beyond a bootcamp or a master’s program. I want them to have some practical experience, even if it’s an internship,” Green-Lerman says.

In addition, she looks for resumes that describe working on at least one analytical project in detail.

“I love it if there are actually numbers in their resume because that means they’re already thinking in terms of demonstrating value and KPIs. But not everyone’s job lends itself to that, so that’s not a hard requirement,” she says.

Data analyst education and training

While there is no set education requirement for data analysts, most data analysts have at least a BS in mathematics, economics, computer science, information management, or statistics. Coding bootcamps can help, and internships can provide experience that many organisations are looking for.

Data analyst certifications

Data analytics skills are in high demand and are relatively rare. Individuals with the right mix of experience and skills can demand high salaries. The right big data certifications and business intelligence certifications can help.

Some popular certifications include the following:

  • Certification of Professional Achievement in Data Sciences
  • Certified Analytics Professional
  • Cloudera Certified Associate (CCA) Data Analyst
  • EMC Proven Professional Data Scientist Associate (EMCDSA)
  • MCSE: Data Management and Analytics
  • Microsoft Certified Solutions Expert (MCSE): Data Management and Analytics

Other data analytics jobs

Data analyst is just one job title in the expanding field of analytics. Here are some of the most popular job titles and the average salary for each position, according to data from PayScale:

  • Analytics manager: $94,000 (€86,000)
  • Business intelligence analyst: $67,350 (€61,600)
  • Data architect: $115,000 (€105,200)
  • Data engineer: $91,650 (€83,800)
  • Data manager: $61,150 (€55,900)
  • Data scientist: $96,050 (€87,850)
  • Database administrator: $72,800 (€66,600)
  • Database developer: $74,650 (€68,300)
  • Research analyst: $54,650 (€50,000)
  • Research scientist: $78,600 (€71,900)
  • Statistician: $71,350 (€64,250)

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