There’s never been a better time to enter the field of data analytics. By some estimates, roughly 2.5 quintillion (that’s 2,500,000,000,000,000,000) bytes of data are being generated every single day and by 2025, there will be 175 zettabytes (that’s 1,000,000,000 terabytes) in the global datasphere. The explosion of modern data generation has likewise created a boom in long-term career opportunities in a challenging and well-compensated field.
Data analytics is the science of analyzing data and extracting useful conclusions to inform an organization’s strategies and operations. Organizations around the globe have embraced data-driven decision-making to more effectively discover opportunities, solve problems and mitigate risk.
But before we get into more detail, it might be useful to draw a distinction between two thriving and often-confused careers in the industry known broadly as “Big Data” or “data science.”
What’s the difference between data analytics and data science?
If you’ve ever searched for data analytics job postings, you’ve likely also received results for another interconnected field: data science. While the two share many similarities, they differ in scope, skills, responsibilities and goals. Understanding these differences is vital in order to determine which is the best career path for you.
Both data science and data analytics use data to solve problems. But data analytics is a branch of data science, and it has a narrower focus.
Data analytics uses processed, historical data to identify trends, generate actionable insights and answer questions to drive better business strategies.
Data science takes raw data from various unconnected sources and creates algorithms, predictive modeling processes, and other custom analyses to shape raw data into insights.
In other words, data analytics makes sense of existing information. Data science finds innovative ways to capture and analyze the data used by analysts.
Data science is a more technical field, and typically requires more advanced education and coding expertise.
Data analytics, while still a complex and challenging field with plenty of career opportunities, doesn’t require quite the same depth of mathematical and programming expertise as data science. Consequently, data analysts can find it easier to get into the field, especially if they bring additional work experience with them, or are using data analytics skills to enhance existing business knowledge, for example.
Data analysts require a unique blend of technical and “soft” skills in order to decipher and communicate meaningful conclusions and solve problems. A data analyst’s toolbox must include:
Math
Math is a core element of data analytics, particularly probability and statistics. Depending on the complexity of your job, the volume of data and algorithms involved, you may need some fluency in calculus and algebra as well. There’s some debate in the data analytics field over exactly how much math is really required and which skills you need, but the general consensus is that any successful data analyst must be very good with numbers.
Computer science
Data analytics requires versatile computer skills, including programming, databases and data analysis tools. Some of the most common tools employed in data analytics range from the everyday like Microsoft Excel to more complex programs and languages, such as Python, R, JavaScript, MATLAB, SQL and machine learning. Then there are visualization tools like Tableau and Qlikview, which are a key element in delivering the insights gained from the analyzed data.
Communication
One major challenge for some data analysts is effectively communicating the patterns and insights you’ve found to others who don’t have the same level of technical expertise. Once you’ve found the numbers in your data, it’s vital to know how to communicate the insight. A data analyst must be multilingual – fluent in the highly technical aspects of the work while also able to provide clear explanations to decision-makers. A data analyst must also be adept in different formats, ready to explain findings and conclusions verbally, textually and visually.
Data analytics in action
There are four main types of data analytics:
Descriptive: uses data to examine, understand and describe something that has already happened. For example, data showing a decrease in monthly sales.
Diagnostic: works to explain why something happened. Why did a company’s sales go down?
Predictive: uses historical and other data to forecast the near future. What is likely to happen to our company’s sales in the next quarter?
Prescriptive: aims to provide a course of action. What steps can our company take to improve sales based on insights about our target customer and past increases or decreases in monthly sales?
Organizations then leverage these analytics to inform and enhance their operations, applying them to key areas like budgeting, risk management, marketing, sales and product development.
Data analytics skills open opportunities in an enormous range of industries and sectors, including life sciences, media, financial services, technology, retail and many more. Those skills can also lead to various data specialties, including these common career tracks:
Data Scientist
Many data scientists start their career as data analysts. Armed with foundational data skills, a data analyst can level up their advanced math and programming abilities and develop new skills like an understanding of machine learning and transition into data science. Many data scientists are employed “in-house” at companies, organizations or governments, for example, to handle ongoing data needs.
Specialist
A data analyst may choose to focus their skills on a particular industry or sector. Here are just a few of the specialized roles available to those with data analytics skills.
- Business analysts examine data specific to a business and use it to optimize internal operations, such as manufacturing processes or organizational structures.
- Financial analysts use data to target investment opportunities, identify potential revenue streams and mitigate financial risk.
- Market analysts consider market trends to determine which products and services to sell, the effective price points, and how to best target customers.
- Actuaries analyze accident, disability, sickness, retirement and mortality rates to create probability tables and forecasting strategies for insurers.
- Web analysts monitor website activity to optimize user experiences and improve website conversion metrics.
- Marketing analysts pore over campaign analytics across various channels, including social media, email, and web traffic, to develop and optimize digital advertising campaigns.
- Healthcare analysts work for public health organizations, from government ministries to hospitals, to understand everything from wait times for certain procedures to the value for money being delivered from different healthcare equipment.
- Insurance and credit analysts use massive data sets to evaluate risk and develop financial and insurance products that determine everything from how much credit you qualify for to how much you will pay to insure your home.
- Data journalists analyze data sets like crime, economic or sports statistics, healthcare records or government employee salaries to either create or supplement news content.
Consultant
After developing your skills and experience to a high degree, working as an external consultant can be an attractive option. This can involve freelancing or working for a consulting firm, conducting analysis for a wide range of clients. Freelance consultancy in particular offers greater freedom to pick your own projects and hours, as well remote work opportunities.
Instructor
Along with the increased demand for data analysts is an increased demand for people to train them. Data experts with sufficient experience and excellent communication skills and a passion for teaching may find opportunities at colleges and universities to train the next generation of data analysts.
Data analysts tend to work in a team environment, collaborating with other analysts to interpret data and then with other departments and individuals within an organization, such as marketers, salespeople, developers and executives.
The work tends to be office-based with the typical accompanying schedule, 9-5, five days a week. Overtime and weekend work is sometimes required to meet aggressive deadlines or complete major projects.
Most of the work is done at a computer, so will involve long hours at a desk.
Like data science, the data analytics field has a distinct gender gap. A 2020 report from Boston Consulting Group found men make up nearly 80 per cent of the workforce and the bulk of senior-level positions as well, although the STEM fields are slowly growing more diverse.
Overall, data analysts benefit from a safe working environment. The most often reported cons of the job are heavy workloads and the accompanying stress, as well as the health concerns stemming from long hours seated in front of a computer.
There’s no better time to be a data analyst. As it also turns out, there’s almost no better place. According to a 2021 report from SalaryExpert.com, the pay for an entry-level data analyst in Canada is the fifth-best on the planet.
The average salary for data analysts in Canada is $70,676, according to Talent.com. Entry-level positions pay just over $65,322, while the most experienced candidates can earn closer to $140,000 per year. This is, on average, less than the average salary for a data scientist.
Compensation can vary widely, depending on the company, industry and location. For example, data analysts in Quebec have the highest average pay ($135,384), while Manitoba is on the low end of the scale at $82,466, according to Talent.com. Ontario sits near the top, at $129,492 annually, along with Nova Scotia ($125,295) and New Brunswick ($123,698).
Expect salaries to be highest in the financial services field (banks and insurance companies in particular) although professional services, media and technology, and even some larger retailers, offer generous pay.