8 Reasons Why Data Analytics is the Career for You in 2023

Companies are crying out for data analysts! No, this is not hyperbole. Whilst the demand for skilled analysts is huge, the supply leaves something to be desired.

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This means that even entry-level positions in data analytics are richly compensated as digital-age businesses seek to leverage consumer data to drive strategy, make investment decisions, assess risks and predict market changes. 

Aside from the practical business applications, data analytics is a varied and exciting field with a long list of career opportunities and roles, you're sure to find an industry that matches your interests. 

Let's go deeper into this topic!

TABLE OF CONTENT 

1. What is Data Analytics? 
2. 8 Reasons Data Analytics is the career for you
   i. Data generation is at an all-time high
   ii. Data Analyst are in demand 
   iii. The Salary and Progression are Excellent
   iv.   More Companies Are Adopting Data Analysis Technologies
   v.      You Can Get Started Easily
   vi.      You’ll become a key decision maker
   vii.       Lots of opportunities for growth
   viii.       Chance to work with household names

3. 5 in-demand data analyst skills

4. Conclusion 
5. FAQs

‍What is data analytics? 

Before we jump into why a career in data analytics is a great career choice, let's recap the definition:

Data analytics is the process of analysing raw data to create actionable insights that inform strategies and increase business intelligence. 

In simpler terms, data analytics takes large sets of unorganised and apparently random data and organises (or “cleans”) it to establish patterns, drawing logical conclusions that can then be passed on to help make business data-driven decisions. 

There are four types of data analysis that are used to answer business-related questions:  

1. Descriptive - answering “what happened?”

2. Diagnostic - answering “why this happened?”

3. Predictive - answering “what might happen in the future?”

4. Prescriptive - answering “what should we do next?”

These four types of data analysis are used to paint a picture of the wider story that data tells us and detail the next steps. 

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8 reasons data analytics is the career for you

1. Data generation is at an all-time high 

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Statista reports that by 2025, the annual revenue from data generation is set to reach $68 billion with 181 zettabytes of data being created. 

Customer data has become the world's richest currency. From it, businesses can determine all sorts of information such as:

• Behavioral patterns of their consumers

• What messages and campaigns do they respond well to

• Which platforms prove more fruitful

• Which demographics show positive responses and much more

   

All of this information is collected seamlessly and by scale. It's therefore crucial for businesses to be able to make sense of this data and to do that, they require talented data analysts. 

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 Information is the oil of the 21st century, and analytics is the combustion engine.                                         -Peter Sondergaard 

2. Data analysts are in demand

The US Bureau of Labor Statistics predicts that the demand for data analytics jobs will grow by 23% between 2021 and 2031, much faster than the average of 5% for all other industries.

Further to this, a report conducted by Analytics Insight predicted job openings for more than 3 million data professionals last year.

The last few years, especially since the pandemic, have given way to an astronomical rise in how data is collected and interpreted for business intelligence and decision-making.

As new digital technologies emerge to assist and enhance businesses, the data analysts will become more and more valuable leading to fantastic career progression and job security. 

3. The salary and progression are excellent 

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With great demand comes great salaries! Due to the evident skill gap in qualified professionals, companies are willing to pay top rates for someone with data analysis skills.

The graphic above shows the average salary for an analyst in the US sitting prettily at just under $75,000 per year, you'll also notice the entry-level figure being rather high at $53,000 per year.

As you gain experience and move through the ranks, the pay moves with it, offering some of the most competitive wages in the market (based on information from Indeed.com): 

• Junior Data Analyst: $53-58K 

• Data Analyst: $75K

• Data Analytics Consultant: $77K

• Senior Data Analyst: $97K

• Data Analytics Manager: $89K

• Insurance Claim Analyst: $51K

• Healthcare Analyst: $63K

• Marketing Analyst: $66K

• Financial Analyst: $72K

• Systems Analyst: $78K

• Data Scientist: $123K

• Machine Learning Engineer: $146K

• Chief Data Officer: $179K

Check out this data analyst salary guide for different countries around the globe. 

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4. More companies are adopting data analysis technologies 

New tech makes life a whole lot easier to perform complex and time-intensive tasks.

For larger businesses, they can have diverse and frankly staggering amounts of data sets to sort through.

The reason these same companies are looking to adopt technologies that support their strategies is this: implementing analytics tools gives them a competitive edge. 

These stats from DataProt reflect this:

• The global rate for adopting business intelligence is 26%

• 52% of software companies and 50% of finance companies use BI tools

• Organisations leave 97% of gathered data unused 

• The global business intelligence market was worth $24 billion in 2021

Virtually every sector uses analytics on some scale, from automotive to fashion, to retail and e-commerce, there's no shortage of choice for which industry you'd like to work in. 

TIP 

What is Business Intelligence?

Business intelligence is the use of data analytics, data mining, and other analytical techniques enabling them to identify opportunities and make data-driven decisions.

 

5. You can get started easily 

The best place to start a career in data analytics is by taking an online course.

Here, you'll learn the fundamentals around data, its uses and programming tools such as HadoopR.

The great thing about an online Data Analysis courses is the flexibility they offer. You can learn in your spare time or balance it around family and other commitments.

Most are self-paced and blend practical and video-based learning and often can be completed within a few months. 

Gaining knowledge around data analytics and completing a course to earn certification will dramatically increase your chances of pivoting careers.

Unsure which course to take? We made a handy list of the best online data analytics courses that you can check out.

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6. You’ll become a key decision maker 

Data analytics is a competitive resource for businesses as analysing data allows for more accurate and data-informed strategies, leading to better results and clearer decisions.

As a key component of this driving force, organisations and managers look to a data scientist for guidance on what to do next. Data visualisation can be a powerful ally.

Throughout your career in data science, you'll have the opportunity to collaborate with plenty of departments and individuals and help to deliver actionable insights. 

7. Lots of opportunities for growth 

Due to the sheer number of industries adopting data management you'll always have options for lateral and upwards movement.

Marketing, business intelligence, healthcare and finance are a few examples of data-driven sectors but there are dozens more all reliant on data. 

The technical aspect of data science means you'll always have a new area to explore and refine, from programming to advanced statistical analysis, there is an abundance of growth opportunities. 

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8. Chance to work with household names 

Generally speaking, the bigger the company, the bigger the reliance is on data scientists.

In terms of who's leading the charge for data analysis software, you can expect names like Microsoft and IBM at the forefront.

But when it comes to clients, there are some heavy hitters that prove alluring to data analysts:

• Bloomberg 

• Deloitte 

• Barclays 

• HSBC

• Experian 

• Amazon 

• Google 

• Netflix 

• Hyundai 

• General Motors 

• JP Morgan Chase 

• BT

• Apple 

• & many more

For an up-to-date list of available data analyst positions, head to Glassdoor.com.

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5 in-demand data analyst skills 

So now that we know some of the best reasons to pursue a career as a data analyst, let's look at the skills companies are actively seeking.

The following list should help point you in the right direction as to which skills are being valued more than others today. 

1. SQL - also known as Structured Query Language, is the standard way that data analysts communicate with databases and is used to clean, organise, extract and process data sets.

SQL is the most requested skill among employers and is foundational to data analytics making this a sensible place to start your learning. 

2. Statistical programming - open-source programmes such as Python and R allow fluent users to write computing language to clean, analyse and visualise data sets.

Both are considered relatively easy to learn for beginners and offer very similar capabilities although it's argued that Python is preferred for engineering-focused environments. 

3. Machine learning - a subset of artificial intelligence (AI), machine learning creates more accurate predictions over time with the more data it processes.

The self-learning algorithms have wide implications in business as they can predict trends based on historical contexts and especially in fields such as medicine for diagnosis.

4. Data management - the practice of collecting, organising and storing data in an efficient, safe and compliant manner.

There are specific roles for data management such as data architects and engineers but data analysts at any level can be expected to handle data in some form. 

5. Data visualisation - using visual elements like graphs, charts and maps to convey patterns and make sense of data for teams and organisations. One of the leading software for data visualisation is Tableau, which you can find more on here.

There's a lot to like about a career in data 

Varied, challenging, well-paid and with plenty of opportunity for growth and progression - there's a lot to like about a career in data analytics.

The skills mentioned above are great places to start and get a feel for the type of work you'll be doing as an analyst. 

Ready to start your journey as Data Analyst? We have a great course for you! 

FAQs

Is data analyst a good career?

The average salary for an analyst in the United States is just under $75,000 per year. The field is growing rapidly, with a projected 23 percent increase in job opportunities from 2021-2031, far more than the average 5 percent for any other industry. Companies such as Amazon, Facebook, and Google are actively seeking data analysts. This is a promising career choice with great potential for growth and development.

How much do data analysts make?

Here is a breakdown to help you better understand a data analyst salary:

• Junior data analyst: $53-58K

• Data analyst: $75K

• Data analytics consultant: $77K

• Senior data analyst: $97K

• Data analytics manager: $89K

Is data analyst a IT job?

A data analyst can be considered in part an IT job, as it requires technical skills and knowledge of data analysis tools and techniques. But it also requires a strong business understanding. It is a role that involves working with data from various sources to extract meaningful information and drive business decisions.

So I can't rely be considered a job related to computer science, but it can certainly be considered a job that is using IT on a daily base to drive decisions.

Data Analyst vs. Data Scientist

Data analysts and data scientists have similar roles in working with data, but their responsibilities and skills are different.

• Data analysts focus on interpreting and visualising data to provide insights and inform business decisions.

• Data scientists focus on developing and testing complex statistical models and algorithms to identify patterns and make predictions.

• Data analysts typically have a solid foundation in SQL, Excel, and data visualisation tools.

• Data scientists typically have a solid foundation in programming languages such as Python or R, machine learning, and statistics.

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