As an older career changer seeking new challenges, data analytics might seem an intimidating option. Perhaps you have concerns about starting from scratch, or about whether or not your skill level is suited to a new career in data analytics. On top of that, tech is mainly the domain of younger people, right? Wrong.
Ageism, in particular, is a known problem in the tech sector. A 2021 study by the University of Gothenburg found that: “Tech workers over 35 are considered old in the industry.” Ouch! Conversely, though—and somewhat paradoxically—experience is highly valued in the field. Indeed, domain expertise is one of the most sought-after skills for data analysts. So despite industry ageism, a recent study by Zippia showed that the average age of data analysts in the U.S. is 43 years old.
This takes us back to our titular question: are you too old to start a new career in data analytics? The short answer, in our opinion, is no. We’ll temper this by saying that there are challenges you may need to overcome, though.
In this post, we’ll look at exactly what it means to be an older person breaking into data analytics, identifying the benefits and challenges of being “older” in the sector, and some steps you can take to get ahead. We’ll cover:
- The benefits of being an “older” data analyst
- The challenges of being an “older” data analyst
- How to overcome age-related obstacles
1. The benefits of being an “older” data analyst
First up, a bit of a confidence boost! What are the tangible benefits of being an older person in data analytics? By “older” here, we’re referring to people aged 30+. That’s not to suggest that being over 30 makes you old. But as we’ve established, within tech, ageism is a problem, so we’ll play along for now and use a deliberately broad definition. To put it another way, as someone with more work and life experience, what are the benefits of being older in data analytics?
You have years of experience
Despite common misconceptions, not all companies want 22-year-old data analysts with degrees from Harvard! Older applicants often bring far richer life and business experience. And this depth of knowledge can put you ahead of the younger competition.
Whatever industry insights and professional networks you’ve built over the years will be incomparable to those of the average graduate. While you may not begin with the same technical skills as a young data graduate, hiring companies are increasingly alert to the fact that experience can offer more value than a technical qualification alone.
You have staying power
According to a study by the Korn Ferry Institute, the cost of replacing young talent at Fortune 500 companies ranges from 50-75% of the position’s annual salary. As an older data analyst, you’ll be seen as more reliable than younger hires.
Whether you’re planning to stick to a role for the foreseeable future or not, the reality is that younger people tend to job-hop more often than older people. The perception that you’re likely to stick to the job—whether that’s true or not—can work in your favor during the hiring process.
You have better writing, communication, and leadership skills
Technical skills, like programming knowledge or math, are vital for data analysts. However, the most underrated data analytics skills are ‘soft’ skills (‘power skills’ may be a better name). Carrying out the technical aspects of the role is important but means little if you can’t effectively communicate your findings.
From cover letters and applications to presentations and emails, older data analysts (and older workers, generally) tend to have much better writing, communication, and leadership skills, fine-tuned after years of experience. These are the power skills you can only develop over time and set your application apart from the rest of the pile. Plus, your ability to integrate these skills directly into any hybrid role makes you a greater asset.
Companies are seeking to fill diversity and inclusion quotas
Race, gender, and sexuality are all common diversity and inclusion watchwords. However, it’s often overlooked that age also plays a role. Okay, so it might not feel great to be the ‘token older person’, but filling diversity and inclusion quotas is a genuine attempt to address entrenched inequalities.
Filling quotas is more than a box-ticking exercise, though. As tech founders grow older they’re starting to see through the common misconception that ‘tech is by young people for young people’. Youth is not as desirable in data analytics, which needs better representation from all age groups. As companies see the value that older analysts’ unique worldview can add, they are adapting their hiring practices accordingly.
There’s a shortage of people for more senior data roles
For younger data analysts, the work required to become a fully-fledged senior data scientist is no mean feat. In addition to perfecting the technical skills, it means acquiring a broad wealth of life and work experience, additional qualifications, and domain expertise.
However, older people shifting careers into data analytics have much of this experience already. As such, if data science is a route you want to pursue, your experience will put you far ahead of the younger competition.
2. The challenges of being an “older” data analyst
Making a good impression while managing an unfamiliar work environment is tough for anyone starting a new job. It can be particularly hard if you’re older and breaking into a new industry. However, it’s not an insurmountable challenge.
In this section, we highlight some common challenges older people in data analytics face, along with some recommendations for overcoming them. Preparation is everything.
Staying updated on the latest data trends and tech
In fast-evolving fields like data analytics, it’s necessary to stay on top of the latest changes and innovations. Fortunately, the data analytics process is fairly well-tread and, therefore, is unlikely to change. What does adapt, however, are the tools and software that data analysts use. As an older person entering the field, it may be intimidating to see younger peers picking up these new skills with seemingly little or no effort.
How to overcome this challenge: Carve out time to read data analytics blogs, listen to data science podcasts, or chat with colleagues about their recent projects. Over time, you’ll start to spot which trends are worth noting, and which are simply ‘white noise’.
Connecting with younger colleagues
With more life and work experience, entering an industry filled with predominantly younger people can be intimidating—and potentially frustrating. In addition to being new to the field, it’s not always easy to know how best to communicate with younger peers. In some cases, your boss may be younger than you, too, which offers unique challenges.
How to overcome this challenge: Take the initiative and start conversations with your younger peers straight away. Aim to find something you have in common. It doesn’t have to be data analytics or something work-related; in fact, it probably won’t be. Perhaps you share a hobby, or both have young families. The main thing is to connect with your colleagues, peers, or boss and cultivate a relationship from there.
Finding a job with a suitable salary
A more practical challenge for older data analysts is finding a suitable salary. Younger workers usually have fewer commitments or financial concerns and can take on lower-paid jobs. If you have a mortgage to pay or a family to feed, though, this poses an additional challenge when taking a sideways step from a well-established career.
How to overcome this challenge: Do your research and have a clear expectation of what you can reasonably earn as an entry-level data analyst. Also explore similar roles, such as data science jobs, which tend to pay better. Mostly, though, don’t undersell yourself. All your transferable skills are fair ground for requesting a higher salary than a graduate. Your experience is an asset.
Additional life challenges can add stress
Making a career change later in life can be tough, especially when needing to hit those vital career progression benchmarks. First, there’s the need to update your skill set, while juggling existing responsibilities, like a full-time job, or a family. Plus, younger people tend to have more flexibility, such as the ability to relocate for the right job opportunities. And pulling the odd, unplanned late-night is more challenging for those with families.
How to overcome this challenge: The solution is all about balance and planning. Carving out and protecting even small amounts of time can help focus your progress. You should also start open and honest conversations with your employer about your outside responsibilities—this will also allow them to support you as best they can. Fortunately, data analytics tends to be a flexible job with remote working becoming the norm, which can help overcome some of these problems.
Lacking general confidence in your abilities
Making major career changes is intimidating, especially when you’re older. Even the most confident individuals can have occasional crises of self-esteem. Moving into a workplace ‘full of young people’ can also encourage a sense of impostor syndrome. And concerns about ageism in the workplace—even if you don’t face any direct discrimination—takes a toll.
How to overcome this challenge: Celebrate every achievement, no matter how small. Start by celebrating your decision to change careers, which takes guts! More practically, it helps to write down any concerns you have, such as gaps in your knowledge, such as data analytics software you are unfamiliar with, or simply more abstract concerns. Compare this list against solid evidence of your abilities. It’s a very effective way of identifying gaps in your skillset and showing where your strengths lie. Where there are gaps, fill them. Where there are strengths, play to them.
3. How to overcome age-related obstacles
As we’ve established, being an older data analyst doesn’t have to get in the way of a flourishing career. The main thing is to be honest about your strengths and weaknesses and to grab the opportunities available to you. And take the following steps to ensure that your age, far from harming your data analytics career, can work in your favor.
Immerse yourself in data analytics with books, blogs, and podcasts
From data analytics books to blogs and podcasts, it’s necessary to immerse yourself in everything data-related. Polishing your knowledge might sound like a time commitment, but it doesn’t have to be. Bookmark some blogs to read on your commute, find some podcasts to listen to at the gym, or read a book while you’re waiting to pick up the kids from school. For total beginners, we can recommend the following data analytics books to get started:
- Hello World: How to be Human in the Age of Algorithms by Hannah Fry
- The Drunkard’s Walk: How Randomness Rules Our Lives by Leonard Mlodinow
- How Smart Machines Think by Sean Gerrish and Kevin Scott
- Knowledge Is Beautiful: A Visual Miscellaneum of Compelling Information by David McCandless
Attend networking events to meet like-minded individuals
One of the best ways to immerse yourself in data is to seek out like-minded enthusiasts and those already working in the field. There are many data analytics networking events available on meetup.com. You’ll find everything from casual coffee mornings to professional events, online seminars, and formal conferences. Plus, one benefit of Covid-19 driving events online is that geography is no longer a barrier to attendance. You can benefit from expert wisdom, no matter where you are.
A polished portfolio is king (and a resume’s important, too!)
In data analytics, a portfolio does a lot of the ‘heavy lifting’ in showing off your skills. It’s a necessity for all data analysts as it demonstrates your competencies in practice. A traditional resume is still important, of course, since this allows potential employers to see your work experience and skill sets at a glance. So make sure it’s up to date, too.
If you’re new to data analytics, creating a portfolio of work might seem like jumping the gun, but you can include pet projects—they don’t have to be client-facing. To get started, we recommend looking at some examples of the best data analytics portfolios on the web and checking out our guide to building a data analytics portfolio from scratch.
Present yourself as you wish to be seen
Fear of ageism and insecurity about stepping into a new workplace can easily lead to us presenting ourselves in the wrong light. Rather than seeing your experience as making you “older” or out of touch, pitch it as a value-add for any employer. When you’re creating your portfolio or attending an interview, be clear about how your unique life experience gives you an advantage. Don’t put yourself down when it comes to your technical skill set, either, even in a self-deprecating way, as this risks planting a seed of doubt in an employer’s mind.
The last thing to remember is that the world is changing. However you might feel about it, the idea of a ‘career for life’ is fast becoming an outdated concept. There’s no need to feel insecure about changing careers later in life. It’s simply the reality of the world we’re now living in. Employers understand that, and by attempting to make a career change at all, you’re showing that you do, too. That takes guts, and it’s more powerful than you might ever realize.
For certain data analytics roles or disciplines (like data science), having more experience presents a clear advantage over younger, less-experienced candidates. But even for entry-level roles, being older doesn’t have to be a barrier to success. Employers increasingly understand the benefits of hiring seasoned professionals and value the unique perspectives they can bring. Capitalize on this, and you can play it to your advantage. Life’s too short not to chase your dreams, so go for it!
Check out this video we made with Dr. Humera Noor Minhas. Dr. Minhas leads us through the initial steps you should take to make a career change into data analytics:
To learn more about a potential career in data analytics, sign up for this free, 5-day data analytics short course. You can also check out the following introductory guides: