You can become a data analyst at any age. However, it takes hard work, and you should be ready. An older person trying to make a career change can pick data analytics, although it can be intimidating. On the other hand, you could have genuine concerns about what it takes to start over a new career or whether you have skill levels suitable for a career in data analytics. There is also the assumption that only young people can handle tech matters. This is not true.
In the tech sector, ageism is an issue. In many cases, people over 35 in tech are considered old. However, in the tech field, experience is valued. One of the skills sincerely sought in tech is domain expertise which comes with age. This means that you can venture into data analysis at 40 or any age, for that matter. There are many challenges to face along the way, but in the end, it may be worthwhile.
Are there benefits of becoming an older analyst?
Being older in tech, according to industry standards, has its benefits. Older in tech can be about those who are 30 years and above. Ageism is an issue in tech. Some of the benefits include:
Years of experience:
You should not assume that everyone wants to hire fresh graduates with degrees. Older applicants have a lot to offer since they have experience in business and life. They may have better knowledge compared to the younger generation. At 40, a person has built professional networks and industry insights, which cannot be compared to fresh graduates. Even though you may not have the same technical skills as the younger ones, most companies know that experience is valuable.
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Staying power
Older data analysts are considered more reliable than younger ones. The younger generation tends to move from one job to the next in search of better options. This adds to the cost of replacing them for companies. On the other hand, older analysts can stick to a role and may job-hop less than younger people. This improves the employability chance of older people.
Better leadership, communication, and writing skills
Data analysts require math, programming knowledge, and technical skills. One area that is often overlooked in data analytics is soft skills. You can learn the technical aspects of the job, which are essential. It is much better to communicate findings in a way people can understand.
Older analysts usually have better leadership, communication, and writing skills. This is something they fine-tune throughout their careers. You acquire such skills over time. They hold a lot of value.
Filling inclusion and diversity quotas
Sexuality, gender, and race are some of the inclusions in diversity watchwords common today. While you may feel terrible being the token person, inclusion and diversity quotas help address entrenched inequalities. Data analytics needs a much better representation, and a company that sees the value of older analysts can allow them to work.
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Shortage of people in the senior roles
Younger analysts must work hard before they can be considered senior data analysts. Having technical skills, work, and life experience would be best. This should be added to domain expertise and any other additional qualification. An older person who wants to shift to data analytics already has experience. Pursuing such a career may put you ahead of the younger people.
Are there challenges associated with older data analysts?
When starting a new job, regardless of age, you must manage an unfamiliar environment and try to make an impression. This can be tough. It gets even more complex for an older person trying their hand at a new industry. You must be aware of some common challenges if you venture into data analytics at 40 or older. However, the challenges can be tackled and managed.
Challenge 1: staying in step with the latest tech and data trends
Data analytics is considered a field that is fast evolving. Stay in touch with all changes and innovations happening around you. However, data analytics is complicated, and changes don’t come in too fast. The software and tools that the data analyst uses are the things to adapt. This can be intimidating for an older person since younger people tend to pick new skills quickly.
You can overcome the challenge by taking the time to read blogs on data analytics. Then, listen to podcasts about the same and talk to colleagues regarding any recent projects they have time for. You can learn to differentiate the important trends from the ones that are not.
Challenge number two: making a connection with the younger colleagues
Tech is dominated by younger people, which can be a challenge. However, an older person has the advantage of work and life experience. When you are in a new job, things can be intimidating. Younger people communicate differently, which can be an issue. Your boss as a data analyst may be younger than you. This may be awkward.
To handle this challenge, initiate conversations with your younger colleagues when you get the job. You can look for the same interests you may have, such as a hobby. The main thing is cultivating a good working relationship with your boss, peers, or colleagues. Even without the technical capacity of younger people, you have many other skills that they may not master until much later. In addition, interacting with them allows you to learn.
Challenge three: Getting a Job offering the deserved salary
This is a common challenge for analysts entering the job market much later. Younger employees don’t have too many financial concerns and commitments to meet. This means they are at liberty to take lower salaries. For an older analyst, you probably have many financial obligations and a family. This may make you reluctant to take a low-paying job. This could reduce your chances of building your career further.
You can handle this with research. Be clear on what to expect at the entry stage of data analysis. Then, you can look at areas like data science that pay much better. You should never undersell yourself. Even without much experience in data analytics, you may possess transferable skills that qualify you for a better salary.
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