Are you a new or aspiring freelance data scientist? To quickly build a profitable freelance data business, you’ve got to make sure you’re being strategic about how you invest your time and money. 

All too often, I see new data freelancers caught holding on to certain misconceptions about entrepreneurship, causing them to stay stagnant rather than propel forward. There are 5 myths, specifically, that I see preventing freelance data scientists from growing their business into the six-figure range. Today I’m going to debunk each of these myths so you can build a freelance data business that gives you the income and impact you desire.


Read all about the top 5 myths that prevent success for the freelance data scientist!

Data-Mania is now a full-scale training and advising company that supports data professionals in becoming better data leaders
(we’ve trained 1 million data professionals and counting!), but my business started off as a one-woman show. I was able to bust through these myths and grow my business from a freelance data scientist into a full-fledged multiple six-figure business, which I now run from an island in Thailand ????

???? Before we continue, an important note: while I will be using the term ‘freelance data scientist’, these myths apply to ALL data services providers. Perhaps you don’t call yourself a data scientist, but instead provide services as a data analyst, data visualization expert, or machine learning engineer. These myths still apply to you too, whatever your niche or specialty, and busting through them will help get your freelance data business to the next level.


Myth 1: If you try, you will fail.

I see SO many aspiring entrepreneurs give up before they even start because they think their business is doomed for failure. The truth is, if you try you might fail. Small Business Association states that 30% of new businesses fail during their first two years, 50% fail during their first five years, and 66% fail in their first 10 years. So yes, if we have an honest look at the numbers, having your business fail is certainly a possibility.

Imagine earning multiple six-figures while working 30 hours per week (or less) from paradise.

There ARE, however, certain things you can do to improve your odds. As a way of preventing a business failure, I recommend that you do the following:

1. Do not quit your day job until you have your second income source built up enough that it can support you. Begin building your business as a freelance data scientist at night, while staying on with your employer. Slowly build your clientele, establish your brand, and build your reputation.  This will build your business and your new revenue streams until you feel comfortable leaving that day job behind. This is what I did, and I’ve never regretted it.

2. Hire a darn business coach. I’m not just saying this because I offer business coaching services (although I’d be delighted to help you!) – I’m saying this because of the crazy awesome impact that I’ve seen from working with business coaches myself.

If you want to safeguard your business from failure, why not learn insider secrets from someone who’s had success with what you’re trying to achieve?  Before I hired coaches, I never imagined I’d be able to earn multiple six-figures while working 30 hours per week (or less) from paradise. Just trust me on this – getting support from a coach is totally worth it!

To start learning those insider secrets NOW, start by downloading Ultimate Toolkit for Data EntrepreneursYou’ll get direct, actionable recommendations on the exact tools and processes you need to set your data business up for long-term success!

32 Free Tools for Data Entrepreneurs and the Freelance Data Scientist

Myth 2:
 You need to have all your offers 100% figured out before you start out as a freelance data scientist. 

Ask anyone who’s tried to start a freelance business: we had absolutely no idea what we were doing at the beginning! That’s normal. 

Don’t let doubt and inexperience get in the way of going after what you want. 

In order to get through the slump of indecision, I suggest you do the following:

  1. List out all of your data & business skills and see what services you could offer.
  2. Conduct market research to determine which combinations of those skills  are most in-demand and would be best for you to sell.
  3. Test the viability of your business ideas.
  4. Package and price your service offers.
  5. Test and discover how best to market your services.

Myth 3:
 You don’t have time to build and manage a six-figure data business.

These days there are tons of different types of tasks that you can automate to save time running your business as a freelance data scientist.  You can automate blog posting, email workflows, and even analytics reporting. All of these types of automation decrease your workload and increase your productivity (meaning you make more money while working fewer hours).

According to an article on, 63% of companies that are outgrowing their competitors are using marketing automation. (The Lenskold and Pedowitz Groups, Lead Generation Marketing Effectiveness Study).  That statistic alone should be enough to get you looking into automation as a regular part of your business. And in cases where you can’t automate, consider delegating instead.

Myth 4:
  You need a huge online following to be successful. 

The larger your online following, the harder it is for you to cultivate meaningful relationships from within your audience. Ask anyone with an online following, as audience sizes increases, engagement with audience members decreases. And, to build a successful freelance data business, you need to cultivate authentic relationships. Trust me, this is more about quality than quantity. People will buy your data services if they feel they know you, like you, and trust you.

The way to sign new clients, and retain existing clients, is to consistently add value to the lives of the people you serve. You don’t need 100k followers. To sign new data services clients, use social as a vehicle to cultivate authentic relationships with the right people. To retain existing clients, engineer an exemplary customer service experience, and always over-deliver.

Myth 5:
 You need a private equity investor to start your online business.

What?!?! You want to sell out before you even started?!? I knnnoooowww that can’t be true! 

Look, it’s 100% unnecessary to pitch investors. You don’t need a million bucks in the bank to start your business as a freelance data scientist. What you do need, however, is some SERIOUS DEDICATION. If you know for sure you want to build a badass data business, now is the time to make moves, not excuses.

Start small, with a few products or services. After you’ve figured out what works best for your business, begin to look for ways to lower overhead and streamline your processes.  Once that’s accomplished, begin to build out your services and grow your team, slowly. In this way, you and your business grow simultaneously, making the transition much easier and more successful.

To get started building your freelance data business, I have just the thing for you. The Ultimate Toolkit for Data Entrepreneurs features my best recommendations for free and low-cost tools and tech to set up your data business with a solid foundation for success. These are the exact processes I use in my business every day and that have helped me scale to the multi-6-figure range!


32 Free Tools for Data Entrepreneurs and the Freelance Data Scientist

Lillian Pierson, P.E.

Lillian Pierson is a CEO & data leader that supports data professionals to evolve into world-class leaders & entrepreneurs. To date, she’s helped educate over 1.3 million data professionals on AI and data science. Lillian has authored 6 data books with Wiley & Sons Publishers as well as 8 data courses with LinkedIn Learning. She’s supported a wide variety of organizations across the globe, from the United Nations and National Geographic, to Ericsson and Saudi Aramco, and everything in between. She is a licensed Professional Engineer, in good standing. She’s been a technical consultant since 2007 and a data business mentor since 2018. She occasionally volunteers her expertise in global summits and forums on data privacy and ethics.

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