New and aspiring tech founders often make the mistake of relying solely on their gut instinct when it comes to developing and launching their business ideas. This is a HUGE problem among the startup community and most founders don’t even realize they’re doing it. In some cases, they’re aware of it, but they don’t realize how serious it is. This is especially true when it comes to data science startup ideas.

The good news is that, with this simple blog read, you’re going to be able to quickly identify whether your data science business ideas are at risk, and if they are, you’re going to get the exact steps you need to mitigate that risk.

Let’s start from the beginning, shall we?

The unfortunate reality is this: Just because your business ideas seem to be working out swell for you right now, that doesn’t mean that this “gut-instinct approach” to business strategy won’t become a serious problem for you tomorrow.

When we’re talking about the data science business ideas that most tech founders have for products and services they want to take to market… There’s really a lot of work that needs to go into choosing the right offer (more on that later). 

Alas, most new founders simply aren’t aware of what could go wrong.

Did you know that there are legions of failed data startup founders who killed their business simply by skipping the market research and validation steps that are involved in choosing from among promising data science offer ideas?

In all honesty, they usually don’t have a set of data science business ideas they’re choosing from. They usually have just one idea, that they maybe came up with when they were driving to the market (or something trivial like that). And once they have the idea? They’re off to the races. 🏁

Here’s how it typically goes.

… Founder gets “awesome” idea.

… They’re so excited about the idea, that they immediately get to work executing upon that idea.

… Then what happens?.?. They spend months, maybe years toiling away… They invest some of their own money… Heck, they may even get some investors and spend theirs too…

… And then, the “launch” they offer and… crickets

No customers…

No one needs it…

Not a single one wants it…

No one ever really needed or wanted it in the first place… 😭

But the “founder” only discovers this fact after they wasted massive amounts of valuable time and money building out the idea…

Seriously, ask any VC – this is the exact mistake that about 85% of new tech founders make, and almost always it ends up sinking the ship.

I call this go-to-market mistake “Magical Thinking”.

My point is, if you’ve got an idea for an “awesome” way on how to earn some extra revenue with your data expertise – make sure you do the market research that’s needed to explore alternatives and at least improve your idea, if not choose a better one from among a collection of vetted data science business ideas.

Take some time to explore the market, even if you don’t exactly know how to do market research – at least use the 5 steps I’m providing below to give it a first pass. But whatever you do, DO NOT FALL INTO THE TRAP OF MAGICAL THINKING.

Take some time to explore the market, even if you don’t exactly know how to do market research – at least use the 5 steps I’m providing below to give it a first pass. But whatever you do, DO NOT FALL INTO THE TRAP OF MAGICAL THINKING.

Don’t get me wrong, I don’t doubt your brilliance. What I am telling you to do here is really just due diligence. 

Market research is a standard part of any go-to-market strategy. If you haven’t executed this process before, that’s because you’re still a little wet behind the ears (and that’s ok!)

So that brings us to the next point: 

How risky are your business ideas, really?

Do YOU have Magical Thinking brewing between your ears?

To find out, answer the following questions:

• Do you have 3 or more unfinished side projects that you were super excited to start but never ended up following through on?
• Have you ever had an idea for a digital product or service and taken it to market without doing market research to validate that idea first?
• Are there 2 or more digital products that you spent time building and then never sold?
• If you have an idea for an awesome way to make extra income from your data expertise, are you planning on doing significant research to validate that idea before deciding it’s the best one for you?
• When you’re excited about an idea, do you always take a step back to evaluate your alternatives before taking action?

If you answered “yes” to at least 2 of the above questions, then you’re at risk of Magical Thinking.

The good news is that it IS possible to nip Magical Thinking in the bud without losing your passion.

You can lean on the following 5 steps as a good place to start.

5 simple steps to validating your data science startup ideas

It doesn’t matter how long you’ve been in business; you need to conduct market research to validate your data science business ideas before taking action on any one of them. 

Here’s what you need to do:

Step 1: Define your target market

The first step in validating your data science startup idea is to define your target market. Determine who you believe needs your product or service and how it will benefit them. Your target market should be specific, and you should be able to clearly define their demographics, pain points, and needs.

Step 2: Research the problem space

Once you have defined your target market, research the problem space. Develop a clear understanding of their most urgent problems, focusing specifically on the problems that you’re well-equipped to solve. This will help you to determine the viability of your idea and whether there’s a market for it.

Step 3: Research the solution space

Next, research the solution space. Look at how other businesses or freelancers are solving the problems that your target market faces. Take note of the offers they’re selling, what they’re including in those offers, and the vehicles they’re using to make additional money working with their customers via upsells, cross-sells, and downsells. This will help you to identify gaps in the market and opportunities for differentiation.

Step 4: Make a list of potential data science startup ideas

Based on your research, make a list of at least 20 service or product offers that you could deliver for your target market. These offers should address the pain points that you identified in the problem space and provide unique value to your customers.

Step 5: Evaluate and choose the best data science startup idea

Finally, evaluate your list of potential offers and choose the very best one. Consider factors such as the size of the market, the competition, the level of demand, and the feasibility of delivering the offer. Your chosen offer should be one that you’re passionate about, has the potential to be profitable, and addresses the needs of your target market.

The above are just the cliff notes, but they should be enough to get you started.

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