3 Big Data Challenges Faced By All Startups (And How to Solve Them)
All startups need to make the right decisions: develop the right features, target the right customers, and hire the right people.
Making good decisions requires accurate data. The difficulty is that startups rarely have enough data, let alone good data.
In this post, we’re going to explore three main data challenges faced by startups:
- Lack of data
- Over-reliance on quantitative data
- Limited resources for data management and infrastructure.
Why do we need data?
Many people imagine entrepreneurs as visionary figures like Steve Jobs, people who build products that most of us don’t even realize we need until they exist.
For most startups, this isn’t the case. Even the visionary founders often base their products on proven use cases.
Steve Jobs created the iPhone by combining several proven use cases: calling, texting, and listening to music. The interface and design developed to achieve those goals were revolutionary, but the need was proven.
Elon Musk pushed electric cars to the forefront by developing a fantastic driving experience, but the need to get from one place to another wasn’t new.
Whether you’re aiming to develop the next iPhone, electric car, or project management app, data helps you prove that a problem exists and needs solving, before you begin product development.
Once you have a product, making good decisions is key to a startup’s survival. Limited resources mean you can only develop so many features. If unsuccessful, you won’t last long.
Time and money are limited and cannot be wasted. You must quickly identify your core users, find more of them, and monetize.
The better we are at gathering, processing, and interpreting data, the better our decisions, and the less time and money we waste.
Data is about increasing the odds of your startup succeeding.
Challenge #1: Lack of data
You’ve examined the market, surveyed potential users, and have validated that a problem actually exists for your startup to solve. Now you want to pursue that opportunity and have formed an early stage startup.
You’re probably a small team: maybe just cofounders, maybe a few employees if you’ve raised some seed money. You might have users — maybe even some paying customers — and are seeing some early traction.
Sound familiar? If so, you’re likely asking yourself:
- What features should we develop next?
- Which of our early users or customers are our best use cases?
- Who should we target, and how?
Or perhaps you’re thinking:
- Why aren’t people adopting my product as I expected?
- Why isn’t this marketing channel working?
- Why do people try the product but don’t convert?
- This usage pattern is strange; why are they doing that?
The difficult part about answering these questions is that, for an early-stage startup, there are very few data points. You may only have 10 users. You may have even fewer if you’re an enterprise startup. The usage patterns may not be the same for even two of your customers.
The solution to this problem? Richer data.
At this stage, richer data will only come from one place: user interviews.
You should be talking to as many customers as you can, as often as you can, in the early days of your startup.
You’ve no doubt heard the stories of the Airbnb founders flying from San Francisco to New York just to speak to some of their earliest hosts.
Or the founders of Stripe convincing their fellow Y Combinator participants to use their product.
The aim here was not growing their customer base. It was understanding their customer base.
To truly develop great products, you must have a deep understanding of your customers and their use case. Even if you’ve experienced the problem yourself, you can’t assume your experience matches that of your potential users.
There could be counterintuitive or overlooked features that are critical to your success, or you may be acquiring users who are different than you first thought.
For Airbnb, the takeaway was hiring professional photographers to take photos of hosts’ homes. This was a friction point for both signing up and potential bookings.
For Stripe, it was pushing users to the “aha!” moment of realizing it took literally one line of code to accept payments. But figuring that out required being very close to early customers, and having frequent conversations with early adopters.
To be able to talk to your customers effectively, you must:
- Have their contact information: at the very least, an email, but ideally a phone number.
- Go to where they are — if you’re in the enterprise space, you must find a way to get face-to-face with customers. Go to their industry events, their meetups, or invite them for a drink.
- If you’re in the consumer space, avoid the temptation to just look at behavior. Try and talk to them on the phone, meet them, or watch them use your product, and combine that information with behavior patterns.
- Spend time learning how to ask the right questions, and to get the best out of customer interviews. This skillset is often overlooked, but can save you a lot of time. The skills are transferable to other domains (working in teams, talking to investors, etc.). Keith Hopper has some great resources on this.
Seek data where you can find it. For an early-stage startup, your best source is your early users and customers.
Challenge #2: Over-reliance on quantitative data
You’ve spoken to your customers and understand their use cases. Maybe you’ve unlocked a new growth channel or have seen a jump in adoption.
And now your startup is at a point where you have more customers than you can fit into your schedule. That’s fine because you’re getting enough volume to be able to see patterns in the quantitative data. These patterns might involve most-used features, frequency of use, or customer profiles.
This is a great stage for your startup to have reached. But — and there’s always a but — with this much quantitative data, founders, marketers, and product managers tend to forget the value of qualitative data. Or they move to more scalable methods of obtaining it, like surveys.
This information is still useful. At this point, you could also ask your customer success or sales teams for insights, since they’re speaking with clients every day.
But there is no true substitute for one-on-one conversations and customer interviews.
While customer success employees often deal with unhappy customers, and salespeople speak with prospects, who is talking to happy users? Who is having conversations with the people using your product in new and exciting ways.
User interviews help you understand the why behind a pattern that you’ve noticed. Far too often, especially in a startup, we’ll notice a pattern, do some preliminary analysis, and come to a conclusion on our own. We think that, because we know the product well, we have enough insights to make decisions.
Sometimes that might be the case, but often you’re just making unfounded assumptions, leading you to overlook something important. That’s why you should always supplement quantitative data with qualitative data from user interviews. You can never talk to your customers enough.
Challenge #3: Limited resources for data management and infrastructure
A startup never has enough resources. There are always features to develop, debt to pay back, and products to build.
As a result, data management and infrastructure often sits far down the priority list in the early days of a startup. But an early investment here can pay major dividends down the road.
Usually developers are given the responsibility of setting up tracking and analytics. Unless you have a developer who is unusually interested in data and tracking, this tends to be ad-hoc, likely inconsistent (particularly if multiple devs work on it), and may not align with what current or future marketing and product teams want.
Over time, you might gather events and setups via various systems – there are at least three different ways to implement Google Analytics.
At some point, companies realize they need to do some cleaning and restructuring, often when they reach roughly 50 people.
By the time a startup recognizes its data needs, the volume can be overwhelming for a fledgling data team.
The best solution here is to do two things consistently:
- Document what you implement (and the rationale behind your decisions)
- Consider your future use cases when making decisions on tools
Documentation is one of the first things that gets left behind in a fast-growth company, but it’s arguably even more important during high growth.
It doesn’t have to be complicated or overly-detailed. Simply noting, “Here’s what I did and why,” with the relevant details is enough. Make it easily searchable, and put it somewhere it can be easily consulted (like the company Google Drive).
Considering future use cases is difficult, but not impossible.
If you’re developing a SaaS app, you will want to track user events. It may be worthwhile to implement a tool like Segment early, instead of hard-coding things.
Developing a naming convention — even a simple one, — will help make things easier to understand in the future.
Training your engineers on how to make these decisions helps spread the load, and reduces work in the future. If everyone is implementing events using a standardized, centralized tool, with an established naming convention, and documentation to match, you will have far fewer problems with infrastructure in the future.
Data drives startups
Whether we like it or not.
- In the beginning, you won’t have enough data; compensate by talking to early customers often, particularly those who are heavy users of your product.
- As you gather more quantitative data, don’t forget to supplement it with qualitative data and carefully consider what data you need to draw conclusions.
- Choose tools and implement them in ways that will scale effectively. Document your decisions, and communicate processes to the entire company. It will save you time in the long run.
The right data will help you make the right decisions. Following these three principles from the very start will set your startup on a course for long-term success.
How can startups solve data problems?
With data integration, you can create a single source of truth for your data teams and everyone who depends on them.