April 18, 2019

Data Analytics for Startups: What You Need to Know


Data analytics for startups—it sounds so simple. And it’s true that it’s not difficult to collect and analyze data. That’s something most startups are already doing. Getting valuable, actionable insight from your data is a bit more complicated, though. Here are six things to know that will help make the right decisions as you grow and manage your startup.

1. Invest in the Right Analytics Team

Data analytics is complicated. What was once the domain of Excel spreadsheet enthusiasts has now become a specialized skill for data analysts and scientists. And while Excel still has its place, you’ll be able to get a lot more out of your data if you can hire a team with more advanced skills.

A good data analytics team will need spreadsheet skills, but analysts should also have data analytics abilities programming skills in Python or R, SQL skills, and a good grasp of statistics.

You need to know what’s realistic for your startup. A data scientist with a PhD and 10 years of experience in deep learning for autonomous driving might look great on paper, but chances are you don’t need that level of experience. Keep high, but reasonable, expectations when hiring data analysts, and look for talented individuals with room to grow as your startup grows.

2. Collect the Right Data

Data is the foundation of your data analytics. Even the best analysts in the world won’t be able to do much for you if they don’t have good data to work with. Make sure you have the data you need for accurate and meaningful analysis. If you’re not sure what you need, ask your analysts—if you’ve hired well, they’ll have a good idea of what’s needed and what is not.

For example, if your startup is in ecommerce, you’ll almost certainly have Google Analytics or some other analytics tool set up already. But you may also want an extension or package to handle A/B testing to see how different page layouts or copy affect user experience.

As an ecommece company, would you need a heatmap to see how click patterns work? Probably not. However, a startup that’s building a mobile game would love this type of insight, and could use it to improve the product by making specific interface choices informed by player actions.

What your startup will need depends on the specifics of your business. Deciding what data to collect is something you should research and implement as early as possible, because the more good data you’ve collected, the more effective your analysts can be in their analysis.

3. Make Key Technology Decisions Early

Along those same lines, it’s important to choose your tech stack early. A company that’s built on a bad foundation isn’t likely to thrive, and constantly switching out the solutions in your stack will wreak havoc with your data analytics. The choices you make now, including fundamental technological ones like databases and even firewall best practices, will impact the types of analyses you can perform.

A poor choice of infrastructure can be crippling. NoSQL databases like MongoDB have become popular in recent years as they allow for quick scaling and building of product. However, this comes at the cost of being able to perform joins across data types—traditional SQL databases like MySQL and PostgreSQL are much better at this. And while it may not seem like you need those features early on (and you may not), keep in mind that any changes to your system once its up and running are likely to be disruptive and expensive. It’s better to start out with a tech stack and a database solution you can grow into, that won’t limit the types of analyses your team can perform.

4. Measure Your Results

What does success look like for your data analytics team? Has an ROI been established with metrics for measurement? For instance, Facebook has an analytics team which measures how relevant a post is to a user, and its success is tied to this measurement. Being able to measure your data team’s effectiveness is key.

That said, don’t go overboard. A common mistake is having too many metrics. Your data analytics team must then balance multiple measurements, and it will be difficult to narrow focus. Like any other team, your data team needs a clear direction, so pick one or two KPIs and stick with them.

5. Find the Supportive Investors

The right investors can be the make-or-break factor in data analytics for your startup. Some investors may scoff at the idea of a team solely devoted to data analytics, thinking it’s only needed at a larger scale. Other investors will demand an experienced analyst as an early hire before you’ve accumulated any data.

Ideally, you want to find investors who understand that a startup should have a data analytics team when data is available to assess. The older data gets, the less useful insight it can provide, so once you’re at the point of generating and collecting data, it makes sense to bring in an analyst or analytics team to help you monetize it.

6. Growth Hacking for Startups

One of the biggest uses for data analytics at a startup is growth hacking. Often, it’s valuable to know what kinds of things correlate with users signing up for your site, or making a purchase so that you can double-down on strategies that prompt users to do those things.

For example, early on in its development, file hosting provider Dropbox analyzed its data and concluded that users who shared a file on their platform were more likely to become repeat users. They never figured out exactly why, because all that mattered was the result. Dropbox rearranged its website to make sharing more accessible, and added hints to prompt users to share.

Their user count skyrocketed as a result of the changes to the platform. But it was analyzing the data and discovering the connection between shares and user signups that made Dropbox’s growth spurt possible.

Chances are that you’ll want a data team to be searching for similar insights at your startup. Identify the actions that lead to the user growth and watch your startup fly!

Learn Data Analytics with Dataquest

Data analytics is about finding and exploring patterns in our world to solve problems. It can involve anything from analyzing the speed of global warming to building self-driving cars.

Data science enables your startup to make an impact on the world, and Dataquest makes it easy for your team members to learn valuable data skills efficiently and affordably. Start learning for free today and see how data analytics can help you and your team.

Celeste Grupman

About the author

Celeste Grupman

Celeste is the Director of Operations at Dataquest. She is passionate about creating affordable access to high-quality skills training for students across the globe.

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