Big Data Analytics Certifications · Data Analytics Certifications

How to Start a Data Analytics Startup?

Data analytics for startups it seems so easy. Indeed, it is not tough to collect and analyze data. That’s something utmost startups are now doing. Obtaining valuable, actionable insight from your data is a little more complicated, though.

Here are a few things to learn that will assist make the right decisions as you build and manage your startup:

1. Invest in the Right Analytics Team

Data analytics is complex. What was once the domain of Excel spreadsheet supporters has now become a specialized skill for data analysts and scientists? While Excel still has its place, you will be ready to make a lot more out of your data if you can use a team with more difficult skills.

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

You need to know what is sensible for your startup. A data scientist with a Ph.D. and ten years of experience in deep learning for independent driving might look great on paper, but chances are you don’t want that level of expertise. Keep high, but reasonable, expectations when hiring data analysts, and look for skilled individuals with room to expand as your startup grows.

2. Collect the Right Data

Data is the foundation of your data analytics. Even the best analysts in the world would not be able to do much for you if they don’t have valuable data to work with. Make confident you have the data you want for reliable and meaningful analysis. If you are not sure what you necessitate, ask your analysts if you have hired well, they will have a good idea of what is needed and what is not.

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

As an e-commerce company, would you want a heatmap to see how click patterns work? Not. However, a startup that’s developing a mobile game would love this type of insight and could use it to change the product by making special interface choices informed by player actions.

What your startup will need depends on the specifics of your business. Deciding what data to get is something you should research and implement as early as possible, because the more useful data you have received, the more efficient your analysts can be in their analysis.

3. Make Key Technology Decisions Early

Along those same lines, it is essential to keep your tech mass early. A company that is built on a bad foundation is not likely to increase, and continuously switching out the solutions in your furnace will wreak havoc with your data analytics. The choices you make now, including major technological ones like databases, will affect the types of analyses you can perform.

A poor selection of infrastructure can be crippling. NoSQL databases like MongoDB have become popular in recent years as they allow for fast scaling and building of the product. However, this happens at the cost of being able to perform joins across data types—traditional SQL databases like MySQL and PostgreSQL are much more skilled at this.

While it may not seem like you want those features early on and you may not, have in mind that any changes to your system once it is up and running are likely to be disruptive and expensive. It is to start 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 progress look like for your data analytics team? Has an ROI been established with metrics for measurement? For example, an analytics team which measures how relevant a post is to a user, and its success is tied to this measurement. Being able to hold your data team’s effectiveness is critical.

That said, don’t go overboard. A common misconception has too many metrics. Your data analytics team must then balance various measurements, and it will be exciting to narrow focus. Like any other team, your data team requires 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 mock at the idea of a team solely devoted to data analytics, thinking it is only needed at a larger scale. Other investors will need an experienced analyst as a new hire before you have collected any data.

Ideally, you want to find investors who know that a startup should have a data analytics team when data is ready to assess. The older data gets, the less useful penetration it can give, so once you are at the point of generating and collecting data, it does sense to bring in an analyst or analytics team to assist you to monetize it.

6. Growth Hacking for Startups:

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

For example, first on in its development, file hosting provider Dropbox analyzed its data and decided that users who shared a file on their platform were more suitable to become repeat users. They never figured out exactly why, because all that meant was the result. Dropbox replaced its website to make sharing more convenient and added information to prompt users to share.

Their user count skyrocketed as a result of the money to the platform. But it was analyzing the data and creating the connection between shares and user signups that made Dropbox’s growth spurt possible. The chances are that you will want a data team to be searching for detailed insights at your startup. Identify the actions that lead to user growth and watch your startup fly!

Conclusion

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


Data science enables your startup to make contact on the world, and Dataquest makes it simple for your team members to learn valuable data skills efficiently and affordable.

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