5 things to remember while building a data analytics startup

In 2022, the power of data is undeniable. A look at the world’s most successful organisations or the global effort to fight COVID-19 reveals the importance of data – and the fact that it’s at the centre of innovation and growth.

That said, harnessing its power is not easy. Here are a few things to remember while building a data analytics startup.

1. Invest in the right analytics team

Data literacy is the biggest challenge when it comes to building a data analytics startup. It should not be a surprise that a startup for data analytics is more likely to succeed with a team of expert data analysts. However, just expertise does not cut it.

Yes, a data-literate team will have a firm grasp of data science and the most advanced data analytics tools. This may even include being proficient in programming. However, what makes an analytics team right is not just expertise, but how it collaborates and uses that collective expertise to solve problems.

Some might even venture to say that talent is secondary. The sooner you find the right fit (culturally, creatively), the better.

2. Collect the correct data

Data is the foundation of a data analytics startup. However, the startup is bound to generate flawed insights if its foundation itself is flawed.

Data analytics companies are often pigeonholed for just collecting and analysing data. However, there is so much more to data analytics. The real work, in fact, goes in planning and building the infrastructure to identify, collect, move, process, analyse, and communicate data reliably. This demands thorough research and critical thinking, as you question the integrity of every data practice that constitutes your startup. 

Again, the sooner a strict habit for documentation and planning is cultivated, the easier it is to find and eliminate errors.

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3. Measure your results 

Measuring completes the feedback loop, allowing a startup to shrink the gap between where it is and where it expects to be. In other words, measurement enables a data analytics startup to determine the effectiveness of its practice.

That said, always have a clear sense of direction on how to measure success. Multiple variables provide multiple perspectives, but they are also harder to juggle. Here is another reason to invest in planning, as measurements, especially so many, will be a waste of time if they are inaccurate.

What should be measured, how, and why? These are crucial questions to consider and define the course of your growth. Also, do not measure in isolation. Always back measurements with actionable insights so you can act upon what you have learned.

4. Find the right investors

The right investor brings more than money to the table. Finding one is not very different from finding the right team. Knowledge and vision are high priorities, but trust and compatibility are also critical.

The key is balance. Finding the right people—investors or otherwise—is important. Find people who do not echo your beliefs, but instead create a culture that encourages challenge and meaningful, growth-oriented discourse. If there are no challenges, you risk creating an echo chamber. Meanwhile, purposeless challenges take up valuable time and energy.

The right balance sees that you use the time and energy to innovate and grow. 

5. Growth hacking

The goal of growth hacking is simple: maximise acquisition with minimum expenditure. The practice involves a variety of strategies, but for data analytics startups, the key is to understand the why of problems: why do users sign up for a website or make a certain purchase, for instance.

Once the why is understood, the feature can be improved to amplify attraction and conversion. The best thing about growth hacking is that it creates more data, which leads to more insights, which leads to better solutions, which creates more data, and so forth. It’s a flywheel for growth.

And the pursuit is not just financially rewarding. Data analytics startups can use the power of data to aid public organisations as well. The same strategies can be used to work upon what content works and why, allowing organisations to create more effective awareness campaigns.

In the end, that is what data analytics is all about: finding hidden truths in complex data and utilising them to your advantage.