Article


When should startups invest in data analysis?

When should startups invest in data analysis?



ByJan Watermann

...

April 3, 2019

Startups often do not have the financial resources to make technology investments that do not yield a direct return. This includes investments in data analysis, which generally only pay off in the future. However, most entrepreneurs are aware that data analysis is still an essential part of a growing business .

Timing is key here. After all, startups want to use their financial resources as efficiently as possible. This article, therefore, answers the question of when data analysis is a worthwhile investment for startups.

Data analysis as a tool in modern companies

With our blog post "Data analysis as a small sized company" we provided interested entrepreneurs a crash course on the usefulness of data analysis. The article explained that data analysis often is an important part of intelligent business decisions.

However, companies also have different requirements and initial situations. Thus, data analysis is not the universal path to success in business development. Much more it helps committed companies to gain an overview of their business practices and provides insights into the behavior of their customers.


Tableau data analysis based on Salesforce

When should startups invest in data analysis?

While most Fortune 500 companies use big data analysis, the situation is usually different for start-ups. They are often struggling with accessibility issues, even with simple data analysis. The costs often make it simply impossible to get started with Tableau or Microsoft PowerBI. So for companies, in-depth data analyses are only possible if the knowledge gained from them brings significantly more value than it costs.

Startups should meet three requirements to use data analysis efficiently:

1) The Foundation - The Data

Nothing works without data. For startups, data analysis is not about big data or artificial intelligence, but rather about the tools that power their business.

2) Improvement in the business model

A bakery will find it difficult to optimize its own business with the help of data analysis alone. The most important factors for data analysis are an existing customer base and a scalable business model. If these two requirements are not met, a data project is out of the question.

3) Implementability of the knowledge gained

Can insights from data analysis influence the existing business? This aspect is particularly relevant to ensure that analyses are worthwhile for start-ups.

This does not mean that early stage start-ups should generally avoid investments in data and analysis. Rather, it is a matter of weighing up whether the insights gained can make a difference. For startups with a small customer base, these can nevertheless provide valuable insights into important business processes. The prerequisite is that sufficient data on the customer base is available and that it can find its deserved influence.

Data analysis in different startup stages

Startups are basically classified into three different stages. Early Stage, Series A, and Growth Stage, where Early Stages usually has the least capital available for investments. Series A start-ups have just received their first major financing and are looking for lucrative investment opportunities. Growth stage start-ups have more capital through several financing rounds and are generally more willing to invest than Series A start-ups. (More Learn more here).

Early-Stage-Startups

At the beginning of their journey, startups often do not have the necessary resources to initiate data projects. Preparing for future data analysis is, therefore, the key to success. If early-stage startups collect data about their customer behavior from the outset, analyzing it in later stages is less complicated. However, there are exceptions. With data-intensive business models, for example, data analyses are generally valuable regardless of a startup’s size.

Therefore, as already mentioned above, for early-stage startups it’s crucial to weigh up the ratio of costs and benefits of data analysis projects. Preparations in the sense of a maintained data structure and the continuous recording of business data are recommended to all companies.

Series A Startups

Startups with a Series A investment typically have sufficient capital to implement data analysis models. Whether this already makes sense depends strongly on the individual case - i.e. the customer base size, the expansion of the business model etc.. However, most Series A startups benefit from the use of Business Intelligence tools, such as Tableau, if enough valuable data is available.

In practice, this means that founders and officials of such start-ups should focus on the analysis of business-related data. This saves capital and ensures that a company gains business insights. You can find an example of an in-depth analysis of various SEO tools here: SEO-Analysis with Tableau.

Growth Stage Startups

Startups in their growth stage should have sufficient capital to establish data analysis as a core element of their business culture. The primary objective is to gain an overview of the business process, the effectiveness of individual business processes and the tools used. A good example is the German Startup N26, which has excellently integrated data analysis processes. An insight into the N26 data team explains that the data department works closely with the company’s customer support, which often is a weakness of a fast-growing startup.

N26 understands that intelligent data analysis can save resources. Therefore, the data department clearly focuses on the company's weakness, which is relieved by "intercepting" unnecessary support requests.

The first step towards Business Intelligence

Getting started with data and analysis is not easy. Datapony helps companies to get an overview of their business data. Whether Early-Stage, Series A, Growth Stage or existing companies - with Datapony's efficient data services, your company will enjoy an uncomplicated introduction to data analysis.

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