The Startup CTO’s Guide To Structuring Your Data Infrastructure

The Startup CTO’s Guide To Structuring Your Data Infrastructure

Analytics and data-driven approaches are used mainly to make business decisions and plan business operations. It isn’t easy to use data effectively to gain business insights. This requires both expertise and a lot infrastructure. For better business performance, engineering must build and maintain database tools. A CTO for a new business faces the challenge of establishing a data-driven culture.

A start-up experiencing steady growth might experience many organizational changes. It is important for the CTO that all these changes are incorporated to keep in line with the data-driven culture. There are many strategies that must be used at every stage of the evaluation of a start up business, whether it is product development, employee management, security administration or employee management. These are some of the things technology officers should know in order to manage these changes effectively.

Immediate startup with one to three employees

This could be the beginning stages of a business, where you might not even have started to make steady income. This phase might have a homepage and limited traffic. There may not be much data. The CTO might not need to worry about setting up a large data infrastructure at this stage.

But, now is the time to plan ahead and make sure you have a way to keep your data safe while anticipating future needs. Be sure to follow best database design practices. Limit access to deletes and updates that could lead to the loss of valuable information. When new features are added, consider how to analyze them and create the code accordingly.

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This phase is important to make sure you have a Google Analytics account. Also, establish a hierarchy and accountability in order to create a data-driven culture within your business. Your business users might ask you to pull data from the DB even at this stage. However, as per, these may be minimal, and it is okay at this phase to handle them ad hoc.

Establishments with four to ten employees

At this stage you can start to see real data coming in. It is important to identify the right product-market match. The company’s leadership should maintain a table with the key KPIs. Other stakeholders might be interested in understanding the true dynamics of the products or operations. This will increase data requests.

Technical team must ensure that users have access to the most relevant tools to help them find and report on the data they need. Understanding the business users is an important part of this challenge. They can be from any department within or outside the company. To meet infrastructural needs, you can set up a slave database and give read-only accounts so that analytic queries can be met without affecting production. Next, find out how your business users would like to interact with the data.

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There are many tools available, both free and paid, that can be used immediately without you having to create your own systems. Some people prefer the SQL results in Excel, while others prefer visual tools that can abstract the raw SQL. You can better understand the business users and plan tools that make sense.

While you must strive to get the best, but keep an eye on the future you might choose tools that don’t require much investment. With only three to four engineers, you may have minimal time to complete the database features. It may not be ideal to dedicate more than 25% of your productive engineering time for analytics unless your specific business industry requires it at baseline.

Eleven to thirty employees

You will begin to notice an increase in data volumes at this stage. At least one business user should dedicate their time to data analysis. If you put the necessary effort into putting analytical tools in the hands of business users, your data could have spread to other silos. This means that you might need to acquire more tools, more data and faster processing.

Now is the time to start thinking about a long-term strategy for data. You should hire a skilled engineer resource to help you build the infrastructure. It will be more difficult to scale up to handle the increasing amount of data and queries at this stage.

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With the current needs in mind, there will be enough strategic indices in the slave database to satisfy the need. However, soon you might need a larger data warehouse to support the growing demand. You may also face some extract-transform-load (ETL) which will aggregate data from various sources, transform and clean it, and load to the querying engines.

At this stage, you have the option to either hire a consultant to provide a customized solution or outsource your data to a data warehouse service. There is no off-the-shelf solution that can free your team from responsibility.


As data volumes increase and the use-cases continue to grow, database management can become more complex and crucial. It is possible that you will need to have a large team of data infrastructure engineers as well, just like Amazon and Twitter. As your company grows, you should think about the role of “Analytics director” who can help plan and execute a visionary database infrastructure. You will have trouble managing your data if you don’t have a long-term strategy.