Data analytics startups

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What are Data Analytics Startups?

Data analytics startups are a surprisingly easy way to fast-track information gathering that will help you make decisions that take the guesswork out of growing your business.

Data analytics sounds much more intimidating than it actually is now that platforms like CodeCloud Technology. Our engineers have done the hard work of creating the code and tools needed to capture data so you get a lot more insight than using Google Analytics alone and don’t have to start at ground zero. That’s essential if no one at the startup is super technical, or the super technical people are busy making sure the product/website/app is firing on all cylinders.

Why Do Data Analytics Matter for Startups?

If you think data analytics is something you can put on the back burner until the business is well established, getting to that point could be a lot more difficult. What you learn from data analytics may just be what gets you to the next stage. It’s powerful, precise information that gives you clear answers to important questions about your marketing, users, product, productivity, customer service, and the list goes on. 

Both the highly developed and startups search for easy-to-use technologies that they can use in their daily operations to capitalize on revenue collection. On the other side, the data collected by businesses is rapidly increased to 2.5 quintillion bytes per day, calling for the need to get a proper way of utilizing it. This has triggered the introduction of data visualization for startups which is meant to aid in data processing.

In any startup, data scientists are responsible for identifying important business metrics and creating a predictive operational model that can work for your brand. In addition, every startup needs to carry out experiments on what can work for them. This requires data to give way forward on what needs to be tested. Below are some of the needs why startups need data science.

Enhancing the Development of Data-Driven Products

There has been a massive shift in the way we do product management in the past decade. It started with writing user stories that were backed by extensive customer research, however, there was very little control over the outcomes. Product success involved a considerable dose of wishful thinking and/or hoping for positive business outcomes.

Data Extraction

Data extraction is the process of collecting or retrieving disparate types of data from a variety of sources, many of which may be poorly organized or completely unstructured. Data extraction makes it possible to consolidate, process, and refine data so that it can be stored in a centralized location in order to be transformed. These locations may be on-site, cloud-based, or a hybrid of the two.

Data extraction is the first step in both ETL (extract, transform, load) and ELT (extract, load, transform) processes. ETL/ELT are themselves part of a complete data integration strategy.

Besides, you won't be able to establish a solid foundation for your business which is a crucial thing for your business's success. Extracting data from your business operations gives you a clear picture of what needs to be done to create a better working environment and steer the success of your brand. You need to understand every single step you are making and its impact on the success of your startup.

The mining of data helps increase the number of sales recorded and create excellent marketing strategies. All these cannot be attained if you do not have data that gives you the way forward about what needs to be done. Data visualization has tools that aid in collecting and analyzing data that are crucial for your business.

Identifying Predictive Models

In short, predictive modeling is a statistical technique using machine learning and data mining to predict and forecast likely future outcomes with the aid of historical and existing data. It works by analyzing current and historical data and projecting what it learns on a model generated to forecast likely outcomes. Predictive modeling can be used to predict just about anything, from TV ratings and a customer’s next purchase to credit risks and corporate earnings.


According to the research done by the Wharton School of Business, using data science in startups saves 24% of your time. This means that you save much of your time with data science and elevate your startup's performance to great heights.

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