Data science is no longer the privilege of big technology companies. It has been a part of almost all offline and online industries.
Everything from large Fintech or IT enterprises to restaurants, fashion shops, and even animal care can be enhanced with artificial intelligence. Many startups are created in a way to improve a product with advanced features later or choose to be AI-based right away because they understand how it can pay off in the future.
Startups are great but risky – one never knows whether their idea will work out or fail. Counting on innovation is the only way to lead your startup to success, and data science consulting would be a wise step towards it. This article will tell you how data science makes startups successful. It will also inform you on how to implement advanced technologies in a startup and provide examples of profitable data-driven solutions out there.
How AI Makes Startups Successful
If you open the list of top 100 startups from around the world, you’ll find out that a great part of them use data science to perform ordinary tasks, only faster, better, and for less money. However, such task optimization is one of the essential benefits of data science. Neural networks can do more than that. They already write novels, create paintings, and play musical instruments. Now imagine what else they can do for the sake of your business. Here are a few more factors explaining how data science will improve your business.
For some starters, personalization is about dividing customers into smaller groups, for example, by gender or age, and then offering products based on this division. With big data and predictive models, a business can give customers exactly what they want and even more. By collecting and analyzing data from past behaviors of users/customers, you’ll understand their future behaviors and be able to optimize your business according to this information.
Data mining is about getting non-trivial data from a great amount of information you have already. Based on non-trivial data, you can search for ways to create something new in your field and outrun competitors. For instance, facial recognition used to be associated with high-level security. Now, it’s also used for fun, and video face filters are at their peak of popularity.
Although the implementation of advanced features requires a strong focus on fundraising, the results are worth the effort. Based on the predictions about your customers, your idea can become profitable way faster. Armed with the knowledge, you will know how to use time and financial resources further. Also, the best startup products become acquired by giant tech companies for billions of dollars.
How Startups Implement Data Science
Usually, tech startup owners are experienced programmers and data scientists. Having a deep knowledge of the subject, faith, and ambitions, they can build exceptional solutions by themselves. What can you do without those skills? The answer is to seek help from a data science company to develop a product under your control and leadership. Startups usually prefer to outsource the development to other countries, where they can get high-quality solutions for a lower cost. It’s a usual practice for such startup companies as Slack, WhatsApp, or Basecamp.
What Startups Use Data Science
After getting enough financial support from investors, it is crucial to set all fears away and strive for better. In this part of the article, we collected five well-known startups that managed to win investors and become successful. However, the list of such startups is more extended, the goal here is to highlight how data science makes ideas real and profitable.
Shoppermotion is a US-based startup that offers offline retail stores to use the power of big data, machine learning, and geo-location to increase sales. All these technologies help track customer behavior inside the store, watch and analyze customers’ interaction with goods, and make decisions about future behaviors based on the past ones.
This British startup worked on the implementation of AI technologies that would make a diagnosis via the app based on the patient’s indicated symptoms. In this app, networks can compare different symptoms and other patient’s data like U/S or MRI with the ones already present in its base, and then identify a disease.
Beauty: Hi Mirror
Hi Mirror app is a smart mirror that provides tips on skincare based on its look and the information about the user’s make-up products. The “smart” part of this app analyzes the state of different skin areas and makes a conclusion about what kind of skincare a person needs at the moment. The app is affordable for every family with an average income in a developed country.
MSQRD (or Masquerade) is a Belarusian startup based on face recognition technology that allows people to use face filters and take photographs. In 2016, Facebook acquired this video filters app. It serves as a part of the bigger service rather than a single application.
Darktrace is a startup product that predicts possible cyberattacks on the Internet. Darktrace AI, also known as Antigena, reacts in the right time to eliminate an imminent threat. It also offers the so-called Enterprise Immune System – an unsupervised machine learning technology for trapping threats in complex enterprise systems and destroying them.
Soon, the technology world will witness significant improvements in NLP for making human interaction with products even more personalized. Self-driving and flying cars will become our usual transport, and scientists will invent more new features to improve businesses. Both online and offline businesses will become smarter and new ways of data security are about to be developed.
In a decade or so, these technologies will become usual, but now data scientist skills are in high demand worldwide. While AI and neural networks still surprise people, it’s a good time to start getting your benefit out of data science. So, if you have an idea and can share it with the world, make it successful with data science.