The terms “startup” has been turning around for a few years in various sectors. From a multinational logistic company that carries passenger on demand or a transnational company that accommodates millions of people without even having any hotel room. All those examples at least one point of their existence had been called startup. However, after getting objectively bigger, calling those firms as startups became absurd. Nonetheless, there are a lot of company that earns millions of dollars and got subjectively bigger over the years, which we don’t hesitate to call startup. Hence, the description itself is blurred in so many ways.

However, the general opinion is that a relatively new company that uses technology extensively is called startup. In fact, being an old or new-founded mean less as name suggests. A company with 10 years of experience may be a startup, too. Therefore, having almost no exact definition makes things easy for a bit. We can put everything together and call a something as startup when the solution itself is as complex as the problem and the part of the solution comes from technology. Thus, implementing technological advancements to any problem (where there is a market for it) is the first step of founding a startup. The initial question suggested in the title becomes immediately obsolete with that in mind. That is, using state-of-art technologies (such as big data in last few years) is a must for a startup, or at least for a successful one.

For a successful startup there are a lot of elements to be ensured where a little bit of luck is needed for lasting long enough to become a non-startup. Analyzing the market conditions and the sectoral behavior is a must so your product must be perfect for the market. Moreover, the nature of the startups ensures that the segmentation in the company is organic; that is, everybody works on their business not in their business. Those and many more elements show the successful, fast-growing, quickly recovering roadmap of a startup; nevertheless, the correct way of success is not directly revealed in every situation. Thus, abovementioned perfect for the market notion must be comprehended at all costs since the number one reason of startup failures are due to wrongly anticipated market structures and such.

This is where big data notion comes in according to our perspective, since the latest technology paradigm suggests that big data is everywhere in data science. Big data has a lot of application alternatives that its applicability area is only bounded with imagination. Thus, many startups are working on big data analysis since technology allows it; thus, with every new technology the sense of being startup is widening more and more. As the new phenomenon is big data the market needs it as it needed cyber security in beginning of online banking in late 90’s. Therefore, a startup that chooses to work on the big data analysis area, provided that is has no financial issues, will become a successful example. In fact, the nature of startup notion suggests creative destruction where everything even a littler older than their alternatives are doomed to extinct.

Thus, analysis of big data field is full of exciting developments where a new enterprise models (startups) are paving the way for further implementation. Even though there are a lot of luck involved in that process, the humble reason of succeeding is catching the latest innovations that the market brings. Analysis of big data is the latest thing that market brings to even individual consumer level, so currently startup means big data.

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