Big Data (15)

Can Big Data Take the Guesswork Out of Identifying Successful Startups?

For every ten businesses started, nine will fail. The failure rate fluctuates by industry, with nearly 60 percent of real estate businesses hanging on for at least four years, compared to less than 40 percent of IT businesses. Reasons vary from an inability to price products properly to a failure to properly pay taxes. What if we could predict which startups would succeed and which would fail? As it turns out, big data may be able to do exactly that.

Identifying Successful Startups is Big Business

Big data

Venture capitalists and lending banks would love to be able to predict which businesses would return lots of profits and which would result in losses.

According to figures from the Small Business Administration, it takes an average of $30,000 to start a new company. Few entrepreneurs have that kind of cash sitting around, so most turn to some type of loan to start their business. Some seek loans from banks, while others pitch their business concepts to investment bankers. The institutions and entities making these loans and investments sure would love to take a big chunk of that risk off their shoulders. Therefore, they’re willing to pay data firms well for the lowdown on which startups are worth their dollars versus the ones who should be ushered to the door.

How Big Data Can Help Identify Successful Startups

Big data

What big data says about startups is not always intuitive. For example, employees who have had lots of previous jobs aren’t necessarily job hoppers. You can’t assume you know what the data is going to indicate.

A number of factors can determine which startups are most likely to succeed or fail. Using big data analytics, data firms can enter a variety of data points on startup companies and then analyze that historical data to determine which factors most play into the success or failure of the business. That data can then be applied to new startups to evaluate which ones have the best (or worst) chances. According to the data scientists working for these firms, the factors that determine success or failure aren’t as cut and dried as we would like to believe. Sometimes the reasons seem rather flimsy or random. Nevertheless, there are some indicators that are pretty reliable in identifying the success of a new company or business concept:

  • -The experience of the company’s founder – A founder’s experience in the industry usually means a solid start for the business and smart management.
  • -The Gartner hype cycle – Any given technology has a ‘hype cycle’. For tech companies, a business has a far greater chance of success if it is born during the peak of the hype cycle. Startups born too early or too late in the cycle have much less chance of riding the wave of the new technological innovation to inevitable success.
  • -The hiring practices of the business – Employees play a huge role in the success of a business. Smart job advertisement, employee screening practices, and engaging workers so that turnover is low are all factors that can affect a startup’s chances.
  • -The industry the business is in – Some industries are growing, some are stagnant, and others are shrinking or struggling. Even the best managed companies with solid hiring practices and a smart pricing structure likely won’t succeed if the industry is stale or in decline.
  • -The startup’s competitors – Lots of competition makes it extremely hard for a new business to stand out and make a mark. This can, however, vary by location. For instance, a barbecue joint in Dallas, Texas or Memphis, Tennessee has intense competition by established restaurants, and a low chance of survival. However, the same business might have a greater chance where barbecue restaurants are less plentiful.

Data for calculating the success of a startup is plentiful. Much of it is available through government reports, social media, and other public records. However, it is usually best to turn to a data firm with experience in evaluating startups, as the science is very new and the practice is still quite nebulous in nature.

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