infoTECH Feature

September 09, 2016

Ways Startups Can Leverage Big Data for a Competitive Advantage

People who are new to the world of big data tend to think that it can only be leveraged by Big Business and for the benefit of Big Brother. While it may be that big data is often used by major corporations as well as by surveillance organizations, the truth is that even small tech startups can benefit from data-driven practices.

Before discussing the various ways that big data can help entrepreneurs achieve success, it is important to understand what is behind this information concept.

As an industry term, big data was formalized by tech analysts in the early 21st century. In general, the principle of big data has always been around as a human practice of collecting and storing volumes of information for analytical purposes. The Ancient Library of Alexandria in Egypt, for example, was a big data repository prior to its destruction.

The modern big data practices are notable for being voluminous, diverse and very speedy. In the past, the data collection and analysis processes took considerable time and effort; thanks to modern technology, very large amounts of information can be collected and analyzed in real-time.

How Tech Startups Utilize Big Data

The personal transportation service Uber is one of the most celebrated tech startups known to use big data extensively. Uber's controversial “surge pricing” model is a product of regression analysis, whereby data specialists come up with projections that suggest when to entice more drivers to be on the road so that they can meet increased passenger demand.

By now, Uber is perhaps too large of company to be considered as an example of a tech startup taking advantage of big data. Nonetheless, there are many examples of small businesses that have successfully incorporated big data to their advantage.

Big data providers do not exclusively serve Big Business. Real estate brokerages can purchase modest subscriptions to data sets from their local property markets. With the right algorithms and data filters, a real estate agency can determine how to price their listings strategically and at the highest price that prospective buyers are willing to negotiate; this strategy can lead to quick closings in an active market.

Is Your Data an Investment or a Product?

All business enterprises produce data; it is up to the principals to decide how to use it. The two basic ways that a tech startup can profit from the data it produces is to either analyze it or sell it. 

The aforementioned transportation service Uber is a company known to sell data to marketing intelligence firms. Small tech startups can do something similar, but it may be a good idea to have an attorney check the privacy laws that govern consumer data prior to selling information.

Tech startups interested in selling data will likely need to install software solutions such as Tableau and Cloudera, which can be easily programmed to collect, organize and analyze data in real-time. Once this software is installed and running, data can be packaged and sold to market research and intelligence firms. For the most part, selling data will not be as profitable as gathering insights from data sets and applying them to the business process.

Data can also be offered to clients and customers for the purpose of engagement or for value added services. Tech startups that offer digital wallets are good examples of providing lots of data about spending patterns. Another example is mobile game developers who provide fun statistics about how players around the world are interacting with their creations.

A good way to get started with big data is to visit information visualization website that provide coverage for your business industry. Infographics and data visualization charts are all the rage these days and there is something for everyone to learn from those images. Data visualization can also be included in communications with customers, clients and business partners. 

In the end, tech startups that fail to embrace big data risk falling behind the curve; after all, this is an industry that is projected to generate revenues of more than $42 billion by the end of the decade. Over the next few years, the lessons learned from big data will be applied not only in the business sphere but also in everyday life.

Edited by Alicia Young

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