6 Ways to Convert Data into Cash
For the past years, tremendous amounts of data are being produced every day. This big data phenomenon is shifting the global economy towards profitable complex data analysis and management. Entrepreneurs and huge companies alike are looking into this fact and are finding ways to make such data profitable. It’s certainly creating not only business opportunities, but job opportunities as well.
A research from McKinsey&Company says benefits vary across business sectors. Those poised to gain substantially from this are “the computer and electronic products and information sectors, as well as finance and insurance, and government”. Other businesses such as retail, online and offline, can still benefit from it when used effectively.
This article discusses how businesses nowadays efficiently and profitably utilize big data in management, customer/service specialization, product/service innovation, operations, and in creating new business models. It poses some of the implications of this growing industry to the business sector, job-creating opportunities, and policy-making, including the internet sales tax.
Big Data is taking over the economy, one business sector at a time. In fact, with the massive volume of data of about 2.7 Zetabytes existing in the digital universe today, it’s projected to become a $50 billion business by 2017. It’s certainly creating a buzz; opening up opportunities never thought possible.
Here’s 6 ways to convert data into cash:
Through automated algorithms, managers can make better decisions through a more precise analysis of data. Rather than anticipatory analysis, they generate predictive analysis through hypothesis-testing via controlled experimentation. With this, they can test which works best with what strategy with minimal to no risks at all.
Customer or Service Specialization
Companies can better reach their target consumers through segmentation. This way, they see which products and/or services are appropriate with which consumer segment. Facebook, for instance, matches users with features and ads. By combining data from various applications, they create behavioral patterns and therefore, make appropriate suggestions. Meanwhile, Amazon offers various content and dynamic pricing, using real-time analysis, to different sets of users and makes adjustments as they go.
Product or Service Innovation
Crowdsourcing is made more effective through social media. Consumer insights can be acquired from various tweets (converted to usable data) which companies use for product/service development and innovation. Also, data is becoming a standalone product as analytics utilize them to provide companies with rich insights to improve operations, thereby creating new revenue streams.
Rather than learning in retrospect, preventive measures could now be taken through predictive analysis. GE, for instance, can help their customer airlines monitor GE jet engine performance, therefore, anticipating maintenance needs.
By analyzing and comparing data among store branches, a certain retail company can appropriate store performance and consumer behavior with product distribution—limiting overstocking.
New Business Models
Big data opens new business ventures. For example, real-time location data is booming with more people using smartphones for navigation and by businesses for mobile advertising. Even insurance companies can now price risks based on a person’s driving behavior rather than his age via mobile apps.
Organizations and business leaders will have to inventory their data assets in order to identify potential value-creating opportunities. The gap between supply and demand for human capital (i.e., data scientists) is projected to reach 140-190,000 people by 2018. While this creates job opportunities, it also demands equipping people.
Technology development is integral since only a few can handle a tremendous amount of data, not to mention, can effectively manipulate, aggregate, and analyze such data.
Privacy and security are still an issue for the IT and business industry. Policy-makers have yet to address legal concerns, establish intellectual property framework, and create policies that balance privacy concerns with capturing value.
When utilized to their advantage, businesses can profit greatly. Small businesses could even offset losses posed by the internet sales tax. If sales boom through effective real-time analysis, they won’t be hurt as much. Then again, the big data industry still has a long way to go as far as policies are concerned.