BIG DATA AND PREDICTIVE ANALYTICS-Big data and predictive analytics have undoubtedly changed business as we know it. Large enterprises that have effectively leveraged predictive analytics like Amazon, Netflix, and Walmart are all enjoying massive success in their respective industries. Their data efforts have allowed them to engage customers effectively through targeted marketing and be more efficient by streamlining their supply chains.
However, as big data and analytics adoption grows, discussions concerning their ethical use are also emerging. The Facebook and Cambridge Analytica scandal has shown how powerful predictive analytics can be in influencing human behavior. Instead of the technology only being used to benefit a select few, there are now calls for the technology to be directed towards the public good.
Here are four ways big data and analytics can be oriented to benefit more stakeholders.
1 – Lower Technical Barriers
One-way predictive analytics could start reaching more people is through lower technical barriers. Previously, only large enterprises with the resources to have data scientists and developers work on efforts in-house were able to apply the technology.
Fortunately, powerful analytics solutions that minimize the need for high levels of technical expertise are now on the rise. For example, Endor has made enterprise predictive analytics more accessible even to non-data scientists. As an MIT spinoff, Endor based its technology on social physics – a field of study that applies mathematics and natural sciences to the analysis of human behavior. Through social physics and artificial intelligence, the company has effectively created a “Google for predictive analytics.” Users simply have to key in questions and get relevant insights as answers.
Through such tools, anyone within an organization can readily work on their data and get forecasts and predictions without needing to extensively learn how to perform the various technical methods for analyses.
2 – Democratized Access
Aside from lowering technical barriers, access to predictive analytics can also be democratized. Knowledge and skills aren’t the only requirements to perform successful data and analytics efforts. Organizations must first have data to process. This is why large enterprises that have long been gathering data have a leg up on others since they already have information about markets and customers on hand. In addition, other costly resources such as storage, and processing power are also needed to perform the complex computations to make sense of big data.
Fortunately, big data is now also becoming more accessible. Blockchain-driven data marketplaces such as those offered by Datum and IOTA allows for secure and decentralized means for data buyers and sellers to transact with each other. The ability of Internet-of-Things (IoT) devices to gather various sensor data and broadcast them has also allowed data streaming to become viable sources of real-time data.
There are also various projects like SingularityNET and Golem that seek to allow users to tap into crowdsourced computing power, storage, and AI. All of these developments allow smaller organizations and even individuals to get access to the resources needed to perform analyses.