Top 4 Ways Big Data and Predictive Analytics Can Benefit More Stakeholders

August 19, 2019 0 comments

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.


3 – Better Data Protection

Among the pressing concerns of the computing public when it comes to big data and predictive analytics is privacy and data security. Large enterprises have been criticized for their unadulterated use of customer information for aggressive targeted marketing. The recent string of data breaches that saw hundreds of millions of user records stolen by cybercriminals have also put these users’ personal and financial security at risk.

As such, anyone who dabbles in big data must take data security and protection into consideration. Fortunately, data marketplaces have taken consent and compensation into consideration. They provide mechanisms that ensure that the data being bought and sold over their platforms are aptly anonymized or that consent from the data owner has been properly obtained.

Through better security measures, the public could rest assured that their privacy is being respected and that their risk of becoming victims to fraud and identity theft are also minimized.


4 – Socially Responsible Efforts

Much of the buzz concerning successful applications of big data and predictive analytics have been about commercial applications. However, companies could actually start using their big data capabilities for social responsibility. If they can use the technology to compel users to buy their products, they should also be able exert the same influence to encourage people to do good.

Aside from the private sector, various efforts by governments to leverage predictive analytics to support policy-making especially in social services have already been undertaken. Gartner has identified that AI and analytics are at the top of the agenda of government CIOs.  One could be hopeful that these efforts would result in better social services.

For example, using predictive analytics in healthcare to identify at-risk individuals and communities could help the corresponding government offices to readily reach out to these citizens and provide help. People could clearly benefit from such initiatives. It’s high time that such efforts that focus on contributing to the public good get better support and attention.


Benefiting Everyone

Big data and predictive analytics should not be constrained to serve the purposes of the few. Wider accessibility could help more people enjoy its benefits. Even the act of allowing smaller enterprises to leverage big data and predictive analytics can help level the playing field and chip away at the dominance of industry giants. Healthy competition should be beneficial for all stakeholders. Considering how revolutionary the technology has been, it should be able to serve the public good or at least have a trickle-down effect that benefits more people.

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