Predictive analytics is an emerging development among major corporations worldwide that utilizes big data to answer questions and gain information about the future: consumer trends, market shifts, and so on. This form of analytics is opening new doors to businesses by enabling them to be better prepared for the future.
However, current manifestations of predictive analytics have arguably fallen flat. The massive costs associated with this industry on necessities such as data science teams, infrastructure, and proprietary data make it inaccessible to the vast majority of businesses. Beyond this, the legitimacy of predictive analytics remains in contention. Many skeptics suggest that the it’s not possible for computers and algorithms to predict the future. Between 1980 and 2016, only 52 empirical papers explored these statistical techniques, with only seven of those conducting proper testing.
Despite criticism, big data has enabled predictive analytics to modeling to grow into a $5 billion industry, with projections of over $12 billion in growth by 2022. This valuation is comprised of the few dozen commercial solutions such as SAP Predictive Analytics and IBM SPSS. Unfortunately, these services are far too primitive for the advanced internal operations of a company such as Amazon.
Regardless, the costs are still quite significant. SAP runs at a base price of $20,000 per desk, with a minimum purchase of five desks. IBM’s service, which is more accessible for individuals and small businesses, can still cost over $8,000 per year to unlock all features. Of course, these costs don’t include the training required to make use of their services. While expensive, these commercial solutions, like their internal enterprise counterparts, fail to address concerns over the value and accuracy of the given analytics.
So while big businesses in retail and finance have been able to leverage proprietary data and major investment to optimize marketing campaigns, protect against fraudulent activities, and improve operations, these practices have remained widely inaccessible to most SMBs.
Shifting the Paradigm
Recent initiatives in predictive analytics have begun to form a different approach to resolving current issues using Social Physics. By utilizing big data, the idea computational theories of human behavior can be the basis for predictions of behavioral patterns, wants, and motivators, to name a few patterns.
Traditional predictive analytics rely on machine learning as the prediction vehicle. It is the reliance on computers for predictions that is the source of discontent among skeptics, as the capabilities of these machines remains unproven. With a Social Physics driven approach, predictions require far less resources, as complex models do not need to be crafted for each predictive question.
Enabling Predictive Analytics for all Businesses
While these developments in Social Physics remain relatively new, there are projects that currently offer Social Physics-based predictive analytics. A leading example is Endor, whose mission is focused on creating a platform where individuals and businesses of all sizes are welcome to participate.
Endor leverages Social Physics with a blockchain-based platform to provide an experience best defined as scalable predictions. This project is unique in that its blockchain-centric approach, allowing systems and computations to communicate and cooperate with one another, a quality not previously thought possible.
What this means is that queries to the platform can share computing resources with similar queries that would, perhaps, perform the same computations and draw from the same data sources. For example, retailers looking for insights into the upcoming Holiday season will likely ask many similar (even identical) questions: expected breakdown of gift speeding across industries and niches, preferences among where and how shopping is likely to take place, and so on. The platform analyzes these queries and minimizes highly redundant, or duplicated variables that carry the same information.
This approach is quite different from traditional alternatives, and represents a far more accessible platform for SMBs. Use of Endor requires zero data science background- users simply ask a question and receive a response. The cost of each question encompasses the resources directly utilized to answer said question, and as more and more users interact with the platform and ask similar questions, the cost per user will decreasing impressively.
Combined with the highly accurate Social Physics approach, Endor is great for preparing your business for both the near and long-term future alike. Don’t be discouraged by any lack of “exclusivity”, either, as they are already working with an enterprise clientele consisting of major corporations such as Walmart, Coca Cola, and Mastercard. The platform can be used for marketing, research and development, fraud prevention, and consumer preferences by any business with pinpoint accuracy.
Endor is unique, no other existing projects or businesses are working towards blockchain-driven, Social Physics predictive analytics solutions. Whether a small business, individual, or enterprise, the company enables new and valuable resources for companies of all backgrounds and objectives to prepare for the future and succeed above their competition.
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