Big Data + Prescriptive Analytics = A Better World?

As data is the new oil, data analytics is the new oil field pump. The oil pump is used to draw oil from underground reservoirs, so is data analytics used to uncover something you knew you had, but had little understanding of its quantity, quality or value.

Brands are on a marathon of collecting and gathering data. And because of this, the analytics landscape is constantly changing and surfacing more and more accurate metrics and analytics models. As a result, marketers are becoming obsessively focused on testing their hypothesis and showing ROI for their spending. Marketers are not happy anymore with only measuring likes and shares. The existence of sophisticated analytics solutions empowers them to capture the right data and find practical ways of capitalizing on it. Sometimes the paradox is that the more data it is available, the more questions still need to be answered.

2015 has been a generous year for digital marketing, with so many trends that aimed at revolutionising the industry and so many technologies created to help marketers make a difference. Prescriptive analytics is the topic that I’ve enjoyed the most reading about, however, I must note that I couldn’t see as many research papers or use cases as I wished.

Prescriptive analytics help companies find the most likely outcome for a situation and find the best course of action based on existing goals, requirements, and limitations. We know how great Amazon are doing on this area with their “anticipatory shipping” (Amazon is able to start shipping products to you before you even order them). Plus, some airline companies and supermarkets are using prescriptive modeling to anticipate our buying needs or determine the optimal pricing structures. I think it’s super cool that some companies have the resources and processes in place to even be able to perform outcome simulation for potential marketing or sales campaigns.

But, here is my first question:

Is prescriptive modeling available and relevant only for companies such as Google, Amazon, IBM or can it work perfectly in the environment of SMB or even non-profits? And if so, what would be the challenges?

Big data may look like a challenge by default for many companies. However, the real challenge with applying prescriptive modeling in the context of big data is ensuring that we’re tackling the right problems. Worldwide there are dozens of startups with noble causes that could benefit from such solutions without having the need to hire an army of excessively expensive data scientists and data analysts. However, it’s necessary to consider that prescriptive analytics is not just about technology, it’s also about having the right people asking the right questions in an environment that could implement the change in the right ways.

Here is my second question:

How prescriptive analytics is used/ or could be used in industries such as healthcare, pharmaceuticals, education or information security?

And I’m not referring to the use of prescriptive analytics at the level of which it helps the companies reveal new business insights or gain a competitive advantage. I’d be more interested to see companies who are using prescriptive analytics as a tool for fighting, anticipating and reducing the cases of cancer patients or cyber attacks. Should this still sound like sci-fi in 2015? Is it naive to think that big data could save the world (well, at least play an important part)? Why these industries haven’t yet got a lot of love from data analytics?

It can be argued that it should be the governmental institutions (not companies) who have access to adequate amount of high-quality data in order to discover actionable information from any existing data sets. Although there is great progress and use of predictive analytics registered in some areas (such as crime analytics, travel industry), still there are so much more industries that could benefit from it. Healthcare institutions could use prescriptive analytics for their most demanding season to optimize high demand or improve resource allocation. Similarly, educational institutions could use prescriptive analytics to improve student enrolment, retention, and curriculum planning, etc. Also pharmaceutical organizations can benefit by improving their drug development and reduce time-to-market for new medicines.

I’m sure that the future will witness the rise of better use of big data and prescriptive analytics, but it will take time, as prescriptive analytics is an “Innovation Trigger” that could take another 5-10 years to reach the plateau of productivity. Although I love history and understanding the past, I would also be very keen in 2016 to read more success stories where brilliant minds are performing critical path analysis and translating big data into actionable and feasible plans in order to make the world a better place.

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