8 Smart Ways To Use Prescriptive Analytics - InformationWeek
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6/28/2016
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Lisa Morgan
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8 Smart Ways To Use Prescriptive Analytics

Somewhere between blind faith and skepticism is the world of prescriptive analytics. Here, machine-generated action items and potential outcomes meet human decision-making. Finding the right balance between algorithms and common sense can be tricky, so consider these tips.
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(Image: geralt via Pixabay)

(Image: geralt via Pixabay)

Marketing and retail have been two of the most publicized use-cases for prescriptive analytics, a type of predictive analytics software that recommends one or more courses of action and shows the likely outcome of each decision.

While prescriptive analytics isn't as mature or widely adopted as descriptive analytics or predictive analytics, Gartner estimates the prescriptive analytics software market will reach $1.1 billion by 2019. Beyond marketing and retail, such tools are starting to be applied in cyber-security, fraud prevention, supply chain optimization, and resource optimization, among other areas of business.

"Prescriptive analytics can take processes that were once expensive, arduous, and difficult, and complete them in a cost-effective and effortless manner," said Doron Cohen, CEO of Powerlinx, a business-to-business matchmaking service, and chairman of Dun & Bradstreet Israel. "The ROI derived from having more time, energy, and money can then be used to identify new opportunities as a business."

Prescriptive analytics systems learn over time, and there's no guarantee their output will always be reliable. "Application of algorithms is not a substitute for robust investigational methodology or common sense," said Willy McColgan, president and general manager of machine intelligence analytics company Zoomi, in an interview. "We have uncovered non-intuitive patterns and correlations, but data-conversant leaders who can see the confounding factors are still necessary [to] apply a sniff test where we may have made assumptions in our experimental design that do not hold up generally."

John Houston, a principal who leads the Advanced Analytics and Predictive Modeling service area at Deloitte Consulting said in an interview he sees adoption of prescriptive analytics software taking place "from the outside in." While customer-facing solutions are more common now, the number and types of use-cases will grow in organizations.

"Future adoption will move into the day-to-day decision-making of knowledge workers, first with tactical, high-frequency decision types and then [to] more strategic, lower frequency decision types," said Houston.

But what, exactly, is the tipping point that makes companies embrace prescriptive analytics? According to Forrester Research principal Michele Goetz, companies start adopting prescriptive analytics when they get focused on why something is happening, and how they can get the information into areas of the business that can immediately act upon it.

"Most organizations that are achieving results from their prescriptive analytics are gaining insight through data science activities," said Goetz, in an interview. "More mature organizations are exploring machine learning and evaluating real-time and near-real-time deployments."

Regardless of where you are in your journey with prescriptive analytics, we've identified eight issues and opportunities to consider as you seek to derive business value from your investments. Once you've reviewed these factors, tell us about your own experiences.

Have you worked with prescriptive analytics software? Is it something your company is considering in the year ahead?

Can such tools every really replace good old-school common sense? Tell us in the comments below.

Lisa Morgan is a freelance writer who covers big data and BI for InformationWeek. She has contributed articles, reports, and other types of content to various publications and sites ranging from SD Times to the Economist Intelligent Unit. Frequent areas of coverage include ... View Full Bio

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LisaMorgan
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LisaMorgan,
User Rank: Moderator
7/8/2016 | 4:23:17 PM
Re: Prescriptive analytics: A thought experiment
I worry about the accuracy of prescriptive analytics too because I think the problem is being oversimplified in a lot of cases.
LisaMorgan
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LisaMorgan,
User Rank: Moderator
7/8/2016 | 4:15:31 PM
Re: Prescriptive analytics: A thought experiment
Thanks for mentioning it.
GaryB790
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GaryB790,
User Rank: Strategist
7/5/2016 | 11:05:30 AM
Re: Prescriptive analytics: A thought experiment
@StevenL530,

I think that you may have missed some information somewhere. The Prescriptive Analytics data is not generated in a vacuum and the data is not static it is a function of accumulated data over some time period.  If need be, there could be a [pick your time frame] window to ensure that the trending isn't under reported by having a massive dataset showing the opposite of what is actually happenning.

Within the Prescriptive Analytics algorithm there should be a "feedback loop"...for example, page hits, or some socitial trending information gathered from somewhere else noting the trending of deserts, sweetners (artificial vs natural), baked or fried or frozed....etc.

Like all prediction algorithms, it takes time [for data sample collection] for the algrithm to optimize and predictive type formulation....and the correct data being sampled.
SteveR329
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SteveR329,
User Rank: Apprentice
6/29/2016 | 6:26:29 PM
A journey not an event
Great article.  I agree with most points.  Data normalization is critical (garbage in, garbage out) as is the human factor.  People are fickle which makes trying to predict the decisions they will make a very big challenge.  It is still a journey worth taking, but as you point out, expect it to be a journey not an event.
StevenL530
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StevenL530,
User Rank: Strategist
6/29/2016 | 10:52:12 AM
Prescriptive analytics: A thought experiment
Your algorithm prescribes adding the Fruity Pebbles cheesecake recipe to your web content. The counter-intuitive recommendation is only counter-intuitive the first time. The recommendation now becomes intuitive, routine. So each year, in late spring, you run another recipe just like this, Cap'n Crunch Cupcakes, Fruit Loops Parfait, and so on. But something changes. One year, the recipe no longer garners clicks. Visits are down. Will the prescriptive algorithm double down on its advice, run more breakfast cereal no-bake recipes? Or will it detect the root of the new trend? If it is just telling me what to do and not why, how does the managing editor know when to change course and in which direction? With dozens of statistical models forecasting every possibility, how to choose? Weather prediction has been banging up against this challenge for over a century. In business, we are always on the hunt for the disruptive change. Not only will prescriptive analytics miss the disruptive change, it will reinforce management's reliance on these now misleading analytics far longer than they otherwise might.
ShannonK584
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ShannonK584,
User Rank: Apprentice
6/28/2016 | 1:53:15 PM
A bit of a limited review of the potential of prescriptive
Many points brought up here are valid. However, I worry that the view of potential use cases / growth with prescriptive are quite limited to the idea of heuristics (rules-based) decision automation that people (including Gartner) are incorrectly calling prescriptive analytics. 

If you view prescriptive analytics as optimization (linear programming), the use cases and benefit are much wider reaching. Also, you don't run into the probably of having misinformation from prescriptive (e.g., infeasible plans or plans that are not optimized to your business objectives). 

I recommend reviewing this article and considering redefining your idea of "prescriptive analytics." The use cases and benefits of what you refer to here are quite different from the use cases and benefits of true optimization. Furthermore, there are far fewer problems, and the benefits make attacking change management issues a no-brainer.
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