Real-time data and predictive analytics are the talk of the town, but what if we could see into the future? Prescriptive analytics moves us several steps beyond predictive, allowing businesses to analyze possible outcomes of future decisions before they are even made.
There Is No “Either/Or”: Why We Need Both Predictive and Prescriptive Analytics
Both predictive and prescriptive analytics inform our business strategies based on collected data, but another major trend in business intelligence is the enhanced use of prescriptive analytics. So, what’s the difference between the two? The difference between predictive and prescriptive is “the former forecasts potential future outcomes, while the latter helps us draw up specific recommendations.”1 Summing up:
- Descriptive analytics tells us what happened.
- Predictive analytics tells us what we should expect to happen.
- Prescriptive analytics, the final step, recommends which optimal course of action we should implement and measures its repercussions.
Even though predictive and prescriptive analytics look at future scenarios by consolidating and leveraging mined data, prescriptive takes a “more technological approach…by utilizing complicated mathematical algorithms, artificial intelligence and machine learning.” As our data becomes cleaner and our AI smarter, prescriptive analytics has assumed a more integral role to the successful running of our business processes. According to Mick Hollison, chief marketing officer of InsideSales.com, we “shouldn’t rely on just one or the other; when used in conjunction, both types of analytics can help [us] create the strongest, most effective business strategy possible.” In today’s increasingly competitive landscape, our businesses “will require prescriptive analytics to provide intelligent recommendations for the optimal next steps for almost any application or business process to drive desired outcomes or accelerate results.“1
Soon, prescriptive analytics will allow companies to generate not just one but several viable “options and their respective potential outcomes,”1 thus reducing the margin of error for a bad or costly decision. And as our data improves, our prescriptive analytics can alter its predictions and better tailor its suggestions, ultimately charting a better course for our company’s future.
When Predictive and Prescriptive Analytics Work Symbiotically, What Does That Look Like?
Both types of analytics are used in our everyday lives. Take navigation apps for example: Motorists everywhere rely on GPS apps to go from point A to point B. So do small businesses that rely on delivery services, both third-party and in-house, to deliver goods in a timely manner. In this instance, predictive analytics can take existing travel data and map out a potentially faster route. When selecting an origin and destination in the navigation app Waze, for example, a multitude of factors are consolidated, and the app advises us on different route choices, each with a predicted ETA. This information can alter immediately if an outlier is registered, say a highway is suddenly reduced from four lanes to two due to an accident. “This is everyday prescriptive analytics at work,” according to Thomas Mathew, chief product officer of Zoomph.1
Furthermore, your solutions app should be intuitive in sending you precisely the information you need while away from your office. Artificial Intelligence (AI), just like a home security system, will become even more an integral part of mobile BI to help better figure out what alerts or notifications you need while you’re away. AI and machine learning (ML) will eventually decide what metrics and information is sent to your mobile BI device for you. With more employees working on-the-go, “traditional alerts triggered by pre-defined thresholds aren’t enough in this new and competitive data landscape. You need automated data discovery to do the digging for you while you’re away from your desktop, so you never miss a beat.2”
A Screen That Gets Me: Making BI Mobile Interfacing Easy to Manipulate for Any User
So How Can We Optimize Our Analytics Programs?
Think Big But Start Small
Data analysis can be overwhelming, and our best insights often remain buried within it. Immanuel Lee, a web analytics engineer at MetroStar Systems, advised thinking big with our overarching analytics strategy but starting small tactically. “With the complexity of big data and the systems that manage and process [it], we can easily overlook the fact that sometimes, there’s a solution in the simplest thing,” he said. “Small wins will help earn support for long-term analytics projects.”1
Create Rich Data Sets and Prove the Results Are Sound
Prescriptive analytics doesn’t work without good data, and predictive analytics doesn’t always account for alternate possibilities. The goal, according to Mathew is to drill down in our predictive analytics “to create richer information sets – for example, accounting for demographics such as gender and age – that will yield better results from our prescriptive recommendations.”1
Arijit Sengupta, founder of Aible, advises not to act too quickly on the results of prescriptive analysis. We must fully understand the logic, or the “why” behind the analysis so we can prove our results are statistically sound. “Pretty graphs can be very compelling, but this is only software, after all, and its analytical power is only as accurate as the human who designed it and the data we feed it,” he added. “It’s critical that business users understand the ‘story’ behind the results and the prescriptive action suggested.”1
Keep Our Systems and Software Current
As our business evolves, so should our systems and software. Both predictive and prescriptive analytics should be continuously updated with the latest data to improve predicted and prescribed actions based on real-time successes and failures.1
Most, but not all, modern business intelligence (BI) tools have built-in prescriptive analytics to provide users with actionable results that empower them to make better decisions. Be sure you are working with the right tools that give you access to this game-changing trend in BI.
The Copley Consulting Group can help. We have more than 30 years of experience helping manufacturers, suppliers, and contractors transform their businesses. Contact us to help evolve your business intelligence needs.
1. Predictive or Prescriptive Analytics? Your Business Needs Both