According to a 2020 Gartner survey, analytics technologies were rated as highly impactful and transformational by 60%. Over the last three years, analytics technologies and solutions have matured further, and the conversation has shifted from their value to their necessity in the present-day business landscape.
An overview of analytics technologies
As such, it is crucial to understand the various types of analytics technologies, and how each of them make an impact on your bottom line. An early Gartner Analytics Ascendancy Model classified analytics technologies into 4 categories:
- Descriptive analytics
- Diagnostic analytics,
- Predictive analytics, and
- Prescriptive analytics
Whereas descriptive and diagnostic analytics offer you hindsight, predictive and prescriptive analytics give you the power of foresight. Therefore, they can help you make better business decisions and achieve better business outcomes over time.
Take a look at each of them, and how they differ.
What is predictive analytics?
Predictive analytics technologies are used to understand what might happen in the future. They make use of historical values of independent variables to predict how their change influences an outcome in the future.
For example, if you want to know when a rotor belt might fail, a predictive analytics solution will observe changes in bearing vibration patterns to spot an upcoming fault.
Predictive analytics makes use of machine learning methods like association models, segmentation models, decision trees, or statistical techniques under the hood. The selection of the technique depends on the use case in question.
What is prescriptive analytics?
Prescriptive analytics tells you what you should do over a course of action. It makes use of heuristic and exact algorithms to help you make the best possible decision. Therefore, prescriptive analytics goes one step beyond predictive analytics.
Consider a common scenario where prescriptive analytics could be applied. Suppose that a grinder is showing rising ball bearing temperature. The bearing is likely to fail in the next 7 days, but slowing the machine could prolong the failure to your next maintenance window. A prescriptive analytics solution can help you infer how much reduction in the speed of the grinder could prolong the failure and still help you meet your operational targets.
A prescriptive analytics solution will usually leverage multiple machine learning techniques. It may also use both structured and unstructured data, and analyze multiple scenarios to prescribe the most optimal decision in a situation.
Predictive vs prescriptive analytics
Take a brief look at how predictive analytics differs from prescriptive analytics.
| Predictive analytics | Prescriptive analytics | |
| What it does | Forecasts a future event based on past trends. | Recommends the best/optimum decision over a course of action. |
| Applicability | Best suited for scenarios where the ability to anticipate an event holds value. | Suitable for scenarios in which a decision must be made, based on multiple factors. |
| Difficulty | Usually less difficult to implement. | More difficult to implement than predictive analytics. |
| Use cases (examples) | Predict faults or anticipate wear of machines and critical assets. | Optimize maintenance schedules across plants, or oil drilling optimization. |
Typically, a prescriptive analytics solution will also leverage predictive technologies to forecast or anticipate a future state. Along with these predictions and other data models, it will leverage data analysis techniques like heuristics or optimization to recommend a decision. Prescriptive analytics solutions are therefore better suited for highly complex planning or to aid long-term decision-making.
Modern analytics solutions like Uptime AI leverage both these technologies to power various features.
Is one better than the other?
In some scenarios, yes. For example, if frequent machine failure is causing unplanned downtime at a simple manufacturing facility, a predictive analytics solution will be adequate to anticipate machine failures. Using its predictions, the failures can be avoided by scheduling maintenance before the failure occurs.
However, if you need to optimize drilling at an offshore rig, you will need to use prescriptive analytics. Why? Because drilling optimization is a complex problem which must take hundreds of independent variables into account.
In other words, one technique is not necessarily better than the other. Depending on your use case, one will be better suited than the other.
Predictive and prescriptive analytics in action
Take a look at how these technologies are leveraged in the following use cases.
Estimating cement quality: predictive analytics
In the cement industry, the quality of the end-product is largely dependent on the clinker quality. For manufacturers that need to meet a certain quality level, optimizing clinker quality is crucial. Clinker quality, in turn, depends on the calcination temperature, kiln environment, and the cooling process. By observing these variables, it is possible to accurately anticipate the quality of the end product in advance.
Line speed optimization: prescriptive analytics
In manufacturing, demand fluctuation can result in overproduction or underproduction, each of which can result in lost value. Therefore, optimizing the speed of production based on demand factors is crucial to avoid it. This is a complex problem, which entails balancing production process and resource constraints, scheduling overtimes, and mitigating downtimes. Prescriptive analytics can be used to optimize line speed while navigating through these factors to effectively meet changing demand levels.
Speeding asset maintenance: prescriptive analytics
Typically, when an asset is about to fail, field technicians are dispatched to discover the cause of failure and carry out a repair. Even if a failure can be predicted, the root cause is generally not known. With a prescriptive maintenance solution, the technician can be informed of the cause of anticipated failure, enabling them to conduct the maintenance work within the shortest span of time. This can be highly valuable in scenarios where minutes of downtime can result in massive losses.
To sum it up
Predictive and prescriptive analytics technologies prove highly effective in maintenance, repair, and operations optimization. Whereas prescriptive analytics is suited for complex decisioning scenarios involving multiple variables, predictive analytics can help anticipate a future event with high confidence.
Modern analytics solutions may implement both these technologies simultaneously to forecast future events, and prescribe the best course of action to tackle them. This can be highly beneficial in maintenance operations at complex facilities.
Leverage the best analytics technologies to eliminate downtime and optimize your maintenance costs with UptimeAI.