Today, most organizations emphasize data to drive business decisions, and rightfully so. But data alone is not the goal. Facts and figures are meaningless if you can’t gain valuable insights that lead to more-informed actions.
Analytics solutions offer a convenient way to leverage business data. But the number of solutions on the market can be daunting—and many may seem to cover a different category of analytics. How can organizations make sense of it all? Start by understanding the different types of data analytics, including descriptive, diagnostic, predictive, and prescriptive analytics.
What are each of these categories? Are they related? In short, they are all forms of data analytics, but each use the data to answer different questions.
Analytics has become an indispensable part of doing business today. With data being generated from every touchpoint, companies have a goldmine of insights at their fingertips. But just collecting data is not enough. The real value lies in analyzing it to drive better business decisions and outcomes. This is where descriptive, predictive and prescriptive analytics come into play.
In this article, we will look at what these three types of analytics are, their key differences, real-world examples of how they are used and why all three are critical for organizations looking to leverage data analytics to its full potential.
What is Descriptive Analytics?
Descriptive analytics refers to making sense of historical data to understand what has happened in the past. It focuses on using statistical techniques like mean, mode, median and variance to summarize large volumes of data. The output is presented visually using reports, dashboards and data visualizations to highlight patterns, trends and relationships in data.
Some examples of insights gained from descriptive analytics:
- Sales performance over the last quarter
- Website traffic and conversion rates
- Customer demographics and purchasing habits
- Social media engagement and sentiment
- Inventory turnover rate
- Employee productivity metrics
Descriptive analytics enables businesses to assess their past performance on different parameters The rich visualizations make it easy to interpret key metrics and KPIs at a glance. This forms the basis for identifying problem areas as well as opportunities for improvement.
What is Predictive Analytics?
While descriptive analytics looks back at past data, predictive analytics looks forward. It leverages statistical and machine learning algorithms to identify trends and patterns in historical and current data. This knowledge is then used to make predictions about unknown future events.
Some common predictive analytics use cases:
- Forecast sales and demand
- Predict customer churn
- Estimate lifetime value of customers
- Identify credit risk
- Anticipate inventory requirements
- Predict equipment failures
- Target marketing campaigns
For example, an e-commerce company can analyze past purchase data of its customers to build a model that predicts the likelihood of a customer abandoning their shopping cart. This model is then used on current data to identify customers at high risk of churning so proactive campaigns can be run to retain them.
The ability to make reasonably accurate predictions enables companies to be more proactive and forward-looking, Predictive analytics minimizes risks and identifies new opportunities to drive growth
What is Prescriptive Analytics?
Predictive analytics helps anticipate the future. Prescriptive analytics takes it a step further by recommending the best course of action to capitalize on these predictions. It leverages machine learning and optimization algorithms to simulate different future scenarios based on proposed actions. It evaluates the outcome of each decision option and suggests the one likely to result in the most optimal outcome.
Some examples of prescriptive analytics:
- Optimize delivery routes for reduced fuel costs
- Identify the most effective product placement in stores
- Recommend product bundles/cross-sells likely to maximize revenue
- Suggest corrective actions to improve manufacturing quality
- Prescribe interventions to improve patient health
- Recommend optimal price points to maximize profit
For instance, a fast food chainplanning a new outlet in a city can leverage prescriptive analytics to determine the optimum location and kitchen layout design to minimize waiting times and maximize customer volume based on predictive demand modeling.
Prescriptive analytics elevates analytics from hindsight to foresight and enables data-driven decision making. It provides a competitive edge by helping organizations continually refine strategies to stay ahead of the curve.
Key Differences Between Descriptive, Predictive and Prescriptive Analytics
While descriptive, predictive and prescriptive analytics are complementary techniques, there are some key differences:
| Parameter | Descriptive Analytics | Predictive Analytics | Prescriptive Analytics |
|---|---|---|---|
| Objective | Understand past business performance | Forecast future outcomes | Recommend data-driven decisions |
| Focus | Historical data | Historical + new data | Simulations using predictive models |
| Output | Reports and visualizations | Future projections and trends | Suggested actions for optimal results |
| Common techniques | Data aggregation, mining, visualization | Statistical modeling, machine learning | Optimization algorithms, simulation |
| Answers what? | What happened? | What could happen? | How can we make the best outcome happen? |
Real-World Examples of Analytics in Action
Let’s look at some real-world examples that showcase how organizations are using descriptive, predictive and prescriptive analytics:
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Netflix leverages viewerwatch history and behaviors to provide personalized recommendations that keep users engaged on the platform. This has been made possible by combining predictive algorithms with descriptive data on content consumption.
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UPS uses predictive analytics to anticipate delivery demand during peak seasons. This allows them to optimize delivery routes and mobilize resources in advance to handle the increased load.
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JP Morgan applies sophisticated predictive models to identify transactions that might be fraudulent. This has reduced fraud losses by hundreds of millions over the years.
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Starbucks utilizes prescriptive analytics to provide store managers with optimal food production schedules for each hour of store operations based on predicted demand.
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John Deere uses prescriptive maintenance enabled by IoT sensors in its tractors to recommend maintenance activities before mechanical issues arise. This has reduced downtime by 65%.
These examples demonstrate how analytics helps drive data-driven decision making and tangible business value across industries.
Why Adopting All 3 Types of Analytics Matters
Many companies admit they struggle with moving from traditional backward-looking reporting to advanced analytics. Often, investments are focused predominantly on one facet like predictive modeling without equal emphasis on descriptive and prescriptive techniques.
However, research shows that high performing organizations successfully adopt an integrated approach leveraging descriptive, predictive and prescriptive analytics synergistically.
Here’s why embracing all three capabilities can maximize analytics ROI:
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Descriptive analytics provides the foundation to measure performance and identify opportunities worth exploring through advanced analytics.
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Predictive analytics allows mitigating future risks and failures. But organizations need prescriptive input to translate predictions into actions.
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Prescriptive analytics provides data-driven recommendations but needs predictive modeling to forecast future scenarios.
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Using descriptive, predictive and prescriptive analytics iteratively enables organizations to keep honing their business strategies continually. Insights from one technique feed into the other to drive a cycle of continuous improvement.
Integrating these three techniques also provides a balanced approach of leveraging historical data, new data as well as simulations to direct business strategy.
Key Takeaways
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Descriptive analytics deals with historical data to understand past performance. Predictive analytics identifies future trends and risks. Prescriptive analytics recommends data-driven actions to achieve optimal outcomes.
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Though complementary, the three types differ in objective, focus, output, techniques and analytical insight provided.
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Organizations like Netflix, UPS, Starbucks use descriptive, predictive and prescriptive analytics in tandem to drive growth.
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Adopting just one approach limits value. An integrated framework leveraging all three capabilities enables data-driven decision making and maximizes ROI.
In today’s highly competitive environment, organizations need to tap into the full potential of data analytics to stay ahead of the curve. While many focus their analytics efforts disproportionately on one capability, the biggest business gains come from implementing a holistic framework encompassing descriptive, predictive and prescriptive analytics.
Companies that have adopted an integrated analytics approach underscore the power of leveraging historical data, projections and simulations together to propel data-driven decision making. Organizations aiming to become true data-driven enterprises need to embrace all three facets within their analytics strategy and harness their synergies through an iterative approach. This is the key to accelerating competitive advantage with analytics and enabling continuous innovation.
Frequency of Entities:
descriptive analytics: 18
predictive analytics: 15
prescriptive analytics: 14
The 4 Types of Data Analytics
- Descriptive Analytics tells you what happened in the past.
- Diagnostic Analytics helps you understand why something happened in the past.
- Predictive Analytics predicts what is most likely to happen in the future.
- Prescriptive Analytics recommends actions you can take to affect those outcomes.
Let’s dive into each type of analytics and put them in context.
Descriptive analytics looks at data statistically to tell you what happened in the past. Descriptive analytics helps a business understand how it is performing by providing context to help stakeholders interpret information. This can be in the form of data visualizations like graphs, charts, reports, and dashboards.
How can descriptive analytics help in the real world? In a healthcare setting, for instance, say that an unusually high number of people are admitted to the emergency room in a short period of time. Descriptive analytics tells you that this is happening and provides real-time data with all the corresponding statistics (date of occurrence, volume, patient details, etc.).
Diagnostic analytics takes descriptive data a step further and provides deeper analysis to answer the question: Why did this happen? Often, diagnostic analysis is referred to as root cause analysis. This includes using processes such as data discovery, data mining, and drill down and drill through.
In the healthcare example mentioned earlier, diagnostic analytics would explore the data and make correlations. For instance, it may help you determine that all of the patients’ symptoms—high fever, dry cough, and fatigue—point to the same infectious agent. You now have an explanation for the sudden spike in volume at the ER.
Predictive analytics takes historical data and feeds it into a machine learning model that considers key trends and patterns. The model is then applied to current data to predict what will happen next.
Back in our hospital example, predictive analytics may forecast a surge in patients admitted to the ER in the next several weeks. Based on patterns in the data, the illness is spreading at a rapid rate.
Descriptive vs Predictive vs Prescriptive Analytics
What is the difference between predictive and prescriptive analytics?
One difference between each type of analytics is the data they use. Though all use historical data, predictive and prescriptive analytics also incorporate other data. Predictive analytics may use future information events to predict what may happen in a particular period.
What is the difference between predictive analytics and predictive analytics?
This uses a combination of historical data analysis, statistical modeling, machine learning and AI to make these forecasts. Prescriptive: Prescriptive analytics goes deeper than predictive analytics, looking at the relationship between certain variables and how these factors impact the final outcome.
What is prescriptive analytics?
Prescriptive analytics builds on the information provided by predictive analytics to recommend a course of action in response to the predicted outcome. Through analyzing prescriptive analytics you’ll be able to map out actionable steps to take in order to shape both the present and future of your business.
What is descriptive analytics & predictive analytics?
Descriptive analytics tells what happened in your business in the past week, month or year, presenting it as numbers and visuals in reports and dashboards. Diagnostic analytics gives the reason why something happened. Predictive analytics determines the potential outcomes of present and past actions and trends.
