Title: Prescriptive Analytics
Author: Kriko
Published: Jun 8, 2023
Last modified: Jul 7, 2026

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# Prescriptive Analytics

Prescriptive analytics is an analytical approach that uses predictive modelling,
optimisation methods and advanced algorithms to recommend the most appropriate actions.
It does not merely estimate what may happen. It also provides guidance on what should
be done in response to possible outcomes.

To generate these recommendations, prescriptive analytics systems evaluate complex
inputs such as business rules, structured and unstructured [data](https://kriko.io/services/data-analysis-insight),
operational constraints and potential scenarios. Systems supported by artificial
intelligence can continuously incorporate and analyse updated [data](https://kriko.io/services/data-analysis-insight)
streams.

This capability enables prescriptive analytics to process large datasets, identify
meaningful relationships and produce recommendations based on the likelihood and
potential impact of different outcomes. These systems may also learn from previous
successes and failures, allowing them to deliver increasingly relevant insights.

Businesses use prescriptive analytics to support decision-making, improve operational
performance and identify actions that may increase profitability.

Prescriptive analytics is closely related to predictive analytics. Predictive analytics
examines existing and historical [data](https://kriko.io/services/data-analysis-insight)
to estimate what is likely to happen, while prescriptive analytics goes one step
further by recommending the actions that should be taken.

Once sufficient [data](https://kriko.io/services/data-analysis-insight) is available
and the relevant conditions are defined, prescriptive analytics can be used to compare
alternative courses of action, apply business rules and determine which option is
most likely to produce the desired result.

The analysis may draw on machine learning, artificial intelligence, statistical 
methods, market research, optimisation techniques and expert knowledge. Since the
results are based on probabilities and assumptions, they are generally presented
together with uncertainty levels, confidence scores or potential risk factors.

Prescriptive analytics focuses on actionable insights. It can help organisations
answer questions such as how to benefit from a future opportunity, how to reduce
potential risks and which strategy should be prioritised under specific conditions.

As a form of business analytics, prescriptive analytics is connected with descriptive,
diagnostic and predictive analytics. It can be used to improve business performance,
develop strategies, allocate resources, monitor market developments and adapt algorithms
to specific organisational objectives.

However, the outputs of prescriptive analytics should be treated as decision-support
guidance rather than guaranteed results. Recommendations must still be evaluated
in light of business context, professional judgement and changing market conditions.

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