Business analytics can be categorized as descriptive, predictive, or prescriptive. Descriptive analytics is the process of using historical business data to understand why certain events happened and summarizing the information into an easily consumable format. Predictive analytics uses data to make forecasts and predictions about what will happen in the future. Prescriptive analytics uses statistical models and machine learning algorithms to determine possibilities and recommend actions. These models and algorithms can find patterns in big data that human analysts may miss.

While descriptive analytics focuses primarily on what has already happened in the past and predictive analytics tries to find correlations to make forward-looking projections, prescriptive analytics looks to determine the why — effectively estimating causality between events.

How prescriptive analytics works

Prescriptive analytics relies on artificial intelligence, and specifically the subfield of machine learning, which encomposes algorithms and models that allow computers to make decisions based on statistical data relationships and patterns.

For example, the Bayes classifier is a common machine learning algorithm that uses a statistical model called Bayes' Theorem to compute the conditional probability of an event happening. Another common (nonstatistical) machine learning algorithm is ID3, which creates a decision tree that structures a graph of possible outcomes from a dataset. In both the statistical and nonstatistical algorithms, the goal is to create a model from past data that can accept new inputs and predict their outcomes.

Data scientists must experiment with machine learning algorithms and features) to create a prescriptive analytics system, because different algorithms make different assumptions about the structure and completeness of data. For example, a linear regression assumes that the prediction variable can be modeled as a weighted sum of the descriptive features. However, not all data is linearly related and therefore the linear regression can't be applied to every data science problem.

Prescriptive analytics systems are not perfect and require close monitoring and maintenance. Data quality issues such as missing or incorrect information can lead to false predictions, and overfitting in models can lead to inflexible predictions that cannot handle changes in data over time. You should implement data quality standards and keep an eye on the models' predictions.


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Examples of prescriptive analytics

Businesses use prescriptive analytics to solve all sorts of real-world problems. Analysts in different industries can use it to improve their processes:

Marketing and sales

Marketing and sales agencies have access to large amounts of customer data that can help them to determine optimal marketing strategies, such as what types of products pair well together and how to price products. Prescriptive analytics allows marketers and sales staff to become more precise with their campaigns and customer outreach, as they no longer have to act simply on intuition and experience.

Transportation industry

Cost-effective delivery is essential for success and profitability in the package delivery and transportation industry. Minimizing energy usage through better route planning and solving logistical issues such as incorrect shipping locations can save time and money.

Shippers produce massive amounts of data. Rather than employing armies of analysts and dispatchers to decide how to best operate, these businesses can automate and build prescriptive models to provide recommendations.

Financial markets

Quantitative researchers and traders use statistical modeling to try to maximize returns. Financial firms can use similar techniques to manage risk and profitability.

For example, financial firms can build algorithms to churn through historical trading data to measure risks of trades. The resulting analytics can help them decide how to size positions, how to hedge them, or whether to place trades at all.

Additionally, these firms can use models to reduce transaction costs by figuring out how and when to best place their trades.

Making prescriptive analytics work for you

Prescriptive analytics can be invaluable for optimizing operations, growing sales, and managing risk. To operate effectively, however, the models and algorithms need a solid data pipeline to ensure that the data being fed into the models is up to date and accurate. Stitch provides a platform for integrating data into a data warehouse for analysis. Try Stitch for free and see how prescriptive analytics can help your business become more effective.

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