The Ultimate Guide to Prescriptive Analytics
What is Prescriptive Analytics? Prescriptive analytics applies mathematics and logic to data to describe a specific course of action. Also, prescriptive analytics has the huge potential to suggest options in decision making that further helps in taking advantage of a future opportunity or mitigating risk in the future and explaining the impact of every option [...]
What is Prescriptive Analytics?
Prescriptive analytics applies mathematics and logic to data to describe a specific course of action. Also, prescriptive analytics has the huge potential to suggest options in decision making that further helps in taking advantage of a future opportunity or mitigating risk in the future and explaining the impact of every option of the decision. Prescriptive analytics techniques can automatically process fresh data to enhance the accuracy in forecasting and can make the decision-making process more efficient. Additionally, prescriptive analytics assists organizations to achieve business objectives such as high profits, better customer service, and operational efficiency. Prescriptive analytics solutions utilize optimization technology to solve complicated decisions with millions of decision constraints and variables. They provide actionable insights into every aspect of the decision-making process.
Prescriptive analysis helps companies to improve business decisions and the effectiveness of actions. Want to know how? Get in touch with our experts now.
Descriptive Analytics Vs Predictive Analytics Vs Prescriptive Analytics
Descriptive analytics uses statistical analysis, to describe and summarize the data using simple and complex statistical analysis techniques. By employing the methods of aggregation, filtration, and statistics, descriptive analytics describes data using means, counts, percentages, sums, minimum or maximum values, and other descriptive values to help in understanding the data. Descriptive analytics is helpful in telling what has happened or what is happening now.
While descriptive analytics aims at analyzing the past, predictive analytics focuses on utilizing the same historical data to build predictive models that can be utilized to make inferences about the future. Predictive analytics tools analyze the historical data as there may be patterns within those data that can help in making better decisions while interacting with customers in the future. There are basically two subclasses; explanatory predictive models and purely predictive models in within predictive analytics.
Prescriptive analytics techniques go a step beyond predictive analytics as the objective here is to identify the best actions to achieve a goal. Prescriptive analytics is basically based on modeling data to understand what could happen and, eventually suggest what the next step should be based on previous steps taken. In prescriptive analytics, analysts can build models using machine learning, data modeling, and complex statistical methods to forecast possible outcomes. Prescriptive analytics techniques are generally used in supply chain operations and routing where decision-making for a human is too complex and difficult to manage.
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Benefits of Prescriptive Analytics
Improves revenue generation
Prescriptive analytics helps in improving revenue generation by providing more detailed and timely information on customers, their buying habits and factors driving the purchase. Also, it empowers businesses to accelerate the cycle of sales and identify new cross-selling opportunities. This can further help businesses to realize a significant ROI for their investment in advanced analytics capabilities.
Gross margin management
Prescriptive analytics tools provide insights into current and anticipated market conditions. This further assists in experiencing higher profitability and productivity.
Prescriptive analytics allows for better inventory management. This reduces the cost of storing inventory for a longer period of time and minimizes manual costs and processes. Also, it provides better control and visibility of expenses.
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Five Pillars to Prescriptive Analytics Success
#1: Hybrid data
Today most businesses run on structured data in all categories. According to recent research, 80 percent of the data produced is unstructured i.e., in the form of image, text, audio, and video. While some businesses may choose to run on unstructured data, doing so could render them non-competitive and unproductive. These businesses may face difficulty in survival as their suppliers, customers, and competitors move beyond them by completely utilizing hybrid data, a combination of structured and unstructured data. Hybrid data empowers businesses to make better decisions by utilizing all the available data. Prescriptive analytics technology can be transformative only when it is able to process hybrid data.
#2: Integrated prescriptions and predictions
Prescriptive analytics techniques help in seeing and then shaping the future. The predictions and prescriptions functions must work in synchronization for prescription analytics to deliver on what it promises. Therefore, symbiotic integration of prescriptions and predictions is the golden key to inherent values of prescriptive analytics.
#3: Prescriptions through a guided framework
Prescriptions in the prescriptive analysis are generated using different methods. A simple and general method of coming up with prescriptions is through a guided framework of business rules. This framework can be complex or simple, depending on the process of business or the initiative that is being governed by prescriptive analytics. A more convenient and scientific way to produce prescriptions to boost decision-making in the future is through operations research (O.R.), the science of data-driven decision-making. But for a prescriptive analytics technology to scale, the solution should use both operations research and business rules synergistically. Only then this technology will be able to generate the most timely and effective prescriptions.
#4: Adaptive algorithms
In the era of growing volume, variety and velocity of data, the prescriptive analytics technology must be able to automatically create new algorithms and recalibrate all its built-in algorithms automatically. This complete recalibration needs to be adaptive in order to successfully help the business process. Recalibration of the algorithms in the predictive analytics tools can be triggered in different ways like with the arrival of new data, after a specified time period, with data changes, and a lot more.
#5: A Feedback mechanism
The feedback mechanism is a very important pillar for the prescriptive analytics as it tells whether prescriptions are being acted upon. There is one drawback to prescriptive analytics that prescriptive analytics software still today needs human assistance to carry out the prescriptions. Visualize a future where it will become a fully embedded and integral component of the business process.
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