Let the Data Drive Your Decisions With Prescriptive Analytics

If you have been following recent trends in data science software or business intelligence, the chances are high that you have run into the term “data-driven culture.” A data-driven culture is an organizational environment in which decision-making processes and workflow models are centered around big data. Every business generates a large amount of data every day. Harnessing the power of this big data is a fundamental factor in remaining competitive within a saturated market. So what exactly is big data? Big data refers to both categories of data, structured and unstructured.

Structured data is the data that resides within your current business intelligence model. This type of data may include customer contact information, inventory numbers, and other categories of historical data that typical business processes have generated. Unstructured data is everything else. Any input or output of data that is not organized or analyzed within a system can be referred to as unstructured data. Big data refers to both of these categories and is the root of all business intelligence processes, including prescriptive analytics.

Types of Data Analytics


There are three main data analytics types within an optimization model – descriptive analytics, predictive analytics, and prescriptive analytics. In a step-by-step process, prescriptive analytics would be considered the very last step. While the other two types of analytics focus on collecting and monitoring data, prescriptive analytics uses that data to create actionable insight. After data mining, descriptive analytics processes large amounts of data to identify trends and patterns. This data set is then transformed into pie charts, line graphs, or other visual representations on a dashboard for easier digestion.

This simplification of data allows even those without a working knowledge of data science technology to interpret operations research. The next step in the business analytics process is predictive analytics. This type of analytics references the data collected by descriptive analytics and calculates the probability of future scenarios. Prescriptive analytics completes this workflow by considering the possible implications of each decision option and creating a plan of action.

How do prescriptive analytics work?


The actionable insight provided by prescriptive analytics is achieved using mathematical and computer science statistical algorithms, free from human bias or error. Automated business strategizing is an excellent means of eliminating gut instinct reactions and decision-making, which can be harmful to organizations. Descriptive and predictive analytics lay the foundation for prescriptive analytics to turn solutions into action.

In other words, prescriptive analytics is the trademark of the successful use of the predictive model. Each form of analytics works together to consider all possible outcomes and the circumstances which led to them. Through this thorough examination of data, you can ensure your decision-making process is based solely on factual evidence and, therefore, highly accurate.

What types of organizations benefit from prescriptive analytics?


Nearly every industry has a use for prescriptive analytics. However, some have the opportunity to benefit more than others—for example, businesses that experience frequent fluctuations in supply, demand, or external influencing factors.

The real estate market is an excellent example of an industry that benefits from prescriptive analytics. The data generated from economic conditions, stock market, and buyer demand can be difficult to monitor without a business intelligence system in place. Taking each of these informational cohorts into consideration when making a decision is even more challenging. Prescriptive analytics facilitates better decision-making processes and takes some of the stress off of those in leadership roles.

The business intelligence field is experiencing rapid developments. Staying on top of the latest data science trends can be tricky! Thankfully, you can rely on data science software industry leaders like TIBCO to provide up-to-date, industry-tailored information regarding today’s most lucrative practices. A data-driven culture is an efficient one.

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