Data analytics technology helps organizations make sense of an ever-increasing volume of data. As this technology matures, it gets better at delivering actionable insights and helping companies determine outcomes. Prescriptive analytics is a modern solution that builds upon other analytics technology and guides organizations to the right decisions for a particular situation.
Table of Contents:
- What is Prescriptive Analytics?
- Benefits of Prescriptive Analytics
- Weaknesses of Prescriptive Analytics
- How Prescriptive Analytics Compares to Other Types of Analytics
- Prescriptive Analytics Use Cases
- How to Prepare Data for Prescriptive Analytics
- How Extract, Transform, Load (ETL) Tools Complement Prescriptive Analytics
- How Integrate.io Can Help
What is Prescriptive Analytics?
Prescriptive analytics is an advanced form of data analytics that makes business decision recommendations. Complex algorithms and machine learning tools allow other tools to look at the available data and analysis to determine the actions that will lead to desired business outcomes.
It simulates the results of many possible decisions, allowing decision-makers to better understand the strategies needed to excel. This type of analysis requires a high level of technical skill and resources, so this solution is better suited to those in leadership positions than it is for those running daily operations. Essentially, it “prescribes” optimal courses of action.
Benefits of Prescriptive Analytics
Prescriptive analytics can deliver many business advantages to data-driven organizations. These benefits include:
A full understanding of the factors influencing decisions: Organizations gain more than simply a decision when they use prescriptive analytics. They can see what influences this action and why these factors matter, as well as learn when to act on the information. Even if a decision-maker passes over a particular option, they will have a greater understanding of everything that went into the recommendation.
Optimizing existing business strategies and processes: Current strategies may not be meeting business growth expectations. Sometimes the reasons behind the underperformance are obvious, but that’s not always the case. Leaders can leverage prescriptive analytics to gain greater insights into changes that can lead to meeting (or exceeding) current and future business goals.
Identifying new opportunities: Prescriptive analytics looks at a vast amount of internal and external data, which allows it to surface new opportunities that could benefit the business. These tools can pick up on new trends, shifts in the marketplace, and other changes that could be helpful to an organization.
Understanding the risks associated with potential decisions: Organizations and individual leaders have their own levels of risk tolerance. Prescriptive analytics solutions can identify potential risks and their severity, and make recommendations on mitigating them.
Speeding up the decision-making process: This technology can analyze massive data sets to extract the most important insights for business leaders. This streamlined process puts recommendations in front of the leadership faster, which speeds up the overall decision-making process.
Using machine learning to improve decision-making capabilities over time: As prescriptive analytics tools gain access to more data, they have more information to work with. This allows them to improve over time and become even more useful to the organization.
Weaknesses of Prescriptive Analytics
Prescriptive analytics is not a one-size-fits-all solution for an organization’s data analysis needs. It has several drawbacks to consider before an enterprise adopts this type of solution.
In order to support the machine learning capabilities, there must be large data sets: The tools can only work with the data that they have. If they need different information to determine good business decisions, then they won’t be able to work effectively. The data quality needs to be high, as recommendations based on poor-quality data can lead to negative consequences for an organization.
Working with these solutions requires having data specialists on staff. Prescriptive analytics tools feature advanced capabilities that are not ideal for the typical business user, or even junior data team members. Recruiting these specialists can be challenging, as data science is an in-demand field.
Prescriptive analytics tools are resource-intensive: Organizations need data lakes or data warehouses, data pipelines, and the infrastructure to support these solutions. The machine learning capabilities of this technology make greater compute demands than do simpler analysis solutions.
Managing the data sets required to support prescriptive analytics may be difficult. Large data sets are necessary to fuel analysis, but managing such large data volumes can be a struggle. Companies must consider everything from data quality to user access control. And it’s easy for a data lake to grow into an unmanageable mess rather quickly.
Tools can’t account for every potential factor. The data on hand can limit what these analytics tools can do, and unexpected variables can throw off recommendations.
How Prescriptive Analytics Compares to Other Types of Analytics
Prescriptive analytics isn’t inherently “better” than other analytic options. Each has its own place in a data-driven organization, so it’s important to know where to use each technology.
The other common types of data analytics are descriptive and predictive:
Descriptive data analytics is the most common tool used for day-to-day operations by business users. These tools look at historical data to create reports and other data visualizations that show already known insights and past happenings. Compared to prescriptive and predictive analytics solutions, descriptive tools are simple and don’t require specialized skills or massive data volumes.
In addition to informing day-to-day operations, descriptive reports allow organizations to track key performance indicators and business results. Those in charge can look at this information to see whether they achieved the results they expected from a prescriptive-driven decision.
Predictive analytics takes a future-looking approach to data analysis. This technology also uses historical data, but it leverages past data trends and other information to make predictions. It surfaces many possibilities, which can help drive management decision-making and forecasting. Some of the use cases for this type of information include planning inventory replenishment, staffing customer service lines, and improving the supply chain.
Prescriptive analytics takes a complementary position alongside descriptive and predictive tools. All three can work together in concert to help an organization sustain growth and continually improve operations.
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Prescriptive Analytics Use Cases
Prescriptive analytics offers a variety of use cases across many industries. A few examples include:
Self-driving cars: Autonomous vehicles operate in ever-changing conditions, and mistakes can be a matter of life and death. While this technology is not yet mature, semi-autonomous driving features are becoming more commonplace on vehicles. Prescriptive analytics ingests and analyzes massive data sets to output the driving recommendations.
GPS navigation: GPS tools must look at historical and current road conditions to help drivers take the best route to their destination. Traffic, obstacles, and severe weather can influence which route is the fastest or which meets the criteria set by the driver. This process continually evaluates the data so it can update as soon as possible.
Insurance risk assessment: Insurance approvals and pricing leverage a complex model that examines the risk of an applicant. Determining whether an individual meets the requirements for an insurance product and how much it should cost requires looking at both historical and predictive data.
Tracking patient outcomes in hospitals: Hospitals can use prescriptive analytics to allocate sufficient resources, improve patient care and determine the best treatment plans to achieve the desired results. Medical data includes hundreds of factors that can influence decisions, and trying to consider all of this information in a fast-paced environment is challenging. Prescriptive analytics automates this process to assist hospital administrators. By making these improvements based on available data and advanced machine learning recommendations, hospitals can help more people and save more lives.
How to Prepare Data for Prescriptive Analytics
Organizations need to get data from the sources to a location that the prescriptive analytics tool can work with. Typically, this is a cloud-based data lake or data warehouse for storing the data in one place, which allows the solution to have full access to the information it needs to make recommendations.
Before the data loads into the data store, it needs to be prepared. The exact steps in this process depend on the type of solution the company uses, how many data sources it has, the formats required by the analysis tool, and the available technical resources.
Cleansing the data and taking other steps to improve its quality leads to better recommendations and more cost-effective data storage. For example, by deduplicating data, before it goes to a data warehouse, an organization can drastically reduce the overall storage space needed.
Many organizations possess more data than they can manually move between databases, applications, and the data store. Data pipeline tools automate many parts of this process, but companies need the right solution to support the robust data needs of prescriptive analytics.
How Extract, Transform, Load (ETL) Tools Complement Prescriptive Analytics
ETL solutions simplify many parts of the data preparation and transfer through a three-step process. They automate extracting some or all data from databases, applications, platforms, and other sources. ETL solutions can do this on a regular basis or as a batch process, depending on how often the data needs to move to the data warehouse and how much it is updated.
The next step, transform, performs all the steps needed to make the data ready for the prescriptive analytics solution. This automated transformation can cleanse the data, mask sensitive data, remove specific data from a set and perform many other functions. By taking care of data transformation in the pipeline itself, organizations can start using the data immediately.
Finally, the data loads into the data lake or data warehouse. The prescriptive analytics tool has a continual source of high-quality data, allowing it to make better recommendations to business leaders.
How Integrate.io Can Help
Not all ETL tools can support the needs of prescriptive analytics solutions. A modern analytics tool needs a modern ETL solution such as Integrate.io. Integrate.io’s cloud-based ETL platform offers organizations the functionality they need to get the most out of a prescriptive analytics investment.
It’s time to get the information your organization needs to make the best data-driven decisions. Find out how Integrate.io can help with prescriptive analytics when you take advantage of our 14-day demo.
