If you are planning to enroll into a business analyst training course, you would have to skim through tons and tons of data science terms that are ruling the roost in the industry. From automated AI ML algorithm modeling to plain business intelligence courses, there is a lot to handle through the curriculum. In the fast evolving business analyst training industry, there is one evergreen specialization you can trust if you are career oriented. It’s called Predictive Intelligence or Predictive Analytics.
Fact: 90% of the businesses have an analyst for their various operations, but only 2% of these are actually able to deliver on the ROI of the executive and tool budgets.
When asked about predictive analytics during my lectures, I refer to this data science specialization as the heart of any Big Data Project. Why?
But before starting, let’s learn the basics. And, we start with the definition first.
What is Prescriptive Analytics?
I have tried to provide the most contemporary definition of Prescriptive Analytics which you can use for many years to come.
In my interactions with the leading data scientists, they have defined prescriptive analytics as an advanced analytics model to find out the reasons and the forces or factors required to gain a desirable result. The prescriptive analytics techniques involve the use of Graph Analysis, Statistical Reasoning, Complex Event Processing, Neural Networking and the Convoluted Neural Networking, Recommendation Engines, and Automated Machine Learning.
Why organizations need Prescriptive Analytics?
Prescriptive Analytics is used by both data science and business analytics teams to proactively build a data-driven culture to influence and drive various digitalization developments and innovations across business analyst training.
Types of problem solved by Prescriptive Analytics:
Prescriptive analytics combs through various types of data to deliver a synchronized and coherent result that can be amalgamated as a single view, single version outcome.
It can be used to further differentiate between structured and unstructured data types using AI and Machine Leaning models. It helps to gain an optimal result whenever the ML algorithms are executed for data analysis of unstructured data. Prescriptive analytics in business parlance not only offers real time insights on what and when an event will happen, but also “how” and “where” it will occur.
Here are some of the very common problems that business analysts would use Prescriptive analytics to solve.
Manufacturing: Workers Productivity and Automation Budgets
The manufacturing sector is the top adoption center of business intelligence tools and dashboard. The prescriptive intelligence dashboards are employed to shed light on the individual and departmental productivity of each sub-group on the shop floor.
For example, in breakdown maintenance routine, prescriptive analytics can tell how much time and cost it would take to fix the broken machinery.
Warehousing
Prescriptive analytics can also fix all the recurring problems associated with warehouse management, including how much each warehouse should supply to the physical stores to ensure supply meets the market demand.
Healthcare
Healthcare sector is also using prescriptive analytics to ensure that each patient gets ample time with the doctor of their choice. The performance of each healthcare department is also optimized using electronic data reporting tools.
Energy consumption and utilities
Building management also employs advanced prescriptive analytics to find out what time of the day should a diesel generator be switched on or off to minimize energy consumption at the consumer level. Similarly, predictive and prescriptive analytics tools are used to optimize elevator performance and deliver the best experience at every floor.
Airline ticketing and flight management
Airline and tourism management companies are using prescriptive maintenance and analytics dashboards to reduce the customer churn across airport centrals, by delivering real time ticketing option and best discounts to retain loyal and punctual fliers. Off the bat, the automated airline ticketing and flight management / planning tools save an airline 40% of the costs that they were initially losing in a pre-AI ML era.
Conclusion
After passing from business analyst training course, you would be able to identify potential challenges in every organization. It allows the analyst to not only work with different types of data but also optimize and reproduce information with various techniques to generate results that would have the maximum impact on your data science models. In short, Prescriptive Analytics holds the power to transform data intelligence and wield you with enough actionable insights that could fix and heal gaps in the systems. You need 8 months of sincere theoretical knowledge and practical training with leading analysts.