The Imminent Future of Predictive Modeling
The software company SAS delivers certification via a performance-based examination in which candidates use SAS Enterprise Miner. Candidates should comprehend the functions for predictive modeling available in SAS Enterprise Miner.
A predictive modeler draws order and context from the mess of large datasets. Data can collect intelligence on products, customers, and competitors in a business setting. The modeler’s work results are accurate information in which designs have been identified. Part of the job is negotiating with data as you find it and then cleaning it up and organizing it to meet the task at hand.
The data comes from the past, things that have occurred. The predictive modeler can remember replicating patterns that indicate that the same patterns will persist in the future by searching through the data. The modeler may notice a slight rise in sales of a product at the same time each year, and the company can parlay this information into specials or coupons to drive those sales even higher.
First, the predictive modeler collects data, using what’s suitable and throwing off what’s not. Some jobs may be as simple as exporting data to a spreadsheet so that other employees can gain entry to the information.
Why Is Predictive Modeling Important?
Businesses have used predictive analytics for decades. With some sales data and a spreadsheet, executives have recognized patterns that have permitted them to forecast when and where sales will rise and fall.
Over the years, the amount of data available to predictive modelers has increased dramatically. Consumer companies have more information about customers from compensation programs and social media mentions. Computer chips and sensors have been implanted in industrial machinery, so manufacturers can draw on vast amounts of data to track long-term product performance. In health care, anonymized patient data enables health care providers, pharmaceutical companies, and insurers to home in on effective treatments and therapies.
Predictive analytics applies precise manipulations on existing data sets to identify new trends and patterns. These are then used to predict future outcomes and trends. By conducting predictive analysis, we can predict future trends and performance. It is also described as predictive analysis, the word prognostic means prediction. Predictive analytics uses data, statistical algorithms, and machine learning techniques to identify the probability of future outcomes based on historical data.
The Future of Predictive Modeling
Predictive modeling also comprehended as predictive analytics and machine learning, are still young and developing technologies, meaning much more to come. As techniques, methods, tools, and technologies enhance, so will the advantages to businesses and societies.
However, these are not technologies businesses can afford to adopt later, after the tech reaches maturity and all the waves are worked out. The near-term advantages are too significant for a late adopter to overcome and remain competitive.
Understand and deploy the technology now and then open the business benefits alongside technological advances.
Predictive Modeling in Platforms
For all but the most famous companies, reaping the benefits of predictive analytics is most easily accomplished by using ERP systems that have built-in technologies and contain pre-trained machine learning. For example, planning, forecasting, and budgeting features may provide a statistical model engine to model multiple scenarios that deal with changing market conditions rapidly.
As another example, a supply planning or supply capacity function can similarly predict potentially late deliveries, purchase or sales orders, and other risks or impacts. Alternate suppliers can also be represented on the dashboard to enable companies to pivot to meet manufacturing or distribution requirements.
Financial modeling, planning, and budgeting are vital areas to reap the many benefits of using these advanced technologies without overwhelming your team.
Combating Cultural Resistance
Many companies experience push-back when introducing predictive modeling. Management is often the first group to submit objections, fearing that the developments will be complicated to interpret and therefore be unusable. Those creating the models must guarantee that they are put in the appropriate form and business context, which will make them intuitive and highly relevant.
Operational users are another group that is often slow to adopt predictive modeling. They worry the result will be unreasonable, uncovering mistakes in past decisions and proving their gut instinct wrong. It is essential to demonstrate that predictive modeling will not hold them accountable or double-check their choices but offer proper guidance when making those decisions.
Summary
By allowing everyone to plan for the future more intelligently, a predictive modeling environment, which is included within a business intelligence infrastructure and widely adopted by operational workers, can empower companies to work more efficiently and profitably than ever before.
