Predicting the future without paranormal powers. Find out in the article what is predictive analytics, which sectors most apply the technique of identifying future trends, but with statistical models and advanced mathematics. Although it seems like the perfect solution, the method has its advantages but there are points of attention.
Predictive analytics is not paranormal
The term predictive analytics refers to the use of statistics and modeling techniques to make predictions about future results and performance. Predictive analytics looks at current and historical data patterns to determine whether the pattern is likely to re-emerge.
This allows companies and investors to adjust the strategy where they use the resources to take advantage of possible events in the market. Predictive analytics can also be used to improve operational efficiency and reduce the risk involved.
how the model works
Predictive analytics is a form of technology that makes predictions about certain unknowns (variables) in the future. It relies on a number of techniques to make these determinations, including artificial intelligence (AI), data mining, machine learning, modeling, and statistics.
Predictive analytics models determine relationships, patterns, and data structures that can be used to draw conclusions about how changes to sequential processes will change outcomes.
Its basis is descriptive and analytical, using past data to determine the probability of certain future outcomes given current conditions or a set of expected future conditions.
For example, data mining involves analyzing large sets of data to detect patterns from them. They are useful for companies in helping with inventory management, developing marketing and forecast sales volume.
Executives and business owners can leverage this type of statistical analysis to determine customer behavior. For example, a business owner can use predictive techniques to identify and target regular customers who may switch their brand and go to a competitor.
It’s the survival of some companies, especially those in highly competitive industries like healthcare and retail. Investors and financial markets professionals can leverage technology to help build investment portfolios and reduce the potential for risk.
Positives of predictive analytics
There are numerous benefits to using predictive analytics. As mentioned above, using this type of analysis can help entities when predictions about outcomes are needed and when no other (obvious) answers are available.
Investors, financial professionals and business leaders can use models to help reduce the risk of operations. For example, an investor and his advisor may use certain models to help create an investment portfolio with minimal risk to the investor, taking into account certain factors such as age, capital and objectives.
There is a significant impact on cost savings when models are used. Companies can determine the likelihood of a product’s success or failure before launch. Another case is to set aside capital for production improvements using predictive techniques before manufacturing begins.
ep reviewspoints of attention
The technique is heavily criticized for possible misuse. These criticisms are more common when they involve predictive models that result in statistical discrimination against racial or ethnic groups in areas such as: credit scores, mortgage credit, employment, or risk of criminal behavior.
A famous example of this is the practice — made illegal — of redlining or isolation of neighborhoods in real estate loans by banks. The predictions extracted from using such analyzes are often accurate, but their use is frowned upon. Data that explicitly includes information such as a person’s race is now typically excluded from predictive analytics.
Knowing what predictive analytics is gives us the information that it is a tool without moral bias. The good or bad use of the technique is directly related to the agent that employs the analytical process.
With information: Investopedia, SAS.