Pros and Cons of Predictive Analysis

When it comes to technology management, planning, and decision making, extracting information from existing data sets—or predictive analysis—can be an essential business tool. Statistical methods and predictive models are used to examine existing data and trends to understand customers and products better while also identifying potential future opportunities and risks.1

These business intelligence models create forecasts by integrating data mining, machine learning, statistical modeling, and other data technology.

Who uses predictive analytics?

Technology management teams in marketing, finance, insurance, retail, tourism, healthcare, pharmaceutical, and utility companies alike rely on predictive analytics to plan for the future or improve the customer experience. Take Netflix and Amazon, for example. Netflix learns which movies viewers are likely to enjoy. Amazon predicts what a customer will buy—even going as far as to patent “anticipatory shipping,” which would deliver packages to a geographic region before a customer buys them.2

Benefits and Advantages of Forecasting and Predictive Analytics

In its multiple forms—predictive modeling, decision analysis and optimization, transaction profiling, and predictive search—predictive analytics can be applied to a range of business strategies and has been a key player in search advertising and recommendation engines.3 These techniques can provide managers and executives with decision-making tools to influence upselling, sales and revenue forecasting, manufacturing optimization, and even new product development. Though beneficial, predictive analytics has notable disadvantages.

Drawbacks and Criticism of Predictive Analytics

A company that wishes to utilize data-driven decision-making needs to have access to substantial relevant data from a range of activities, and sometimes big data sets are hard to come by. Even if a company has sufficient data, critics argue that computers and algorithms fail to consider variables—from changing weather to moods to relationships—that might influence customer-purchasing patterns when anticipating human behavior.

Time also plays a role in how well these techniques work. Though a model may be successful at one point in time, customer behavior changes with time and therefore, a model must be updated. The 2008 financial crisis exemplifies how crucial time consideration is because invalid models were predicting the likelihood of mortgage customers repaying loans without considering the possibility that U.S. housing prices might drop.4

A thorough understanding of predictive analytics can help you with business forecasting, deciding when and when not to implement predictive methods into a technology management plan, and managing data scientists.

Learn more about data analytics, big data, predictive models, and other helpful technology management tools by applying to Georgetown University’s online Master’s in Technology Management program.

Sources

  1. "Predictive Analytics," Webopedia. Retrieved on March 9, 2022 from https://www.webopedia.com/definitions/predictive-analytics/.
  2. "Amazon Wants to Ship Your Package Before You Buy It," The Wall Street Journal. Retrieved on March 9, 2022 from https://www.wsj.com/articles/BL-DGB-32082.
  3. "Predictive Analytics: How to Forecast the Future," Clever ISM. Retrieved on March 9, 2022 from https://www.cleverism.com/predictive-analytics-forecast-future/.
  4. "A Predictive Analytics Primer," Harvard Business Review. Retrieved on March 9, 2022 from https://hbr.org/2014/09/a-predictive-analytics-primer.