Complex systems connect to and analyze disparate datasets to give human users a clearer picture of the world around them and possibly a glance at the future.

Sadly, many companies are stuck using outmoded analytics that give them static, historical reports that only describe what has already happened and are useless in planning for the future. No one can expect to unveil what’s coming with absolute certainty, but even having some idea of what to expect next quarter or next year can evolve a business and transform an industry. 

Predictions like those, indeed predictive analytics itself, rely on a deep understanding of the past and present, expressed by data. New to the idea of predictive analytics? Let’s run through the theory and practice of this evolving field.

Defining predictive analytics

Predictive analytics use data to create an outline of the future. Advanced, complex systems use historical data to establish patterns and then use those patterns to give human users an idea of what’s to come.

Data scientists have developed multiple predictive analytics models, each tailored to different functions:

  • Forecast models use multiple input types to estimate future outcomes: how long an engine component will last, how many in-store customers to expect on a given day, how much of an item you should keep in inventory, etc.
  • Classification models use data to sort information and are especially helpful in answering yes/no questions: predicting which customers or employees are most likely to churn, etc.
  • Outlier models alert users to data points that don’t correspond with predictions, since those could be reasons for concern: Anomalies like a store’s unexplained dip in sales or a sudden spike in email traffic might highlight something to look into — like malfeasance, poor performance, or fraud.
  • Time series models take regularly changing conditions into account: Holidays like Halloween and Christmas come each year, and decoration manufacturers consider that in their models; other businesses pay attention to other predictable cycles, including election seasons, the Olympics, and astronomical and weather events.
  • Clustering models sort data into smaller groupings so users can address them in targeted ways: Sending different messages to clients at different stages in a sales funnel is a good use of clustering.

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Why use predictive analytics?

Predictive analytics aren’t a crystal ball, but their benefits are transforming nearly every industry. Businesses with a more-precise picture of the future are exponentially better off than those planning based on the last 90 days or year. These companies can now make decisions informed by copious granular details instead of vague guesses based on outdated information.

At their best, predictive analytics infuse intelligence directly into workflows, automatically guiding users to take the right action at the right time to build the future they want. Executives can also use predictive analytics to guide the larger course of a business’s decisions and direction.

How do you lay the foundation for a successful predictive analytics program?

A successful predictive analytics rollout requires a few key elements:

  • The right data sources: Do you have the data you need? If not, how can you find it?
  • Useful, clean data: Build a model incorporating data that’s relevant to the problem under consideration, scrubbed of inaccuracies, duplicate entries, formatting issues, or other deficiencies.
  • Automation and machine learning: Large, complex datasets quickly outstrip human capabilities and require massive computing power to make sense of them.
  • Tie-in to business objectives: Predictive analytics don’t exist for their own good. They must support broader business goals.

Who uses predictive analytics?

One of the most powerful aspects of predictive analytics is that, when executed properly, they can benefit a varied user base. Nontechnical users will glean deep intelligence about possible future circumstances without the aid of technical teammates on the data team or IT department. Throughout virtually every vertical, from the C-suite to line-level employees, decisions and workflows informed by predictive analytics become more efficient and effective.

Where can predictive analytics be used?

Predictive analytics have shown potential in countless industries and focus areas within businesses. Here are a few examples:

  • Retail and marketing: The right intelligence infused into back-office and front-line user workflows inform decisions around changing sales goals, adjusting marketing and advertising campaigns, and optimizing inventory. 
  • Manufacturing, supply chains, and logistics: Predictive analytics can help manufacturers determine how much of which items to produce, when to change suppliers or materials, and which transport lines or providers might be most appropriate going forward. They can also use historical trends and current customer feedback to improve design and planning.
  • Human resources: Companies work hard to recruit the best talent and craft compelling employee benefits packages — but instead of hunches, predictive analytics can help HR teams keep employees happier as margins tighten for businesses of all kinds. Predictive analytics can also give HR professionals a heads-up on team members who might be looking to exit soon and help inform policies and benefits that could keep them around longer.
  • Healthcare: The COVID-19 pandemic has proven the value of predictive analytics, including modeling how the disease would spread. Nonemergency applications of predictive analytics in healthcare could include predicting hospital facilities utilization, modifying pharmaceutical formulas, or creating insurance plans.

Seeing and creating the future with predictive analytics

If predictive analytics are the holy grail of business intel, there’s one last thing to say about them: Unlike the grail, they’re here, and organizations are taking advantage of them now. Some companies will collect and synthesize data but remain focused on the past. Others, though, will take a step forward, using that data to peer days, months, even years ahead.

If you’re ready to look into the future, ask some questions: What data do you have now to feed your predictive analytics system? What other data do you need? And then: What’s keeping you from getting started?

Free analyst report from Harvard Business Review:

>> Read about the state of analytics and obstacles that hinder progress

Get Free HBR Report

Carmen DeCouto developed a passion for data as a customer before joining Sisense. With over seven years of experience in a variety of technologies, Carmen is dedicated to empowering advanced data teams as they tackle the next wave of industry-redefining challenges.

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