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The 4 Common Challenges of Predictive Analytics

By Sriram Parthasarathy | July 10, 2018

Predictive analytics is a branch of analytics that uses historical data, machine learning, and Artificial Intelligence (AI) to help users act preemptively. Predictive analytics answers this question: “What is most likely to happen based on my current data, and what can I do to change that outcome?”

>> Related: What Is Predictive Analytics? <<

Predictive analytics has become much more prevalent over the past few years. It aids banks in approving credit or detecting suspicious activity, e-mail providers in filtering spam, and retailers in predicting customers’ likelihood to churn out or purchase products.

But predictive analytics is a complex capability, and therefore implementing it is also complicated and comes with challenges. When companies take a traditional approach to predictive analytics (meaning they treat it like any other type of analytics), they often hit roadblocks.

4 Common Predictive Analytics Challenges and Possible Solutions

Expertise

Expertise is a challenge because predictive analytics solutions are typically designed for data scientists who have deep understanding of statistical modeling, R, and Python. This is inherently limiting. In fact, most application teams can’t even begin to approach predictive analytics without first hiring a dedicated data scientist (or two or three!).

Solution: Fortunately, you don’t have to settle for a limiting solution. Today, new predictive analytics solutions are emerging, and they’re designed for almost anyone to use. Most importantly, they don’t require expertise in statistical modeling, Python, or R.

Adoption

It’s not a secret that the more difficult a new technology is to use, the less likely end users are to adopt it—and predictive analytics solutions are notoriously difficult in meeting this challenge. This is because they typically live as standalone tools, which means users have to switch from their primary business application over to the predictive analytics solution in order to use it. What’s more, traditional predictive tools are hard to scale and deploy, which makes updating them a painful process.

Solution: Predictive analytics is most effective when it’s embedded inside the applications people already rely on. Embedding machine learning and AI inside your application gives you a huge strategic advantage over the competition—and gives your end users a strategic advantage for their businesses.

Empowering End Users

No information is valuable in a vacuum. And that’s one of the reasons predictive analytics has fallen short in empowering end users. Often, predictive tools deliver information and insights, but they fail to let users take action. As we discussed above, if users wants to act on the data, they have to jump to yet another application—ultimately wasting time and interrupting their workflow.

Solution: By embedding intelligence workflows into your regular business applications, you’ll empower your users to take immediate action or trigger another process—saving them a lot of time and frustration.

Burdensome Project Lists

Every predictive analytics project requires an extensive list of steps, which are almost always handled by a dedicated data scientist. The challenge is that for every update and release, these steps place more of a burden on your application team. They include:

  1. Data prep
  2. Data cleansing
  3. Identifying important columns
  4. Recognizing correlations
  5. Understanding how different algorithms (math) work
  6. Choosing the right algorithm for the right problem
  7. Deciding the right properties for the algorithm
  8. Ensuring the data format is correct
  9. Understanding the output of the algorithm run
  10. Re-training the algorithm with new data
  11. Dealing with imbalanced data
  12. Deploying/re-deploying the model
  13. Predicting in real time/batch
  14. Integrating with your primary application to build data insights into the application and initiate user action (when embedding predictive)

Solution: Some predictive analytics solutions shoulder many of these steps rather than placing the burden completely on your team. By choosing one of these more streamlined predictive analytics solutions, you can turn a 14-plus-step process into a three-step process.

For more information on innovative analytics capabilities, read our ebook: The 5 Levels of Analytics Maturity >

 

About the Author

Sriram Parthasarathy is the Senior Director of Predictive Analytics at Logi Analytics. Prior to working at Logi, Sriram was a practicing data scientist, implementing and advising companies in healthcare and financial services for their use of Predictive Analytics. Prior to that, Sriram was with MicroStrategy for over a decade, where he led and launched several product modules/offerings to the market.

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