How to Avoid Predictive Analytics Mistakes

Five Critical Predictive Analytics Mistakes (and How to Avoid Them)

March 3, 2022

The business world is increasingly in love with all things AI. Included in this is the increasing demand for predictive analytics among enterprise companies. In fact, according to research firm Markets & Markets, demand for predictive analytics is expected to grow to an impressive US$28B by the year 2026. Forecasts are often educated guesses, but if demand for data scientists (the specialists needed for most predictive analytics projects) is any indication, the estimates might just be on target. In fact, in 2021, the demand for data scientists, as measured by job openings, grew by over 250% over 2020.

Yet, with all the need for machine learning and predictive analytics, the reality is that over 87% of machine learning projects still fail. The past five years have seen a flurry of activity in the world of machine learning and predictive analytics with new tools that promise to make predictive analytics simple for everyone.

While that is almost true, there are still many pitfalls to adopting predictive analytics that, if not heeded, will place you among the 87% of failed projects instead of the 13% that succeed. Here are the top 5 mistakes to avoid – and how to avoid them:

1. Not aligning your business use case with your predictive analytics models 

Predictive Analytics relies on machine learning to build the necessary predictive models. A telling stat from VentureBeat is that nearly 90% of machine learning models never make it into production. There are many reasons for this, but one of the most important is that often predictive models have not been developed in cooperation with business users but instead have been built as experiments from available data.

A critical step to ensuring your adoption of predictive analytics is successful is to align your business needs with your machine learning development. Not all business problems need immediate solutions. For example, you may have plenty of valuable data to analyze and predict churn. Still, if your company is satisfied with the level of churn, you may have a hard time building support for implementing a churn prediction model.

A second critical alignment is determining if the business is willing and has the resources to solve a problem identified by a predictive model. Your predictive analytics solution may provide valuable insights into optimizing digital marketing campaigns to maximize Return on Ad Spend (ROAS). Still, if your digital marketing team is stretched to capacity and does not have the bandwidth to deal with the issue, your model will likely fall on deaf ears.

Finally, and equally important, make sure that you know what data is available for the model in question and that you have ready access to the data. 

Key Takeaways:

  • Plan your predictive analytics projects based on critical business challenges and essential opportunities to your Line of Business leaders.
  • Ensure that the organization has the resources and willingness to solve the problems once your predictive analytics models have identified them.
  • Make sure you have data available to build predictive models for the business challenges you are trying to address.

2. Assuming your data is “just fine.”

Knowing that data exists in your organization to build a predictive model is only half the battle. Companies just getting started with predictive analytics often have challenges with a second part of the data readiness issue – data quality and state.

Predictive analytics relies on machine learning. Machine learning algorithms require a flat table to build predictions. While that may seem simple enough, data in the enterprise seldom live in simple, organized flat tables. Most businesses have large data repositories in multiple states and readiness, even when using data lakes and warehouses. Your most valuable data is probably trapped in a complex series of relational data structures that require complex SQL code to transform into clean “machine learning” ready flat tables.

See it in Action

See the power of Feature Engineering Automation with a personal demo.

An additional challenge to success in predictive analytics is data cleanliness. Enterprise data is seldom neat and prepared for any form of analytics. Cleaning data for traditional Business Intelligence applications is a well-understood need and is often part of many organizational data management processes. However, making your data ready for predictive analytics is an entirely different problem. Data quality issues that are often not considered “deal breakers” for BI applications can significantly impact the quality of your predictive analytics models. Preparing your data for AI means identifying missing values, values that may skew your models, and a whole host of other challenges unique to the world of Machine Learning.

Key Takeaways:

  • Enterprise data exists in complex relational databases and flat files that you must combine into flat tables ready for Machine Learning.
  • Data that is “clean enough” for Business Intelligence is often not ready for Machine Learning algorithms and requires further cleanup before it becomes usable for ML.

3. Jumping on the No-Code AI bandwagon too eagerly
(or assuming a data science platform is the answer)

The past few years have seen a flurry of activity in the world of predictive analytics and machine learning. New tools and platforms are available that promise to make building predictive models fast, simple, and (some claim) simple enough even for business users.  The truth is that there are a plethora of so-called “No-Code AI” tools in the market, many have specific limitations that are required to make them simple enough for anyone to use, but that also, by design, limit their usefulness.

To begin with, the majority of tools require a flat table as the primary data input. These tools also typically require that the data be already “clean” and ready for your AI model. As discussed earlier, this generally is not the case – so make sure you plan for how you will build your “AI-ready” flat table before you invest in a predictive analytics solution.

Another complication is that many assume that using a No-Code tool means, by definition, that you do not need to be familiar with Machine Learning concepts and terminology. While some of the complexities of Machine Learning can be abstracted by No-Code tools, understanding the fundamentals of how predictive analytics platforms build your predictions is still crucial. Predictive Analytics and Machine Learning require something called “features.” Understanding which features are most relevant to a model – and why – are fundamental concepts.

The relevancy of features helps avoid the continuous frustration of not knowing why your models are not performing as expected. Finally, it’s also critical to understand that success breeds growth. In the case of predictive analytics, building a successful model that impacts your business will inspire your company to request new models.

Many No-Code platforms support limited use-cases. Ensure that whatever tool you choose can support a wide range of use-cases that will support your business as your predictive analytics needs grow.

Finally, the opposite challenge can also be a problem – settling on an advanced platform designed for data scientists. The role of automation for the data science community is different than when attempting to enable non-data science teams (like your BI team) with predictive analytics. Make sure your platform of choice is designed for your intended audience and is not overwhelming.

Key Takeaway:

  • Make sure your No-Code platform can support relational database structures as well as flat tables – and that it can provide data cleansing and prep capabilities aimed at ML.
  • Get a good understanding of the fundamentals of Machine Learning. Learn about features and the Machine Learning process to better understand why models sometimes don’t work as predicted.
  • Make sure your platform of choice supports the right target user in your company – that it’s not overly complex but that it can also scale with your business as your needs and use-cases grow.

4. Avoiding the Operationalization (MLOps) discussion

Another critical challenge for many new organizations to predictive analytics is not thinking about how your predictive analytics models will be deployed and used by your Line of Business users. Operationalization (also known as MLOps) of predictive analytics models is one of the biggest challenges of successfully leveraging the benefits of predictive analytics.

The first question to answer is how do you intend to use your model? Are you looking to integrate it into a BI dashboard? Are you trying to integrate your predictions into an end-user-facing system (like Salesforce)? Depending on the answer, your deployment methodology and options will differ. Once again, choosing the right platform and the partner is critical in ensuring that your models deploy successfully.

A second and essential part of model deployment is performance monitoring. Unlike BI implementations, your predictive model heavily depends on the data fed into it. Model “drift,” as it’s often known, happens when the data fed into your predictive model is no longer representative of the data you used to train your model. Model drift can happen for several valid reasons – your business may be changing, economic conditions may impact your business or a pandemic may have completely changed the way your company operates. Regardless of the reason, you need to ensure that you have a good model monitoring and retraining strategy in place to keep your predictive models working at their best.

Key Takeaways:

  • Operationalizing your model can take many forms. Think about how you want line-of-business users to leverage your predictions and where those predictions will live.
  • Your data changes as your business changes and economic conditions change; having a solid model performance monitoring and retraining strategy is critical of success. 

5. Not thinking about “What’s next?”

Building and deploying your first Machine Learning model has become significantly easier to do. The tools available have become more accessible, and the amount of coding required has dropped by orders of magnitude.  As a business, however, you need to think long-term in the world of predictive analytics, which means thinking about how your Machine Learning practice will evolve.

For starters, what went right about your first model and what did not? What challenges did you have? Learning from your first model is a critical step in ensuring that your model-building practice improves as time goes on.

Ensure that you have a thorough debrief for each project with all involved stakeholders. What other use-cases are available that are important to your business? Having a ready pipeline of ideas and use-cases to explore is an equally important step in ensuring that you maintain momentum. Understand the needs of your business units and work with each to build a pipeline of projects that will keep your team engaged.

Lastly, what is the long-term strategy for predictive analytics and machine learning? Will your BI team retain the lead in the area? Do you want to establish a data science practice in-house? Can your existing vendor assist you in this determination and provide tools to support both? Knowing how to scale your practice will ensure that your long-term chances of success remain high.

Key Takeaways:

  • Debrief at the end of each project – what went right? What challenges did you have? How would you do things differently next time? Include all your stakeholders.
  • Have a project pipeline. Align with business units to determine their critical projects and prepare for ongoing work to keep your team, and your company engaged.
  • Have a long-term plan. How will you use predictive analytics long-term? Do you want to build a data science practice in-house? Can your vendor scale with your needs as you grow?

Putting it all together

Getting started with predictive analytics as little as five years ago may have seemed a daunting and costly proposition. The advent of No-Code and Low-Code AI platforms has made the barrier to entry significantly smaller for even small and mid-sized companies. There are, however, risks associated with embarking on a Machine Learning initiative without understanding the basic tenets of success.
From aligning your predictive analytics projects with key business initiatives to having the proper balance between a no-code approach and a practical understanding of how machine learning operates, finding the right mix of software, training, and services to complement your strategy. Learn more about dotData’s No-Code ML tools and our approach to helping clients that are just starting with predictive analytics and machine learning.

Share On

Walter Paliska

Walter Paliska

Walter brings 25+ years of experience in enterprise marketing to dotData. Walter oversees the Marketing organization and is responsible for product marketing and demand generation for dotData. Walter’s background includes experience with both software and hardware companies, and he has worked in seven different startups, including three successful bootstrap startups.