The 4 Common Predictive Analytics Challenges and Solutions
Predictive analytics challenges are a common hurdle for organizations looking to leverage data for strategic decision-making. These challenges can range from the expertise required to implement solutions effectively, to ensuring widespread adoption among end users. Understanding and addressing these predictive analytics challenges is crucial for maximizing the value of your data and achieving successful outcomes.
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. There are many examples of how predictive analytics is used in different industries.
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
Predictive analytics challenges often stem from the need for specialized expertise, difficulties in adoption, limited empowerment of end users, and burdensome project requirements. Addressing these issues with modern solutions can help organizations streamline processes and maximize the value of predictive analytics.
1. Expertise: The Barrier to Entry
Challenge:
Expertise is a significant challenge in predictive analytics because these solutions are typically designed for data scientists who possess deep knowledge of statistical modeling, R, and Python. This creates a barrier to entry for many organizations, as most application teams cannot begin to approach predictive analytics without first hiring a dedicated data scientist—or even several.
Solution:
Fortunately, you don’t have to settle for a limiting solution. Today, new predictive analytics tools are emerging, designed to be user-friendly and accessible to a broader audience. These modern solutions eliminate the need for expertise in statistical modeling, Python, or R, making predictive analytics more approachable for business users and other non-technical stakeholders.
2. Adoption: Overcoming Resistance to Change
Challenge:
It’s no secret that the more difficult a new technology is to use, the less likely end users are to adopt it. Predictive analytics solutions face this challenge because they often exist as standalone tools. This means users must switch from their primary business application to the predictive analytics solution, creating friction in their workflow. Additionally, traditional predictive tools are hard to scale and deploy, which complicates the process of updating and maintaining them.
Solution:
Predictive analytics is most effective when embedded within the applications people already use and trust. Embedding machine learning and AI capabilities directly into your primary business applications offers a significant strategic advantage. This not only enhances user adoption by reducing the need to switch between tools but also streamlines processes and improves user experience.
3. Empowering End Users: From Insight to Action
Challenge:
No information is valuable in a vacuum. One of the primary shortcomings of traditional predictive analytics tools is their inability to empower end users to take action on the insights provided. Often, users receive valuable data but must switch to another application to act on it, which disrupts their workflow and leads to inefficiencies.
Solution:
By embedding intelligence workflows into your regular business applications, you empower users to take immediate action based on the insights provided. This integration saves time and reduces frustration by allowing users to trigger processes directly within the same platform where they receive the data, streamlining their workflow.
4. Burdensome Project Lists: Simplifying the Process
Challenge:
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:
- Data prep
- Data cleansing
- Identifying important columns
- Recognizing correlations
- Understanding how different algorithms (math) work
- Choosing the right algorithm for the right problem
- Deciding the right properties for the algorithm
- Ensuring the data format is correct
- Understanding the output of the algorithm run
- Re-training the algorithm with new data
- Dealing with imbalanced data
- Deploying/re-deploying the model
- Predicting in real time/batch
- 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. This simplification not only reduces the workload on your data scientists and application teams but also speeds up the deployment and iteration of predictive models, making it easier to keep your analytics up-to-date and relevant.
Best Practices for Predictive Analytics Implementation
To address these challenges effectively, here are some best practices to follow:
- Leverage User-Friendly Tools: Opt for predictive analytics solutions that cater to business users, eliminating the need for specialized knowledge in coding or statistical modeling.
- Embed Predictive Analytics in Existing Workflows: Enhance user adoption and streamline operations by embedding predictive analytics capabilities directly within the business applications your team already uses.
- Automate Routine Tasks: Select tools that automate the more labor-intensive aspects of predictive analytics, such as data preparation and model deployment, to free up resources for more strategic activities.
- Focus on Actionable Insights: Ensure that your predictive analytics solutions not only deliver insights but also integrate with your existing systems to allow users to take immediate action based on the data.
By following these best practices, organizations can overcome the common challenges associated with predictive analytics and fully leverage its potential to drive better decision-making and achieve their strategic goals.