Benefits, Challenges, & Risks of Predictive Analytics for Your Application

In today’s turbulent software market, predictive analytics has become a key feature for modern customers. Predictive analytics refers to the use of historical data, machine learning, and AI to predict what will happen in the future. This sets certain applications apart from the pack, offering application teams a significant advantage in a competitive market. Predictive analytics is becoming more common across all business applications, including CRMs, supply chain tools, and marketing automation. But we’re also seeing its use expand in other industries, such as Financial Services for credit and financial risk assessment, and Human Resources to identify employee trends.

Using the information from predictive analytics can help business applications suggest actions for positive operational changes. Analysts can use predictive analytics to foresee if a change will help them reduce risks, improve operations, and/or increase revenue.

Benefits & Competitive Advantage of Predictive Analytics

By embedding predictive analytics in their applications, businesses demonstrate an awareness of customer priorities, building trust, revenue, and operational efficiency. These benefits ultimately contribute to the creation of more intelligent, user-centric, and responsive applications that align with user needs and business goals.

1. Data-Driven Decision Making

Embedding predictive analytics empowers the development team to make informed decisions based on data insights. By integrating predictive models directly into the application, developers can provide real-time recommendations, forecasts, or insights to end-users. As a result, the team can create more intelligent and responsive systems that adapt to user behavior, preferences, and changing conditions. Data-driven decision-making leads to more effective product development and a better user experience.

2. Enhanced User Experience

Predictive analytics embedded within an application can provide personalized and context-aware experiences for users. By analyzing user behavior, historical data, and other relevant information, the software can proactively suggest relevant content, products, or actions. This not only improves user satisfaction but also encourages user engagement and loyalty. Your system becomes more intuitive and anticipates user needs, leading to higher retention rates and increased user interaction.

3. Efficient Resource Allocation

Embedded predictive analytics can also help your development team optimize resource allocation. By forecasting demand, identifying potential performance bottlenecks, or predicting maintenance needs, they can allocate resources more efficiently. For example, in an e-commerce application, predictive analytics can help anticipate spikes in traffic during specific events or seasons, allowing the team to scale server capacity accordingly. This prevents unnecessary over-provisioning and under-provisioning of resources, resulting in cost savings and improved application performance.

Risks of Predictive Analytics

While predictive analytics might seem like an obvious inclusion for application teams, it’s worth noting the potential risks, especially in the age of AI. These include data privacy and security concerns, model accuracy and bias challenges, user perception and trust issues, and the dependency on data quality and availability.

1. Data Privacy and Security Concerns

Embedded predictive analytics often require access to sensitive user data for accurate predictions, which can raise concerns about data privacy and security. If not properly implemented and secured, the predictive models might expose sensitive information to unauthorized individuals or entities. The development team must ensure that proper data encryption, access controls, and compliance with relevant data protection regulations (such as GDPR or HIPAA) are in place to mitigate these risks.

2. Model Accuracy and Bias

Predictive models are only as good as the data they are trained on. If the training data is incomplete, biased, or not representative of the application’s user base, the predictive analytics may produce inaccurate or biased predictions. This can lead to poor user experiences, incorrect recommendations, and reinforce existing (and even harmful) biases. The development team needs to continuously monitor and improve model accuracy and fairness, which may require regular data updates and refinement of the predictive algorithms.

3. User Perception and Trust

Users might be uncomfortable or hesitant to use an application that employs predictive analytics, especially if they are unaware of how their data is being used to make predictions (e.g. “black box”). Lack of transparency and understanding about how predictions are generated can erode user trust and lead to decreased adoption of the application. The development team needs to be transparent about the use of predictive analytics, provide clear explanations of how predictions are made, and offer users control over their data and privacy settings to build and maintain user trust.

Remember to Implement with Care

Exposure to these risks can be limited with a mature embedded analytics solution that offers services to ensure successful deployment, training, and ongoing support.

Common Predictive Analytics Challenges & Recommended Solutions

We’ve discussed risks, but there are also hurdles that often surface as you begin to implement predictive analytics. Addressing these challenges 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

As we’ve discussed above, 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

Information in a vacuum isn’t very valuable. 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:

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

Instead of placing the burden completely on your team, certain predictive analytics solutions (like insightsoftware’s) shoulder many of these steps. By choosing one of these more streamlined predictive analytics platforms, you can turn a headache-inducing process into a simple, three-step process. This reduces the workload on your data scientists and application teams and also speeds up the deployment and iteration of predictive models, making it easier to keep your analytics up-to-date and relevant.

Should You Build or Buy Your Predictive Analytics Solution?

If you’re ready to implement predictive analytics into your systems, you have a choice — either build predictive analytics into your application internally (using open-source UI components) or buy a third-party tool that comes with predictive analytics already included.

Building Predictive Analytics Software

While the in-house route gives you total control over the project, such as its scope, budget, and timeline, it does so at a cost. Developing in-house predictive analytics capabilities could take up to 20% of your resources over three months of full-time effort. Companies traditionally build their own predictive analytics solutions when they:

• Have significant IT resources to build, test, correct, and maintain an analytics platform.

• Have a flexible schedule, or their time to market isn’t a priority currently.

• Only need basic reporting tools and a UI with limited functionality when analytics is part of the core competency.

Buying Predictive Analytics Software

With third-party analytics solutions that offer predictive functionality, there’s no need to worry about product maintenance, training, or documentation, since vendors extensively document their platforms. Instead, your software will immediately offer predictive analytics to users that is ready to scale with their needs. Firms often turn to commercially available predictive analytics solutions when they:

• Need a competitive BI tool on a tight timeline.

• Need their analytics to scale reliably with their app or software.

• Can’t let future integrations, feature upgrades, or security flaws from third-party UI components risk their app or software crashing.

The choice between building and buying predictive analytics software for application teams depends on your team’s expertise, available resources, timeline, and the level of customization required. Building offers tailored integration and customization, but can be resource-intensive. Buying provides rapid deployment and expertise but may require compromises and introduce vendor dependencies.

Best Practices for Predictive Analytics Implementation

Whether you’re building or buying, we recommend following these best practices to minimize the risks and avoid the challenges we covered earlier:

• 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.

Trusted, Tested Predictive Analytics with insightsoftware

Investing in a mature, third-party embedded analytics solution, like insightsoftware, mitigates a lot of these risks. Application teams across the globe use our analytics platform to provide users with predictive insights and unlock more value from their solution.

We use modern HTML5 and fully open APIs so you can customize and enhance the platform in its entirety. Even the tiniest details of the dashboards, data visualizations, interactions, scorecards, labels, and more can be tailored. Our solutions also enhance security for application teams and users via robust authentication and access control mechanisms, SSO integration, data encryption, auditing and monitoring features, secure APIs for customization, and regular security patch updates.

We’ll work with you to kickstart your customers’ BI and analytics journey quickly and easily with an analytics platform that delivers an embedded-focused, personalized, easy-to-use analytics experience for you and your customers. Watch the webinar below to learn more, or visit our predictive analytics page to get a demo of how insightsoftware can enhance your insights today!