In this modern, turbulent market, predictive analytics has become a key feature for analytics software customers. Predictive analytics refers to the use of historical data, machine learning, and artificial intelligence to predict what will happen in the future. This ability to analyze and predict future scenarios sets certain applications apart from the pack, offering application teams significant advantage in a competitive market. Predictive analytics is becoming more common across all business applications, like CRM, supply chain and marketing automation. But we’re also seeing its use expand in other industries, like Financial Services applications for credit risk assessment or Human Resources applications to identify employee trends.
Using the information from predictive analytics can help companies—and business applications—suggest actions that can affect positive operational changes. Analysts can use predictive analytics to foresee if a change will help them reduce risks, improve operations, and/or increase revenue. At its heart, predictive analytics answers the question, “What is most likely to happen based on my current data, and what can I do to change that outcome?”
Competitive Advantage for Application Teams
While it’s becoming more common-place, AI-driven predictive analytics capabilities are still a point-of-difference for business applications, helping them appeal to a future-focused market. By embedding predictive analytics in their applications, businesses demonstrate an awareness of customer priorities, building trust, revenue and operational efficiency.
Embedded predictive analytics offers the development team the advantages of data-driven decision making, an enhanced user experience, and efficient resource allocation. These benefits ultimately contribute to the creation of more intelligent, user-centric, and responsive applications that align with user needs and business goals.
Data-Driven Decision Making: Embedded 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. This enables the team to create more intelligent and responsive applications that adapt to user behavior, preferences, and changing conditions. Data-driven decision-making leads to more effective product development and a better user experience.
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 application can proactively suggest relevant content, products, or actions. This not only improves user satisfaction but also encourages user engagement and loyalty. The application becomes more intuitive and anticipates user needs, leading to higher retention rates and increased user interaction.
Efficient Resource Allocation: Embedded predictive analytics can help the development team optimize resource allocation. By forecasting demand, identifying potential performance bottlenecks, or predicting maintenance needs, the team 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 over-provisioning and under-provisioning of resources, resulting in cost savings and improved application performance.
What are the Risks for Application Teams?
While predictive analytics might seem like a no brainer inclusion for application teams, it’s worth noting the risks. 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.
Data Privacy and Security Concerns: Embedded predictive analytics often require access to sensitive user data for accurate predictions. This 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.
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 even reinforce existing 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.
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. 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.
It’s clear that while predictive analytics is becoming more accepted, there is still some residual consumer distrust that application teams need to mitigate. This highlights the importance of building or buying a predictive analytics tool that focuses on security, monitoring and transparent communication to effectively manage the potential downsides of incorporating predictive analytics into an application. Exposure to these risks can be limited with a mature embedded analytics solution that offers services to ensure successful deployment, training, and ongoing support.
Should You Build or Buy Your Predictive Analytics Solution?
You can either build predictive analytics into your application internally (using open-source UI components) or buy a mature third-party tool that comes with that feature already included. We’ve discussed both options at length in previous posts, but here’s the breakdown:
Building Predictive Analytics Software
While the in-house route gives you total control over the project, like 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.
- Tailored Integration: When you build predictive analytics software in-house, you have the advantage of tailoring it to seamlessly integrate with your existing applications. This can lead to a more unified and consistent user experience.
- Customized Features: Your application team can design and implement predictive features that precisely meet the needs of your application’s users. This level of customization can result in more relevant insights and better user engagement.
- Enhanced Skill Development: Building your own software allows your application team to develop new skills in data science, machine learning, and analytics. This can lead to cross-functional expertise and a better understanding of the technology driving your application.
- Resource Intensive: Developing predictive analytics software requires significant time, effort, and specialized expertise. This can divert your application team’s focus from core application development and potentially stretch resources thin.
- Higher Costs: In-house development incurs costs not only in terms of hiring or training data science experts but also in ongoing maintenance, updates, and potential debugging.
- Development Delays: Building predictive analytics software can introduce delays in application development and deployment as your team navigates the complexities of data modeling and algorithm implementation.
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.
- Time and Resource Savings: Purchasing a pre-built predictive analytics solution can save your application team substantial time and resources compared to building from scratch.
- Rapid Deployment: Buying a solution allows you to quickly integrate predictive analytics capabilities into your application, enabling you to provide value to users sooner.
- Expertise from Vendors: Buying from reputable vendors gives you access to their expertise and research in predictive analytics, which can result in more accurate and effective models.
- Limited Customization: Purchased solutions might not perfectly align with your application’s unique requirements. This can lead to compromises in terms of features and user experience.
- Vendor Dependence: You become reliant on the vendor for updates, support, and compatibility. If the vendor discontinues the product or changes their terms, it can impact your application’s functionality.
- Potential Overkill: Pre-built solutions might come with features and complexity that exceed your application’s needs, potentially making the integration more complicated than necessary.
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.
Trusted, Tested Predictive Analytics with Logi Symphony
Flexibility, security and user trust are the three key reasons applications teams might hesitate to buy predictive analytics. Investing in a mature, third-party embedded analytics solution, like Logi Symphony which offers predictive analytics functionality, mitigates a lot of these risks. Application teams across the world are using Logi to provide users with predictive insights and unlock more value from their solution.
Logi Symphony uses modern HTML5 and fully open APIs, meaning you can customize and enhance the platform in its entirety. Your content creators can customize even the tiniest details of the dashboards, data visualizations, interactions, scorecards, labels, and more that they use. The level of customization provided by Logi Symphony easily allows content creators to meet any unique design requirements. The platform is 100% customizable and extensible, requiring no add-ons or additional products.
Logi Symphony enhances security for application teams and users by offering robust authentication and access control mechanisms, single sign-on integration, data encryption for transmission and storage, , auditing and monitoring features, secure APIs for customization, and regular updates with security patches. These features collectively safeguard sensitive data, prevent unauthorized access, and ensure seamless integration within the parent application, contributing to a secure and trustworthy embedded predictive analytics experience.
Organizations looking to add embedded predictive analytics into their applications often want a partner to help meet their embedding needs rather than simply a supplier. insightsoftware brings a human touch to your embedded analytics software experience. The goal is to help you create the most irresistible and compelling platform that users can’t wait to explore.
We’ll work with you to kickstart your customer’s BI and Analytics journey quickly and easily. We’ll help create significant, actionable insights with an analytics platform that delivers an embedded-focused, personalized, easy-to-use analytics experience for you and your customers.
Want to see how Logi Symphony’s predictive analytics can increase the value of your application for your team and users? Visit our website to learn more about Logi Symphony’s predictive analytics capabilities.