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In today's data-driven business landscape, companies are increasingly aware of the potential of data science and machine learning to transform their Go-To-Market strategies. However, the journey to successfully implementing these advanced analytics processes is fraught with challenges. Organizations often find themselves struggling to bridge the gap between their business analysts' market expertise and the deep technical knowledge required for effective data science implementation.
The attempt to build AI models for Go-To-Market strategies in-house frequently results in slow, complex, and chaotic projects. This complexity leads to a staggering failure rate, with industry statistics suggesting that up to 80% of data science initiatives fail to deliver tangible value. The resource-intensive nature of these projects presents another significant hurdle. Hiring specialized data scientists is not only expensive but also comes with a prolonged onboarding period, making it difficult for many organizations to justify the investment, especially given the uncertain outcomes.
Even when organizations manage to overcome these initial obstacles and successfully build a model, they often face an uphill battle in driving adoption across different departments. The technical nature of these models can create a disconnect with business users, leading to skepticism and reluctance to integrate the insights into daily decision-making processes. Furthermore, once implemented, models tend to remain static, quickly becoming outdated and losing the trust of the business units they were designed to support.
These challenges collectively paint a picture of why implementing data science within an organization, particularly for Go-To-Market strategies, can be a painful and often disappointing endeavor.
Drawing from extensive experience in building thousands of predictive AI models for diverse clients, Forwrd has developed a revolutionary approach to automated data science for GTM teams. This approach is encapsulated in a new workflow based on a comprehensive "Data Science Flywheel" methodology.
At the heart of Forwrd's new platform is a circular workflow that represents the interconnected processes necessary for building and maintaining predictive AI models.
The image above shows an "Automated Data Science Platform" represented as a circular workflow. This flywheel design illustrates the interconnected processes necessary for building and maintaining predictive AI models. Let's break down the components:
Data: The process begins with data integration, including over 20 native cloud integrations. This stage involves normalization, enrichment, and modeling of the data.
Analysis: This phase in the Flywheel includes defining metrics (business logic) and correlation analysis. Stages essential for any business oriented machine learning procedure.
Evaluation and Simulation: This phase in the process represents evaluating AI model performance and simulating outcomes. While building an AI model, builders should compare model performance to benchmarks and previous model performance. Remember that the end goal of the model is to improve or move some kind of business metric and for that case simulation of results on predicted cases is essential.
Activation: This stage involves automating insights through alerts, data-push capabilities, and API integrations. Allowing end users at the organization to consume the model.
Reporting: This flywheel stage includes performance reporting, showcasing the results and insights gained from the models.
Monitoring and Continuous Learning: The central wheel indicates that the platform continuously monitors and learns, improving model performance over time.
Proactive Insights: As part of the ongoing process, the platform generates proactive insights to guide decision-making.
Each of the steps above are required in order to build a machine learning or predictive AI model.
Forwrd has incorporated the flywheel components above into the user interface of our product. Matching between the data science methodologies to an interface which is built to support these workflows, yet in an user-friendly and intuitive way.
Based on this flywheel concept, Forwrd has designed a workflow that simplifies the complex process of data science and AI model development. The new workflow guides users through each stage of the flywheel, making data science accessible to a broader range of professionals.
Forwrd's new workflow breaks down the complex data science process into five intuitive stages:
Base:
This initial stage focuses on data preparation. Users can easily join different data points, normalize inputs, and enrich their datasets. The streamlined interface makes it simple to create a solid foundation for analysis.
Metrics:
Here, users can define and build the business logic that will drive their AI models. The intuitive UI allows for quick setup of key performance indicators and other crucial metrics.
Correlation:
This stage analyzes the relationships between various parameters and fields, helping users understand which factors have the most significant influence on their models. Visual representations make these insights easy to grasp.
Simulation:
Users can now run simulations to see how their models perform in real-world scenarios. This feature allows for rapid iteration and refinement of models before deployment.
Activation:
The model activation stage automates the implementation of insights. Users can set up alerts, push data to existing workflows, and integrate with CRM systems, marketing automation tools, and other platforms.
Monitoring:
Allows users to view in real-time the results, accuracy, precision and recall of their AI model and how to model is impacting business metrics.
By simplifying these complex processes, Forwrd's new UI democratizes access to advanced data science techniques. Business operators can now harness the power of AI to drive more informed decision-making and optimize their go-to-market strategies without relying heavily on data science teams.
The user interface is built around this flywheel concept, with each section of the outer ring representing a different stage in the data science workflow. The UI snippets shown for each stage demonstrate how the platform makes complex data science tasks more accessible and user-friendly.
This design allows users to move through the entire data science process - from data preparation to insight activation - in a logical, step-by-step manner. By visualizing the process as a flywheel, it emphasizes the continuous nature of data science work, where insights lead to new questions, further analysis, and model refinement.
The platform's UI is designed to abstract away much of the technical complexity, allowing users to focus on business logic, interpretation of results, and actionable insights. This approach democratizes access to advanced data science techniques, making it possible for a broader range of professionals to leverage AI and machine learning in their work.
