5 Steps To Embedding Predictive Analytics And Machine Learning Into Your Business These days everyone is talking about Predictive Analytics and Machine Learning (ML). Organizations of every size and in every industry are thinking about investing new analytic capabilities. Businesses need more than business intelligence (BI) capabilities such as reporting, dashboards and performance monitoring to effectively compete. The increased speed demanded by consumers and businesses alike puts pressure on enterprises to respond in real-time, which creates the need for more advanced, more predictive analytics and machine learning. The future belongs to organizations that can embed predictive analytics throughout their business. Predictive analytics and ML are not just more advanced BI. Taking advantage of predictive analytics and ML requires a different approach. Read thispaper to get a better understanding of the power of predictive analytics and how to successfully embed predictive analytics in your organization. Success begins with a focus on decisions. Following this approach will help you become a predictive enterprise. What I Said And What I Learned At Predictive Analytics World 2019 It was a pleasure to participate in Predictive Analytics World again this year. I presented on Backwards Engineering – planning Machine Learning (ML) deployment in reverse. Data shows that a traditional data-first approach to analytics is not generating much value for companies as expected. I urged the audience to instead adopt a decisions-first mindset. The last mile – getting ML models embedded into production systems – is critically important for analytic value and yet it is hard and often neglected. To succeed the data shows that you need to: Make the project about the decision, not the analytic Too often we see analytics underutilized because they were not designed with the action/decision in mind. You can see the slides from my presentationhere. There were many other great presentations in the business track that I moderated. See this blog post for a summary of my key takeaways. There is a common high-level customer journey for insurance customers: Once a preferred provider and policy are decided, the customer applies for the policy If they are approved, the policy is issued If they have a claim, they fill out the relevant paperwork and the claim is adjudicated When the policy is up for renewal, a renewal decision is made While at a high-level, a similar path applies to most customers, customers each have different requirements at different stages of their individual journey. Developing a customer-centric view and adopting an innovative approach to delivering a next best offer (NBO) or next best action (NBA) is extremely valuable in terms of increasing customer lifetime value and retaining profitable customers. Learn in this blog post by Decision Management Solutions’ Zoe Zhou, best practices for creating a superior insurance customer experience.