While the analytics models and tools might be well understood by any data scientist, it’s equally important to have knowledge around the engine application, operating environment, configuration and upstream/downstream components. As an example, a mining customer spent $650,000 and lost over 900 hours of production as a result of unplanned downtime. By using our predictive analytics models, the customer would have foreseen the issue and proactively planned for the repair with a cost of $12,000 and 24 hours downtime.

With the critical insights and recommendations in hand, the next step is to effectively communicate. Visualization is the key to understand the data as analysts work with it, and communicate the results to stakeholders. Effective visualizations should encompass more than a static story, by incorporating controls for the end-users to manipulate and interact with the representation. Almost 50 percent of the brain’s resources are dedicated to vision and over two-thirds of the body’s sense receptors are in the eye, therefore, vision is the primary and fastest way to process information.

Industrial companies are increasingly challenged to get the most out of our investments in big data, and the best path to maximize ROI is through agile development, which utilizes short sprints to deliver incremental wins. Consider using data-driven rapid improvement workshops and continuous improvement initiatives to connect your data scientists with technical experts and business stakeholders who will implement changes based on the analytics driven insights. Building small, project-focused applications, and solutions on top of a strong data model as a foundation, allows teams can move fast and deliver results quickly that meet diverse needs of the business. Delivering on the promise of big data is challenging, so it’s important to regularly reflect on the tactics and outcomes of each phase. Iterate, pivot, operationalize, and communicate wins within the enterprise before moving on to the next patch of incremental value.