Solving Larger Business Problems with Mnubo IoT Analytics Suite and Data Science Studio
In my last blog post, I reviewed the first essential steps to quickly and efficiently transform oil (your data) into gasoline (a valuable refined by-product of your data). In this post, I will focus on how advanced analytics and AI can further increase the value of your data and help you solve larger business problems.
There is no formal definition of advanced analytics, but it always includes the use of statistical or quantitative algorithms, predictive models, data mining, or other more sophisticated analysis techniques. Some of these techniques may include the use of AI to generate new information, predict potential outcomes, and recognize patterns via correlations, clustering, or optimization.
Advanced analytics hopes to go beyond the simple use of mathematical aggregations (e.g., sum, min, max, and average) and traditional business intelligence to generate even more commercial value. Examples of business use cases leveraging advanced analytics are various and abundant. They go from the most obvious, such as:
- Using anomaly detection to shift away from calendar-based to predictive maintenance and to increase the first call resolution rate for customer service calls through proactive support
- Leveraging time series prediction to highlight non-compliant usage/performance to optimize asset lifetime and upsell planning
…to those who are gaining in popularity, including:
- Reducing the number and costs of truck rolls
- Preventing customers from churning by measuring their first experience with the product (onboarding phase)
- Optimizing inventory management
- Insuring uptime instead of downtime
But how can you get there?
You need significant business problems to solve
There are many ways for an organization to identify and choose what problems to solve first. At Mnubo, we use and promote a Design Thinking approach. We put in place a two–day interactive workshop tailored to enable your leadership teams to unlock the business value of their IoT investments. Over these two days, Mnubo IoT experts:
- Deep dive into industry trends
- Layout the key considerations of an IoT solution
- Share and discuss customer studies from similar companies in similar verticals
- Align cross–organization functional stakeholders on the key business goals for their IoT strategy in general and analytics strategy more specifically
This workshop helps set the foundation of how IoT-driven advanced analytics can help the business build a long-term competitive advantage via defined key use cases (i.e. larger business problems).
You need analytics tools tailored made to your level of technical expertise
I strongly believe that for larger problems to be solved, as quick as possible, an analytics product needs to empower both the technical and the non-technical users. Through Mnubo IoT Analytics Suite, non-technical users (e.g., product manager, sales and marketing team, CxOs, etc.) can leverage easy-to-use visual query tools and analytics wizards to answer more complex use cases quickly, like:
- How long before the batteries of my products reach a level where they need to be replaced
- When to schedule truck roll according to battery life
- Which customers are likely to return my products during the refund period
- Which customers can be re-engaged to reduce churn
- When should I re-order spare parts
- How frequently should I maintain my products to reduce downtimes and optimize inventories
- When should I roll trucks to replenish my commercial coolers
For technical users and data scientists, Mnubo Data Science Studio is a fully managed IoT-focused AI studio to develop, train, and maintain models as well as ad-hoc algorithms against live streaming data. This studio empowers technical teams to work on more advanced models and algorithms and answer complex use cases, such as:
- Identifying impending faults early to counteract product malfunctions
- Reducing costly emergency repairs proactively
- Identifying anomalies before products are being deployed and updated
- Creating new service offerings
- Reinforcing brand engagement
You need ways to quickly and easily productize the advanced IP you developed
How many models and algorithms have been developed over the years, worked fine on the developer’s laptop, yet never made it to production against live streaming data? Most of them. Mnubo Data Science Studio includes a one-click model deployment process. The latter abstracts the complexity of building and maintaining the infrastructure required to power production–ready AI at scale. This ultimately allows for this IP to become a sound ROI.
You need ways to share your insights and embed them in broader solutions
The Mnubo platform enables users to create rich, interactive dashboards. Both business and technical users can easily drill, slice, and view results within highly visual dashboards. It also includes all the relevant tools to perform a quick and easy integration of analytics content and capabilities within business process, web, and mobile applications. The latter uses auto-generated code which is timesaving for application developers.
Mnubo IoT Analytics Suite includes advanced IoT analytics tools and a full-fledged commercial grade dashboarding environment. With minimal configuration and no coding, you can generate predictions, find anomalies, evaluate device performance & health, assess customer experience & churn, and a lot more. You’ll be able to easily share these insights with others as dashboard and reports, or via external apps and existing corporate solutions.
Mnubo Data Science Studio is a fully managed AI studio allowing data scientists to develop, train, test, productize, and run models and other heuristics. Leveraging fully hosted and managed Jupyter notebooks, it provides a world-class workbench for feature engineering, collaboration, versioning, model productization, and much more. Data Science Studio also promotes a ‘bring your own model’ approach by allowing data science teams to leverage their preferred libraries (e.g. Keras/TensorFlow, Pytorch, scikit-learn, pandas, etc.).