For years we have had numbers thrown at us highlighting the billions (or trillions) of devices connecting to the internet. What started as a ‘FOMO’ (fear of missing out) tactic has progressed into an over-used marketing term with little measurable impact.
But as the market matures, many of these newly ‘connected devices’ will drop out of the IoT race due to a lack of differentiation and measurable benefits. The IoT will reach a tipping point, where the profusion of connected devices will generate an uncontrollable amount of uncaptured and unrealized data. Product makers who have not already considered analytics in their product roadmap, will rush to develop platforms in-house, use re-purposed CRM/ERP systems or enlist the help of a third party to try and quickly tap into their growing reserve of sensor data.
But with literally hundreds of platforms fighting to get a piece of the IoT pie, product makers will often find themselves asking “which platform is right for me?” It is important to know upfront what business questions you want to answer (usage behavior, anomaly detection, product performance, etc) and how you want to answer them (real-time, on-demand, etc). When evaluating which analytics platform to implement for your connected products, consider the following aspects that would influence how you deal with IoT data:
Data integration merging different structures or sources of data while keeping costs and complexity at a minimum. The variety of IoT data further complicates how platforms can control and maintain data quality. Purpose-built IoT analytic platforms are versatile, capable of ingesting any data type from any data source.
Data volume increases when billions of sensors and sensor-enable devices are producing more dynamic, heterogeneous data in real-time. IoT-specific analytics platforms are designed to process the scale (volume) of IoT data at high speeds to enable rapid decision making.
Availability of skills will continue to burden enterprises as analytics in the world of IoT/sensor data require skillsets that are very different from most traditional software projects. Leveraging analytics 3.0 platforms alleviates the burden of finding qualified engineers capable of analyzing and managing IoT data.
Cost will vary tremendously depending on the chosen platform. Whether building in-house, enlisting the help of a third party vendor or implementing a turn-key solution, all analytics platforms require an initial capital investment. However, cloud-based analytics platforms reduce the time to market and insight enabling decision makers to realize a quicker ROI.
CONNECTED, SO WHAT?
mnubo believes product manufacturers interested in improving their bottom line, represent the biggest opportunity for IoT Analytics – wherein data-driven insights lower operating costs, increase productivity, and help extend into new markets or develop new revenue streams.
Build your customer experience
Once a product is connected, product makers have the ability to better understand and profile their customers. They can build the customer experience to better understand the engagement and satisfaction over time. Data can optimize service packages and new product offerings.
Drive down the cost of operations
Data can be used more effectively to drive down the cost of operations with predictive maintenance, proactive replacement, smart replenishment and so on.
Transform your product into a service
Once a product is connected it is no longer just a product, but rather a service. There is an opportunity to translate these new ‘services’ into data-centric business models, where usage-performance data enables new revenue streams (replenishment as a service, data-driven warranty services, etc). Furthermore with edge and cloud intelligence, there is the ability to create a more contextual, enriched product. One that is smart in its behaviour and performance.
Not only do IoT analytics promote seamlessness for the end user, but also for the product makers. In the past, it was up to the IT department to disseminate analytics throughout the organization. Today, IoT alleviates this burden, empowering multiple stakeholders with targeted business insights. For example
- How/when/where is the product being used?
- What are the most popular features?
- What are product faults?
- Who is negatively/positively impacted by the latest firmware updates?
- Is the latest firmware update influencing connectivity? Data collection? Etc
Marketing and Sales
- How/when/where is the product being used?
- How is engagement changing over time?
- Is engagement influenced by a specific marketing campaign?
Operations and Aftermarket Services
- How can truck rolls be optimized?
- How can I reduce product downtime?
- How can I reduce the cost of warranties?
Insights from connected devices will drive operational strategies and empower data-driven business decisions. A real-time view on usage and product insights help deliver more intelligent services to IoT service providers and the end consumer. Analytics answer the question – connected, so what?