Why you need to design for analytics to succeed at IoT
As companies in a variety of industries plan and execute their digital transformation strategies, powered by the internet of things, they should be designing everything — products, applications, sensors, networks, services, etc. — with one all-encompassing goal: to maximize the value of the data they create or ingest.
The key to maximizing that value is analytics. Timely, reliable, accessible, trustworthy analytics is the future battlefield on which IoT wars will ultimately be won or lost. Without significant data analytics investments, most organizations have little hope of making sense out of all the data generated by IoT, and of gaining real value for themselves and their customers.
With so many constantly connected devices, we now have an opportunity to learn about how products really work — or don’t work — over a period of time in a customer environment such as a home, school, workplace, healthcare facility or other environment.
A new product, created for today’s connected world, should be designed to analyze the usage patterns, effectiveness and performance of the real-world production instances in the hands of actual customers. The main goal of doing so is to inform the development of future products and services, so they can increase in quality, effectiveness and customer satisfaction.
Getting personal
IoT gives manufacturers an opportunity to rethink what it means to bring a product to a mass market. It opens up possibilities of whole new ways of offering products on a personalized basis, because everyone uses products differently or has different goals or purposes in mind when they buy and use products.
Manufacturers in general, and consumer goods makers specifically, are starting to explore moving away from the traditional industrial model of building low product variability with high volume. For decades, companies have been building lots of things that basically look and act the same. With that model, everyone buys essentially the same thing, or the same thing with perhaps a slight variation such as the size, color or style of the product. But in today’s market, more consumers are looking for personalization, unique customized offerings that are designed and made just for them and tailored to meet their needs.
Part of this trend has been driven by the rise of technology such as 3D printing. This enables mass customization, or the production of personalized goods or services that meet customers’ needs. Mass customization aims to deliver individualized products while maintaining the low costs enabled by mass production.
But connected devices also allow the products to adapt themselves in the field, realigning their capabilities and learning from customers’ preferences to provide uniquely personal product experiences.
Thriving through analytics
In a recent interview, Mark Bregman, senior vice president and CTO at NetApp, described “data thrivers” as “those organizations building their business around data and then deciding what business to be in.” This thinking represents a profound shift in how businesses make product decisions.
For instance, a maker of cosmetic products for women could deploy a connected cosmetics case that would be able to discern which products a consumer is actually using, how frequently she is using them and the current level of each consumable.
A cloud-connected application could not only determine when to provide just-in-time resupply, but might also use a built-in camera to provide makeup suggestions. Based on the customer’s usage patterns, certain personalized cosmetics blends could be offered just for that unique customer’s needs. The possibilities are endless.
The key to making this whole process — or a similar process with a different set of products — work is to design the products with data collection and advanced analytics in mind from the beginning. The products themselves will then become a principal source of valuable information to inform the future business strategy. Data thrivers will be organizations that embrace an analytics-led business strategy as intensely as other more traditional business disciplines.
Operational intelligence and digital twins
A significant component of designing for analytics is the digital twin, which is a near-real-time digital replica of a physical asset, process or system that can be used for various purposes. This digital representation of a product instance provides a historical record of its operating performance throughout its lifecycle.
Companies can compare electronic “twins” of real products with the design model to learn how they can enhance future products. Advanced analytics technologies, such as artificial intelligence and machine learning, can be used to create dynamic digital simulation models that update as their physical counterparts change, providing insights into potential customer service issues before they impact customers.
This type of near-real-time advanced analytics is at the heart of an operational intelligence revolution that promises to allow companies to provide better warranties and service levels, while reducing support costs and increasing customer engagement and affinity.
Traditional business intelligence analytics will also be informed by accumulated sensor data and help to optimize strategic decisions. But it is an impending revolution in operational intelligence that promises to produce the most compelling benefits for connected product manufacturers.
Challenges ahead
Of course, the whole idea of gathering and analyzing personal data about how products are used is not without challenges. Product companies will need to offer significant incentives and benefits for customers to want to share such information. Sufficient safeguards of personal privacy, transparency about who owns product usage data, and controls affecting what such information can be used for must be provided.
But these concerns should not stop companies from embracing this new era of connected, personalized products that will ultimately deliver better customer experiences and financial gain for businesses. Companies such as Amazon, Google and Apple, with their in-home digital assistants, are already proving that consumers will embrace connected devices that deliver both utility and security. The common thread between these companies is that they have data and analytics in their DNA. The transformative companies of the future will be the ones that rebuild their organizations to fully embrace these disciplines.
By designing for analytics, even the most unsophisticated companies (or products) can be transformed into digital powerhouses of tomorrow.
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