Will Industrial Analytics Fuel the Jump to IIoT Hyperspace?
Photo credit: Fotolia
Industrial analytics can become the fulcrum for competitive advantage within the industrial IoT (IIoT) space. However, advantage is gained based on how companies wield analytics platforms and tools.
Industrial analytics describes nothing less than when, how, why and where all data within manufacturing operations are collected, analyzed and utilized. Current methods of analyses tend to be historical (what just happened?), real-time and descriptive (statistical process control and modeling). However, for IIoT competitive advantage, models become predictive and prescriptive (what might happen? what should happen?).
Making the jump from “what just happened?” to “what might or should happen?” is not business as usual. Organizations must make the cultural and organizational jump from spreadsheet mindset towards a sophisticated analytics culture.
That scenario places the CIO, and the IT department, into a necessarily – and demanding – collaborative role. And not only with Operations. Competitive advantage happens when customer-facing business units translate the business value of a manufacturing offering.
In anybody’s playbook, that’s called sales.
How well do your Operations and IT people get along with each other, let alone get along with the folks across the status quo abyss: the folks in business, in sales? Does that conundrum represent the biggest hurdle impeding the jump of an organization into competitive IIoT hyperspace?
Data-rich but Industrial Analytics Insights-poor.
151 decision makers and data analysts participating in the IOT Industrial Analytics Report 2016/2017 (Free opt-in) herald increased revenue, through advances in predictive maintenance applications, as the key value driver for IIoT competitiveness.
In the future, IIoT value will be driven and disseminated to customers through:
- Predictive and prescriptive maintenance of machinery and equipment (79%),
- Customer and/or marketing related analytics (77%); and
- Analysis of field-based product usage (end user analysis) (77%).
While this formula sounds familiar, it is hardly business and operational processes as usual. Currently, organizations collect data but are insights-poor in interpretation for competitive IIoT value creation. Only 32% of participants say they are good or excellent at extracting the right insights from sensor data.
Study participants predict the decline in use of spreadsheets for transfer of knowledge and deriving value (from 54% to 27%) in 5 years. In addition, they foresee an increase in utilization of business intelligence (39% to 77%) and advanced analytics tools (50% to 79%).
Data Scientists as a Competitive, Organizational Asset
A whopping 92% of study participants state the biggest IoT skills gap within organizations is lack of data scientists. Only 22% of respondents had all of the appropriate skill sets currently on board. In addition, there is a concurrent deficit in machine learning capabilities (83% say it’s important, only 33% have that capability). An additional shortfall is in M2M/IoT infrastructure (68% need it, only 17% have it).
That scenario puts the CIO in a translational role across the organization. To execute a competitive IIoT strategy, the CIO works with HR to hire or sub-contract data scientists. Those data scientists are key to translating value into line of business units.
This strategy is easier identified than executed.
These deficits create huge customer acquisition opportunities for organizations prepared to address them now, rather than defer them to the future.
How do analyses of field-based product usage from superior performing machinery confer a competitive advantage to an organization’s customers?
Creating line of business value creation puts current organizational cultures in uncomfortable positions. Analysis of maintenance information is best gleaned by collaboration with end users. These teams become translational for the enterprise.
For starters, technical professionals (data scientists, engineers) work on teams with plant floor employees, who typically are not hired for their critical thinking skills. Business intelligence tools not only involve traditional, periodic customer satisfaction and customer experience surveys. End user experience and Voice of the Customer (end user) literally humanize predictive analytics.
Then things get interesting.
The jump to IIoT hyperspace starts with the jump from the plant floor to line of business units. Otherwise, what goes on, on the plant floor, stays on the plant floor – as the saying goes. And there is pure gold in that predictive analytics data, waiting to be mined and translated to enterprising line of business units.
Ask yourself these questions:
- Where does your organization currently reside regarding IIoT competitiveness?
- What part does predictive analytics play in executing your strategy?
- How might flattening workplace dynamics allow you to translate the value of predictive analytics to line of business units?