Next week, experts from Clockwork Solutions share their insights during the Smart Industry webinar “IIoT Data is Useless—How Actionable Insights Gleaned from Data are Critical to Your Digital Transformation.”
Today VP of Industry Solutions Serg Posadas and Director of Services Brad Young preview their presentation.
Smart Industry: Is data useless?
Serg: Data alone is not much use. To support decisions and actions, data must be analyzed and developed into informative insights that directly address a business’ objectives. And it’s much more involved than simply plugging data into a pre-fab model to create magically
insightful analytics. Lots of data preparation is required ahead of the modeling. We need to understand the desired outcomes from the data analysis. Only then can the proper modeling platform, analytics, and visualization be implemented to support business goals.
We should understand that data is an overloaded term. Across an enterprise, different centers and employees define and use data with varying practices. So when we talk about data, we’re often speaking from differing bases and sometimes focused on fundamentally dissimilar goals. So as we collectively work to apply data to a specific purpose, it’s not unusual to find people pulling on different ropes. Often, data issues are complex not because the data itself is cryptic, but more because the people managing the data are complex.
This influence of the human element is changing though. Technology is evolving to enable a growing Industrial Internet of Things (IIoT) where automated data streams are gathered continuously and transmitted from machine to machine. Capital assets are being outfitted with the ability to transmit signals about their health. This acceleration of data production is fastest around the industrial data related to enterprise assets—about twice as fast as any other type of data. M2M (machine to machine) systems will communicate this data directly to each other. GE reports that within the next five years, more devices will be connected to each other than there will be people on the planet: that’s over 50 billion connected machines. This transformation will shift the complexity of data issues—from dealing with the humans in the loop to handling the soaring volume of data.
To be positioned for this future, companies must capture the value from this data—not simply explore and display it for business-monitoring purposes. An analytics platform must be in place to drive the value out of these huge data volumes by transforming historical and real-time data into data about the future and insights that directly apply to today’s difficult, complex decisions.
Brad: Data, and data points, are useless by themselves. Data combined with analytics (or
data analysis) is not useless, and can be very powerful. The power is realized when smart data analysis is put into the hands of those that can make decisions to affect change. You have to have all three pieces: Data, Analytics, Decision Makers.
Smart Industry: What differentiates a piece of data from an actionable insight?
Serg: Well a piece of data is to an actionable insight what a bolt is to an aircraft engine. We need to add lots of additional parts and processes to the bolt before it becomes part of an insight. While the bolt doesn’t make the engine, a faulty bolt can certainly break the engine. If the bolt works its way loose, the entire engine can fall apart due to the imbalance. In the same manner, analytics can be vulnerable to faulty pieces of data. It is the data scientist’s job to design models, platforms, processes and decision-support systems to withstand the problems we see with data elements every day.
Whether the data transmission went awry, or a human injected an entry error, or the data is missing, or we experience high levels of uncertainty. Our data analytics must be able to recognize these conditions and adjust to them. We don’t want to build an elegant model with elaborate dashboards only to have one problematic input drive us towards the wrong decision. The idea is to use the data to improve our business processes, not to create new problems because we failed to design our modeling and analysis properly.
One piece of data does not create an insight, so how much data is needed? Well...how much data to you have? How dirty is that data? And how much time do we have until we use that data for an important decision? You should start by clearly defining the question we’re trying to answer. Understanding the end goal comes first, before we can define the data requirements. A specific set of metrics are designed to answer each individual question. These metrics are driven by time-dependent historical observations, future states, or both. The analytics platform should then populate detailed, time-based answers to a vast set of complex questions. From these answers we construct actionable insights on business operations.