Industrial companies are becoming increasingly seasoned and sophisticated in their exploration of data analytics. They’re aware of – and intrigued by – the promise of analytics but are increasingly focusing on getting down to business, encountering challenges and asking tough questions.
This means confronting cultural barriers (i.e. “this is how it’s always been done”) and working to ensure the broad availability of data and analytics (versus making them available only to an organization’s most motivated groups). They’re also moving away from addressing narrow challenges with data and instead treating data as a powerful, broadly applicable and dynamic asset. To first-movers in the industrial sector, data is an asset on par with capital, equipment or personnel.
These are some of the findings from Element Analytics’ workshop, “Digital Transformation Readiness” at the recent OSIsoft PI World.
At Element, we’re always curious about companies’ state of digital transformation and factors blocking them from their goals. At the OSIsoft show, we invited leaders to our “Digital Transformation Readiness” Workshop to continue getting a feel for their goals and perceived obstacles.
We used a “Design Thinking” approach in which participants “Ideastormed” the broadest range of ideas instead of the “right” idea. From there, participants voted on top ideas in order to identify the most pressing topics. The Design Thinking process helps ensure voices are heard and problems are viewed from all perspectives. It is an iterative process which brings agility and empathy into problem solving. It’s also a process that’s been ingrained in Element’s values since our founding.
We gathered together leaders in diverse roles and industries, including:
During the session, we addressed 3 key topics:
With more than 100 Post-It Notes on the wall, Sameer Kalwani, Founder and VP of Product, lead a round-table discussion in which participants elaborated on their rationale for submitting and voting on various ideas.
While the participating companies came from different industries, roles, and perspectives, in the workshop we were able to identify unifying themes.
Digital Transformation Opportunities:
By far, data omnipresence - the idea of making data available to anyone in the organization at any time - was the leading opportunity identified by workshop participants. This includes enabling the mobile worker through augmented reality and heads up displays for field tech. To us, data omnipresence shows that industry first-movers are eager to implement broadly impactful data-driven capabilities, not just one-off successes. They see the transformational successes that are possible when data is broadly available – whole new applications, use cases and insights – and want to make them a reality.
Data omnipresence dovetails with what we at Element have seen over the past five years: an increasing focus on using data to improve whole businesses versus outcomes tied to specific use-cases (i.e. efficiency, risk reduction). Data is increasingly seen as a capability in and of itself, not a tool narrowly applicable to individual use-cases. Some call this “Data as an Asset” where asset operators look at data similarly to equipment in terms of availability, effectiveness, quality etc.
The second most popular opportunity was using data to unlock new insights. This encompasses using technologies such as machine learning, AI, blockchain, IOT, and wireless services to help make more data-driven decisions. Enabling greater operational efficiency was the third most popular opportunity and includes making product development faster and cheaper, predicting process control, and increasing operational excellence. Additional areas of interest included risk reduction and knowledge capture.
Digital Transformation Blockers
In terms of challenges, by far the greatest identified by workshop participants was developing a culture that is adaptable to the requirements for transformation. This includes finding and hiring people who think outside the box and the importance of developing a mindset that it is okay to fail. One participant stated, “We’re a 100 year old company. People tell me this is just how we do things.”
Element’s take: While it may seem like a no-brainer to embrace data-driven changes to fundamentally improve an organization, this desire needs to be balanced with the realities of industrial companies. These organizations often have millions (or billions) of dollars invested in fixed capital assets, concerns about worker safety and a focus on environmental stewardship. These companies – and many of us at Element have spent decades working for them – adhere closely to processes and question the need for change for a reason: they stand to lose money, jobs and potentially harm the environment (just to give a few examples) if they make a wrong move. The goal, then, is to address the very real concerns of companies while working to overcome a reticence to change simply because “we’ve always done it this way.” This isn’t simple or easy but we believe it’s a big key to success.
“Our teams are organized around applications, but we need all the information associated with one asset. We need to bring the data together to achieve this.”
Other significant barriers to change included data integration and data quality. Companies are unable to consolidate siloed data and systems in order to leverage the same data in new and unique ways. Additionally, the lack of standardization (e.g. naming of sensor tags) leads to operators’ distrust of resulting analytics. One participant added, “Our teams are organized around applications, but we need all the information associated with one asset. We need to bring the data together to achieve this.”
We were heartened by the conversation at the workshop. Participants – in many ways, the vanguard of industrial data analytics – are piloting programs, asking tough questions and poking holes in receive wisdom (i.e. if a social media company profits from analytics, it should be easy for an industrial company to do, right?). These first-movers are engaged in the hard, important, sometimes incremental work that can eventually pave the way to big insights and business transformation. To use a sports analogy, you need to learn how to block and tackle before you can score touchdowns. The blocking and tackling may not be sexy – or taught in engineering school – but it’s super-important. Progress is being made.
Where are you on the journey toward the wise and wide-spread use of data? Have you begun exploring how to make data analytics a reality in your organization? Have you started identifying elements – technical, cultural, logistical – that stand in the way of generating valuable insights? If not, do you risk being left behind?