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7 Ways to Think Differently about your Industrial Data: Lessons shared by the Element Customer Advisory Board

December 20, 2022

Earlier this week, we hosted an inspirational conversation with several of our visionary customers.  These companies are leading the way to provide a connected experience for their frontline employees so they can achieve more. This group of leaders are on the forefront of Industrial Transformation and while generally optimistic about their journeys, they have seen first the challenges.  These are the words used to describe their journey in 2022:

I love the tension between these words - the journey is complex, complicated, challenging and messy - yet it is also inevitable, amazing and exciting.  Progress is being made, value is being recognized, some are reaching scale and details matter.  

  1. People, Process, Technology & DATA
    For years, we’ve optimized around people, process & technology.  We’ve refined hiring profiles, upskilled our team, cleaned processes, evaluated, bought and decommissioned technology - but why?  How do you know what to invest in next?  Data of course!  Unless you focus on the data, it will be that much more challenging to make data driven decisions that ultimately help us build and execute the roadmap to optimization across people, processes and technology.
  2. Lowering the Cost of Curiosity and Building Collaboration
    Data silos are not a new topic of discussion among this audience, nor is it new to talk about unlocking data or creating data transparency.  But what was incredibly interesting was a conversation around significantly more data ‘openness’ in order to provide employees the space to be curious with the data.  Great ideas can come from anywhere in the organization, but if the data is not available to those people, and if those people aren’t given the time or luxury to be curious around the data, then it’s a missed opportunity.
  3. Connected 3rd Parties
    Speaking of data transparency, what if you gave your vendors access to data that was relevant to them?  Is this type of trust and transparency with partners necessary for them to continue to optimize on your behalf?  Could they provide you a better product or service if they could make real time, data driven decisions to optimize their place in your workflows?  It adds pressure to select the right partners and build the right relationships, but if this is the modern era of bi-directional open partners, then is this the right step to help them be a better partner to you? Some industries, such as airlines, have already embraced this concept.  Can we all create greener, safer and more sustainable businesses by being better partners?
  4. Optimized Data belongs in the Boardroom & The Shop Floor
    It is no surprise that executives ask for data.  They are trained to make data driven decisions.  Yet the more data they ask for, the more friction it adds if the data isn’t organized in systems - in turn adding up to hundreds of spreadsheets to be completed by people on the shop floor (and frequently on a recurring basis - taking more time away from the plant). Rarely is this a closed loop system - the feedback loop is missing creating mistrust and a lack of transparency.  How do we unwind the team from manual data entry, empower them with the data they need to effectively run their business and give them the space to be curious?  You need a trustworthy data foundation where everyone can access what they need and share questions, thoughts and ideas.
  5. Architecture First Mindset
    It’s not easy to obtain budget for data architecture projects without a concrete business plan, but it’s essential for the long term health of a modern industrial tech stack.   With every decision to add a new piece of technology it’s necessary to think about scaling for the future, without having to do rework, due to technologies that don’t expand or integrate. The most important thing according to one of our CAB members…. Only invest in software that has an open API / SDK / Integrations.  Stated another way - avoid walled gardens. A modern industrial technology stack will, by definition, allow for best of breed technologies that work well together and have the ability to integrate with all of the legacy technology that already exists within your organization.
  6. Information in Context
    There is no shortage of industrial operations data, the hurdle is how you make the data easily consumed by the board room, the shop floor and everyone in between such that they understand the data.  In other words, data needs context to be understood, analyzed and acted upon.  Your metadata strategy and architecture is critical to long term data transformation.
  7. Data Movement is not a One Time Activity
    Time series data is an obvious data stream, but other data will change over time as well.  Getting data to the right place at first blush may feel like a one time activity, but in actuality, you will need it to stay fresh to keep making decisions based on the most current information. You could code your way through this problem as a brute force method, but over time, data quality will erode and you will need to start over. For a sustainable data architecture, it’s best to configure a data product rather than customize code.  

Industrial organizations have been collecting data for decades.  If you consider data an asset, these companies have a much bigger balance sheet than you would imagine.  The question is how do you get value out of the data - it’s impatiently waiting for you.  Data must be contextualized, understood and available to all.  Executives and people on the shop floor.  Internal employees and vendors.  Think big, start small and move fast.  Element is here to partner with you on the journey.  

The Element Team and I thank our CAB members for sharing their insights and opinions with us.  Everyday we become a better partner to you when our goals are aligned and we understand your long term vision.

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