What do all successful restaurants have in common? They live and die by mise en place. Of course there are other elements that contribute to their success – atmosphere, customer experience, quality of food, location. But from fast food to five-star restaurants, expediency, efficiency, and execution requires careful operational planning.
Mise en place is a French culinary term that means “a putting in place.” It means that kitchen staff have all their ingredients measured, cut, peeled, sliced, grated; pans, mixing bowls, tools, and other equipment is where it needs to be; numerous sauces are simmering that will be used across multiple dishes – all before they actually start cooking. This technique enables chefs to assemble meals quickly and effortlessly, which equates to efficient kitchen operations and cost effectiveness for the restaurant.
You may wonder why I am talking about what goes into managing successful restaurant operations. This is a useful analogy for thinking about how we begin moving from discrete data projects to data strategy, which is a foundational piece for true data and Industrial Transformation.
Think of each dish that a restaurant offers as a distinct data project – imagine if every time an order is placed that the chef is starting from scratch every time- having to find the individual ingredients, cut up and prep each of them, make the sauces, cook, and plate. It would likely take an hour or more to prepare each dish. By the time it’s ready, diners would have long since walked out and found a meal somewhere else.
Industrial projects focused on optimizing operational efficiency often are approached in this manner. We start with a data build, collecting, contextualizing, and tagging all this data for each individual project; then build models – months, even years go by. It’s a time and labor intensive process for your subject matter experts (SMEs). And having to repeat this process over and over again for each discrete project is inefficient and costly. What’s more, it pulls your SMEs off task from higher value work and it kills their motivation.
My colleague Steve Beamer recently cited a report from Anaconda, which found that nearly 50% of a data scientist’s time is spent on data preparation—data loading and cleansing—and they lose even more time when contending with numerous data environments and dependencies.
With the current data project approach, every project is disconnected from the one that preceded it, so we’re always starting from scratch. Worse, project success is often predicated on disparate, tribal knowledge that’s locked away in the heads or spreadsheets of the people who worked on the last project. If they’re no longer around or the data can’t be accessed, we’re starting from scratch again. It’s challenging and time consuming to build repeatable models and almost impossible to scale.
We are in a race when it comes to operational data, there’s exponentially more of it and it is only going to increase. Using this data strategically begs for a strategic approach to managing it.
True digital transformation strategy should provide us with a holistic view of all the relevant, potential variables – maintenance schedule, sourcing of materials, transit process for materials, how they’re stored, etc. – all in one place.
It should enable us to Learn, Iterate and Optimize to win the operations data race.
The IX Journey
We view the IX Journey, as moving from a Project-Based Approach to a Strategic & Foundational approach, that sets the table, pun intended, for a Transformational strategy.
Most organizations are at the beginning of their IX Journey. But it’s time for us to move faster or we risk being left behind. Here’s an example of an organization that is moving from a “walk’ to a ‘fast trot’
Evonik, a leading specialty chemical company that is active in over 100 countries around the world and a major supplier for many industries, chose to work with Element Unify to bring together IT and OT metadata for a solid, governed data foundation that supports analytics at scale as part of their overall digital transformation efforts. Ensuring asset reliability and preventing unplanned downtime are critical challenges.
With 400+ data sources, Evonik built 1,347 Asset Twins. Results include 80% less time spent building asset models, 40% less analytics deployment work, and savings of $110K from one analytic at one plant.
A foundational approach to enterprise data strategy eliminates the need to start from scratch for each new individual data project. A strategic and holistic approach where we have the necessary data, cleaned and prepped, at our fingertips on day one saves significant time and effort. SMEs will be empowered with useful operations data that can now drive business outcomes.
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