What can your data do for you?
Simon Sinek sums it up nicely: always start with the why. Why do you feel a need to collect more data? What problems do you want it to solve? What corporate objectives are these linked to? Your answers to these questions will clarify which data and which data strategy you actually need.
The automotive industry can help to illustrate this. Every manufacturer collects huge quantities of data, both during production and from the finished product and driver (through apps). This data can serve various purposes:
- Improved production
- Product innovation
- Personalized services for the driver
- Value of the data in and of itself
Various applications can help make production more efficient. For example, a learning algorithm (machine learning) can enable automated quality control. Product innovation leads to ever-better and ultimately, self-driving cars. A digital assistant within the car can support the driver through suggestions based on their actual driving behavior and habits. McKinsey and Co. researchers estimate that the market for vehicle data will be worth $750 billion annually by 2030. For example, data can be used to generate high data density maps, showing anything from average increases in traffic speed to the whereabouts of potential hazards on the road.
Take a both/and approach
This is a great time for data from a technological point of view. Relatively inexpensive sensors can do the capturing, and the cloud offers all the flexible storage space and computing power you need to gain insights from the data. New technologies, such as Digital Twin, offer novel opportunities to produce insights based on current data sources. An existing CAD model or 2D design can suffice to create a digital copy of your plant.
As stated in our Digital Flow vision, you will create the most value by paying attention to both ends of a spectrum. In addition to data's purely technological side, it is also important not to ignore the 'softer' aspects. Who is responsible for what data at your company? Who will ultimately determine the truth of the matter if two data sources prove contradictory? In other words, who will help ensure the quality of the data within your organization? A data-driven approach requires a data-driven mindset, requiring the right human skillset and standardized methods in its turn.
Start small and focus on value
Such a mindset requires time and resources. That means you should start small, with just one use case of genuine business value. Data-based insights can help to increase output, improve productivity, optimize the use of assets and deliver higher quality.
Always strive for scalability to deliver Proof of Value (PoV) and build sufficient confidence to roll out Smart Industry across the company.
This blog is the third in a series where we discuss the myths and facts about the digital transformation in the industrial sector. The first three myths have already been dispelled. Discover which myths or facts will bite the dust in the coming weeks.