Data siloes
There are two types of data siloes: accidental and intentional. Accidental siloes tend to be data that’s been stored by an individual or department, without realising the importance of central data sources (or because a central data source didn’t previously exist). Usually, data quality and data governance initiatives should weed these out and stop them from happening again. The presence of siloes can significantly hamper the potential of advanced technologies like the Internet of Things (IoT) and Artificial Intelligence (AI), which are central to modern manufacturing.
Intentional data siloes are a bit more difficult to manage, as they come from people wanting to control their data and keep it in the formats and platforms that they are used to. Data ownership and accountability is one thing, but keeping it separate is another, and it’s a serious block to cultural data evolution, halting progress and adding complications. Overcoming this type of silo takes, again, strong communication and top-down influence.
Either way, siloes are not conducive to a strong data culture. It means that there is valuable information that isn’t available to the business as a whole. It skews insights and damages the efficacy of data technology.
No ownership or accountability
At the other end of the spectrum, there can be the perception that data is ‘not my problem’: it’s a data issue, a tech issue, a leadership issue. If you want to create a strong data culture, data has to be everyone’s problem, with every single person who handles data taking responsibility for its reliability, accuracy, timeliness, and availability.
The key to overcoming this obstacle is showing what good data quality should look like and explaining what can happen at a business and individual level if it isn’t maintained. As with some of the other points above, it’s about creating understanding and appreciation.
Looking beyond data platforms
Digital transformation of Industry 4.0 requires a holistic approach. Especially one that encompasses AI, IoT, and real-time analytics to fully leverage the terabytes of production data generated daily. Embracing these technological advancements is essential for a true cultural shift towards data-driven manufacturing.
incorporating concepts like digital twins and augmented reality into manufacturing processes shows how technological advancements can transform data culture. This holistic approach to data, combining MDM and PIM with cutting-edge technologies, is critical for manufacturers seeking to thrive in an increasingly complex and data-driven industry.