In this era of digital transformation strategic processes and operations are mostly data-driven. Data science is therefore one of the main themes around Quality 4.0. The growing role of data in organizations is seen through the innovative trends and technologies in data warehousing, CRM systems, data analytics and business intelligence.
Nevertheless, the shift to smart manufacturing requires greater attention to data management and data quality. Industry 4.0 technologies can be used to transform already-in-use machines in learning self-aware tools, in order to optimize the performance and processes of existing systems. At the same time, the risk of error accumulation or minor inconveniences could result in revenue loss and inefficiency: this is the reason why it is necessary to develop data reliant technologies that comply with regulations.
Data for business purpose
Ensuring data quality and integrity in manufacturing environments is the greatest challenge, as, in fact, information is heterogeneous and complex. If a company takes the risk to rely on data for decision-making, it must set its own standards for ensuring data quality through its own methodology of data profiling, measurement and cleansing with continuous data quality monitoring.
According to ISO 25012, data quality is defined as “the capability of data to satisfy stated and implied needs when used under specified conditions”. On one hand it is important to have in mind that data must be useful for business purposes, so in the measurements, one should try to focus especially on the dimensions that are relevant to reach the maximum ROI (return on investment). This is mostly driven by costs and time that are needed to achieve the desired quality level. On the other hand, considering all dimensions gives a complete picture that may lead to new perspectives as data are interconnected.
Measuring data quality
Assessing data quality is thus a great challenge: it needs to be broken and analyzed into measurable characteristics and dimensions, because each of these can capture a specific aspect of quality. Measurement is also the first step to diagnose and fix data quality.
In the field of industrial certification of quality, certifying the level of quality of specific data repositories can provide customers and partners with confidence. The standard ISO/IEC 25012:2008 has been defined to provide the industry with a general data quality model for data retained in a structured format within a computer system.
Adopting automated control management systems allows companies to ensure the quality of processes and products, avoiding the risks of violations of regulations. Quality 4.0 becomes a competitive advantage and represents the basic approach of Digital Manufacturing.
