The leading global supplier of technology and services started to use supervised machine learning algorithms to predict product tariff numbers with high accuracy. This innovative solution is a great example of how artificial intelligence can greatly improve the quality of services while simultaneously reducing costs, since the company can rely on high data quality.
Manually assigning tariff numbers to a product is a time-consuming procedure. For foreign trade, each company must classify its products as a pre-requisite for export/import processes. As a result of this innovative solution, the service regarding quality, speed and accuracy of the shared classification service has been significantly improved. The enabler for this innovative solution is the combination of high-quality material master data and the availability of a standardized global tariff code classification process at Bosch.
The leading tobacco company set up an Enterprise Analytics and Data (EAD) business unit in 2017 to support the fundamental transformation of its business model and product portfolio towards creating a "smoke-free future". Philip Morris International sees data as a key engine in the acceleration of this business transformation. The novelty of the EAD programme is in the convergence of data governance and data science, which enables the company to overcome the opposition traditionally created between defensive and offensive data strategies.
In the last eighteen months, the team identified more than 40 data owners and 200+ business/data experts. In parallel, they implemented an enterprise-wide data governance repository platform to capture and manage metadata as well as to facilitate interactions between data owners. They registered over 2,300 business terms, metrics and KPIs, over 700 data entities, over 5,200 data attributes and over 9,500 reference data values. More than 27 conceptual and 54 logical data models have been mapped. Additionally, a data lake called "PMI Data Ocean" has been set up as the enterprise-wide platform for data analytics and data science delivering a cross-functional and multi-functional data repository. The team took special care in “data privacy by design” processes being embedded into their operational deliveries as well as analytical use cases and the design of digital solutions. Philip Morris International and the Competence Center Corporate Data Quality have published a case study about this innovative approach. Please find more information here.
The leading integrated telecommunication company set up a “Chief Data Office” (CDO). The purpose of this new business unit is to open up data silos and to foster analytical skills within the company. A key priority is to identify and implement use cases which generate business value from vast sources of data.
One major challenge is the mindset shift regarding how data is treated within the company. To address these issues, the CDO organised its activities into four key areas: (1) Implementation of big data use cases and development of guidelines to standardise these use cases in order to streamline them for faster re-use. (2) Development of the architecture and the "T-Data model" for promoting a shared understanding of the key data entities inside the firm. (3) Creation of a CDO portal to allow the community to exchange and collaborate. (4) Development of a data governance blueprint focusing on transparency, quality and privacy.
The chief data office has been successful in creating building blocks toward its overall data vision. It established a common vocabulary and mindset on use cases, data analytics and data governance topics, increased the visibility of available data assets and made it easier to transfer use case assets as well as to exploit them for new use cases.
The CDQ Good Practice Award was launched in 2013 as a joint initiative of the Competence Center Corporate Data Quality (CC CDQ) and the European Foundation for Quality Management (EFQM). This award acknowledges world-class and innovative data management initiatives paving the way for digital and data-driven enterprises. The good practices submitted by the participating organizations are evaluated by an international jury of data management experts and the CC CDQ community.