Time series and non-time series data has presence on all levels in a traditional business model, as per ISA S95 manufacturing standards. Additionally, master data and transactional data is contextually structured for each level. However, as all the data is not information, leveraging the expertise of architects, data scientists, designers, developers and integrators is crucial to put data and information to work for enhanced decision-making.
Over capacity, aging work force, heavy debt and cash flow issues can sometimes hamper data-driven decision making. Leveraging Business 4.0TM enablers such as cloud computing, artificial intelligence (AI), automation, big data and analytics, and IoT are key to sustainable growth. When combined with the right tools as well as adoption of hybrid approaches, this can help derive business intelligence.
This paper talks about deploying extraction, transformation, loading technologies to help extract contextual key performance indicators.