Insights Blog
Insights into data management in the processs industries
Transforming Data into Digital Assets
Being digital refers to data and information stored digitally. Being an asset refers to having control of the data and information to produce positive economic value. Being a digital asset consequently refers to data & information we own and control and from which we can generate sustainable and tangible benefits. This has been the job of Sigmafine and its users for almost three (3) decades now.
Tolerance for bad data
Whether it is called “Industry 4.0”, “Edge Computing”, “Big Data” or “IOT”, the tolerance of People, Applications and Business Processes to poor data quality in industrial plants is diminishing rapidly. The Modern process industries thrive on readily usable information and credible data to deliver sustainable tangible business results.
What is the Industrial Plant Dataset
The “Industrial Plant Dataset” is a complex amalgam of synchronous and asynchronous data types and data sources which must be collected, checked, structured and organized to service the business and operational scenarios of users, applications and business process.
Data Quality, a two-sided Benefit Model
The business case for improving Data Quality is blessed by a two-sided benefit model: downside avoidance & upside realization. Data Quality professionals have a rule which they call the “Rule of 10”: “It costs 10 times as much to complete a unit of work when the input data are defective as it does when they are perfect”.
Defining Data Quality in the Process Industries
Data Quality is an abstract concept until we are confronted with bad data or information which is not usable, not credible, not presented correctly, not accurate, etc.. Then Data Quality becomes a very concrete experience. While, there are many definitions for Data Quality, all of them converge to the same focal point. “fitness for use” by people, applications and business processes.
Trusting Data for Action
In process and manufacturing, information systems should be implemented with a system of checks and balances to maximize the quality of data and the economic potential of data. Creating an environment where data can be trusted is not an afterthought. Data quality, similar to product quality, needs to be built into our business and data management activities so that we can trust data for action at all times.