One of the best definitions of an “information system” with a practical approach rather than an academic approach is – an automated system that produces data results for further usage. An algorithm (as an engine for any information system) is a rule to transform input data to output data. So fundamentally any information system is transforming input data to output data. We can even say that’s the sole reason for an information system to exist; therefore the value of an information system is defined through the value of the data. So any information system design starts with data and implements algorithms, hardware and everything else required to deliver data with known structure and value.
We start the design of an information system with data. First of all, we document all planned datasets for processing and storing. Data characteristics include:
- the amount of data
- data lifecycle (amount of new data per period, data lifetime, rules of processing outdated (dead) data)
- data classification with relationship to core business from availability / integrity / confidentiality perspective including financial KPIs (like the financial impact from data lost over the last hour)
- data processing geography (the physical location of data processing hardware)
- external requirements for each data class (personal data laws, PCI DSS, HIPAA, SOX, medical data laws, etc).
Data is not only stored, but also processed (transformed) by information systems. So the next step is to create a full inventory of all information systems, their architectural traits, interoperability and relationship, hardware requirements in abstract resources: Continue reading