SFIA: The Role of Autonomy in Process Adoption

I was recently involved in an organizational skills assessment where we assessed approximately 300 IT staff using the Skills Framework for the Information Age (SFIA). We used SFIA to identify the skills present and the levels of responsibility at which individuals were operating. SFIA uses 7 levels of responsibility, with each level being represented in terms of autonomy, influence, complexity and business skills. We would generally expect each of the 4 components to be at the same level, but what was interesting is that almost 20 percent of the staff had a level of autonomy that was lower than the other 3 components. If individuals were operating at SFIA level 5 for influence, complexity and business skills, their autonomy level was at level 4.

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COBIT Process Maturity and Process Capability

Process maturity has been a core component of COBIT for more than a decade. Determining the level of process maturity for given IT-related processes allows organizations to determine which processes are essentially under control and which processes represent potential “pain points.” The concept of process maturity in earlier versions of COBIT was adopted and adapted from the Software Engineering Institute’s Capability Maturity Model. In COBIT 5, process maturity has been replaced by the concept of process capability. This is based on the ISO/IEC 15504 standard “Information technology—Process assessment.” ISO/IEC 15504 and the process capability model in COBIT 5 define six capability levels (0–incomplete, 1–performed, 2–managed, 3–established, 4–predictable, 5–optimizing).

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Using In-memory Computing to Manage Big Data

In the telecommunications industry, for example, transactions are measured in the hundreds and thousands of transactions per second. Consider mobile prepaid databases that need to be checked every time a transaction is made. Does the subscriber belong to my network? Is the subscriber entitled to the service? Does the subscriber have enough air time? This easily translates to millions and billions of transactions on that particular database. Consider the emergence of real-time user profiling for contextual advertising. This requires a large amount of storage for easily retrievable transactional information used for profiling. As the industry grows larger, the volume of transactions also grows quickly. These use cases can also apply in the world of big data. This creates the need to build not just bigger and bigger but also faster and faster systems.

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