Contributions for the January Issue of The Best Practice Magazine
Submit your article about Improving performance (IMP) - Process management (PCM), Managing performance and measurement (MPM) & Process asset development (PAD) for next month's issue of The Demix Best Practice Magazine.
Sustaining Habit & Persistence (SHP); GOV & II
Sustaining Habit & Persistence (SHP) In the CMMI model, the Practice Areas in Sustaining Habit & Persistence (SHP) Capability Area enable a persistent and habitual organization culture. The SHP practices apply to the processes the organization develops and uses and NOT the CMMI practices.
SHP practices address organizational persistence and habit from two different perspectives: Governance (GOV) Implementation Infrastructure (II)
Governance (GOV) This Practices Area contains practices senior management performs to promote the way work is accomplished that is relevant and important to the business and the organization.
Implementation Infrastructure (II) This Practice Area describes the necessary infrastructure to build, follow, sustain and improve processes over time.
Agile definitely demonstrated to be the best approach for organizations to keep up with changes that happen due to economic, social or even health sudden new scenarios: companies are able to quicker and better adapt and proactively respond to disruptions. But, as for any changes, it is hard and it is not for free. It implies the acquisition of new competences, skills and behaviors, which should lead to new organizational processes, structures, models. How much does it cost to create new habits replacing the old one? What we can do to accelerate that process towards more agility?
Years ago, the COBIT® 5 Process Assessment Model (PAM) was commonly used to assess the maturity level of a COBIT® implementation. The PAM provided indicators for nine attributes and six process capability levels and was used to guide auditors and IT departments.
There is no PAM for COBIT® 2019, but Capability Maturity Model Integration (CMMI) can be used to measure capability levels and combine that information with other factors to give value to the organizational process for measuring maturity. With that information, it is possible to create custom schemas and tools.
Artificial intelligence (AI) is seemingly everywhere, with events such as the COVID-19 pandemic spurring increased investment in AI as organizations accelerate plans to power an increasingly digitally connected workforce. However, upon closer examination, the reality is that many organizations’ investments in AI have yet to pay off. There are myriad reasons contributing to this, including siloed or messy data, overburdened data science or engineering talent and difficulty integrating AI capability into enterprise applications. One significant factor that is often overlooked or swept aside is AI governance.
Governance may not be top of mind when thinking about factors that contribute to increased innovation, but in the case of AI, establishing good governance could be the key to unlocking real value. Rather than thinking of governance as a hindrance, it is helpful to think about it as a set of guardrails and, ultimately, a force multiplier for data science and engineering teams. For the teams on the ground, governance provides preapproved processes to follow, allowing them to move faster and create innovative new solutions. For chief information officers (CIOs) and other stakeholders, governance ensures compliance in the form of auditability and transparency, ultimately yielding higher quality and better performing systems, which, in turn, generate more value.
Considering the practices and current and future legislation in Turkey and around the world, the Solvency II framework1 and new International Financial Reporting Standards (IFRS) regulations2 (especially IFRS 9 and IFRS 17) are areas where there has been discussion recently from the actuary and risk management perspectives as well as the data dimension. Given that the framework and regulations are data-focused, and the right way to apply them depends on data quality, the importance of data governance can be seen.
Considering the responsibilities of actuary and risk management functions within the Solvency II framework and IFRS regulations, and risk managers’ general job description, the quality of the data used for all calculations, modeling and reporting is very important and critical to outcomes. Since the data used for calculations, modeling and reporting are kept on information systems in all institutions, ensuring data quality is mainly the data owner’s job, but the IT department is also responsible because it retains the data.
Rapid advancements in Software Defined Infrastructure and Cloud Computing have rendered IT infrastructure flexible, intangible, and on-demand. However, infrastructure is not yet intelligent and relies on human intervention to understand the correlation between the different IT elements, recognize data trends and take appropriate action. Artificial Intelligence can bring about a transformation in this arena, and eventually initiate infrastructure that is not only self-driven but also flawless.
The Impact of AI on Infrastructure Management Services The disruption caused by AI seeping into every aspect of digital services is quite significant and can have far reaching implications for software development and IT support service providers. Let's look at how it is affecting IT infrastructure management services in particular -
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