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Sam Higgins

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Sam Higgins  is an IBRS Advisor over 20 years of both tactical and strategic experience in the application of information and communications technology (ICT) to achieve business outcomes from large complex organisations. Through previous roles as a leading ICT executive, strategist, architect, industry analyst, program consultant and advisor, Sam has developed an extensive knowledge of key markets including as-a-Service (Cloud) computing, enterprise architecture (including service-orientation and information management), enterprise applications and development, business intelligence; along with ICT management and governance practices such as ICT planning, strategic sourcing, portfolio and project management. Sam’s knowledge of service-oriented architecture and associated business models is widely recognised, and he was a contributing author on the Paul Allen book Service-orientation: Winning Strategies and Best Practices, released in 2006 by Cambridge University Press. As the former Research Director for Longhaus he undertook the first in depth research into the implications of cloud computing and other “as-a-Service” ICT offerings on the Australian and near shore markets. The 2010 report entitled, Defining cloud computing highlights provider gaps in the Australian ICT market, was widely reported in both the online ICT industry press and mainstream media.

Conclusion: Despite being first published over 10 years ago, ITIL service design remains a pain point for both project delivery and service operations teams respectively. The former claims the latter requires the creation of additional deliverables at the point of service transition, while the latter expresses frustration at the lack of attention paid to service design during early stages of project delivery.

The reality is responsibility for IT service design extends beyond both these teams with all functions across IT having a role to play, from strategy all the way through to operations. When all aspects of the IT organisation contribute to the design of new, and modification of existing, services the artificial hump of service design can be avoided. The key is identifying who should be capturing and sharing what information to support service design – an outcome that can be achieved by adopting an end-to-end process integration model for the business IT.


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Conclusion: Despite market hype around the role of data scientists and in-house developers for the successful exploitation of artificial intelligence (AI), organisations are increasingly looking to their vendor partners to provide ready-made solutions. Both business and technology leaders are expecting solutions to be based on the vendor’s ability to leverage their customer base across various industries to create AI features such as machine learning models.

Vendors are responding by increasingly incorporating these features into their offerings, along with a new breed of vendors that are producing pre-trained or baseline machine learning models for common use cases for specific industries.

However, organisations must be prepared to contribute to this AI product development or continuous improvement process which in practical terms means giving major vendors access to data. Without access to good data the result will be sub-optimal for both parties.


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Conclusion: Enterprise architecture (EA) framework standards, such as the Zachman Framework or The Open Group Architecture Framework (TOGAF), are often promoted by advocates as complete solutions for organisations seeking to maximise business alignment and mitigate risk during major transformations through the use of an agreed set of structured planning practices.

However, the term ‘framework’ has become overloaded and not all industry offerings are created equal. Some frameworks provide well-defined content meta models while others provide detailed methodologies and some industry-specific reference models. Therefore, organisations must understand the elements that make up a complete EA framework, then ensure that they adopt aspects from multiple sources to provide complete coverage in support of a contemporary EA practice.


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Conclusion: During periods of business-as-usual activity or low project investment, organisations often consolidate or reduce thei.e.terprise architecture (EA) capability. Conversely, when entering a period of transformation or increased investment, organisations often look to increase their EA activity and so must take stock of the state of current EA practices.

This assessment should not only review the number and calibre of the individual architects within the EA team but also include reviewing and/or renewing the organisation’s commitment to the tools and techniques employed in the form of a chosen EA framework standard.

However, the term “framework” has become overloaded and not all industry offerings are created equal, nor are they contemporary. Therefore, it is important to understand the elements that make up a complete “standard” when it comes to EA frameworks. In most cases, a hybrid approach is required to provide coverage of all the necessary elements needed to ensure the EA team can support the delivery of outcomes aligned to business strategy.


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Conclusion: With both the NSW and commonwealth parliaments passing respective Modern Slavery Acts in 20181, there are now real implications and consequences for business leaders and their suppliers who ignore the risks of slavery within their supply chains.

Unlike the California Transparency in Supply Chains Act 2010 which applies to tangible goods offered for sale, Australian firms will need to disclose their efforts to eradicate slavery and human trafficking from their supply chain of both goods and services. This means at least 2,100 public and private firms2 have until 1 July 2019 to ask explicitly of suppliers, whether local or foreign, off-premise Cloud or on-premise device manufacturer: What are you and your organisation doing with respect to modern slavery risks?

For many organisations in Australia this will mean more than just adding new evaluation criteria to be applied to current and potential suppliers. Rather it requires providing an accurate attestation on the issue of modern slavery which will require lifting the hood on all manner of “as-a-Service” offerings. Thereby exposing aspects of service delivery that the majority of firms previously thought they no longer needed to concern themselves with, having “transferred” risks, such as those found in supply chains, to their vendor partners.


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Conclusion: Medium and large sized enterprises are complex, socio-technical systems that comprise many interdependent resources – including people, information and technology – that must interact with each other and their environment in support of a common mission1. These complex entities undergo varying levels of transformation throughout their useful life in a continual quest to remain capable of fulfilling the business mission and achieving their desired business outcomes.

A mature enterprise architecture (EA) practice is extremely beneficial in supporting and enabling a business to transform in a considered manner, to formulate and execute their evolving strategies. Whether in response to traditional business, modern digital or the emerging AI-enabled transformation agendas, the case for adoption of EA remains as strong as ever.


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Conclusion: The IT organisation in most enterprises suffers from the “Cobbler’s Children” syndrome – they give great advice but do not practise what they preach. A prime example is when IT does not apply Enterprise Architecture approaches and capabilities to the business of IT itself1 and yet expects other departments to apply such principles. Sadly, a new deficiency is emerging in IT as increasingly the role of analytics is democratised across the business – leading to the lack of data analytics capability for IT itself.

As organisations embrace data science, artificial intelligence and machine learning to generate increasingly sophisticated insights for performance improvement, IT must not let itself be left behind. This means ensuring that within a contemporary IT-as-a-Service operating model, space is created for the role of IT Data Analyst. This should be an inward-facing function with primary responsibility for the generation and curation of the IT organisation’s own core information assets in the form of data relating to the portfolio of IT assets, services and initiatives, including curation of operating data from Cloud providers and other partners.


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Conclusion: Australians have become increasingly concerned not only with what data is being held about them and others, but how this data is being used and whether the resulting information or analysis can or should be trusted by them or third parties.

The 2018 amendments to the Privacy Act for mandatory data breach notification provisions are only the start of the reform process, with Australia lagging a decade behind the US, Europe and UK in data regulation.

Therefore, organisations seeking to address the increasing concerns should look beyond existing data risk frameworks for security and privacy, moving instead to adopt robust ethical controls across the data supply chain1 that embodies principles designed to mitigate these new risks. Risks that include the amplification of negative bias that may artificially intensify social, racial or economic discord, or using data for purposes to which individual sources would not have agreed to.

Early adopters of effective data ethics will then have a competitive advantage over those who fail to address the concerns, particularly of consumers, as to how their data is used and if the results should be trusted.


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Conclusion: Increasingly, leaders in the field of AI adoption are calling out the limitations of the current machine learning techniques as they relate to knowledge representation and predictive analysis.

Organisations seeking to adopt machine learning as part of their AI-enabled transformation programs should ensure they fully understand these limitations to avoid unproductive investments driven by hype rather than reality by expanding their definitions of machine learning to include the use of graph networks and social physics solutions.


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Related Articles:

"Machine learning will displace “extract, transform and load” in business intelligence and data integration" IBRS, 2018-02-01 10:03:37

"Prepare to manage the “evolution” of AI-based solutions with “DataOps”" IBRS, 2018-03-31 06:43:42

"Preparing for the shift from digital to AI-enabled transformation" IBRS, 2018-06-01 04:10:21

Conclusion: In IBRS’s 2018 Top Business Technology Trends Priorities Report, we noted that despite significant media attention on blockchain or distributed ledger technology (DLT) in 2017, the primary concerns of Australia’s Chief Information Officers (CIOs) in 2018 remains focused on the more pressing issues of migration to the Cloud, and its impact on IT operations and staffing.

However, ignoring DLT in the long term is no longer an option. After 10 years since the advent of blockchain, real world and production examples are now emerging from market-influencing players in Australia such as the Australian Securities Exchange (ASX) and Commonwealth Bank (CBA). This, combined with significant investment from credible vendors (both old and new), requires that CIOs and their Enterprise Architects review the implications of DLT becoming a mainstream means for secure, immutable data exchange to enable fully automated multi-party workflows.


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Related Articles:

"Blockchain Principles and Cases" IBRS, 2016-03-31 23:14:46

"The Top Business Technology Priorities for 2016" IBRS, 2016-02-01 01:10:48

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