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.
- IBRS iQ
31 December 2017
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.
- Sourcing & Staffing
29 April 2019
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.
- Governance & Planning
05 April 2019
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.
- Operations & Service Delivery
05 March 2019
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.
- Governance & Planning
04 February 2019
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.
07 January 2019
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.
03 December 2018
Conclusion: Organisations seeking to ride the new wave of AI-enabled transformation are facing a clear choice when it comes to the adoption of supporting AI capabilities such as machine learning or speech recognition, either:
- DIY (Do It Yourself) – By adopting AI early as stand-alone services; or
- MODIFY (Make Others Do It For You) – By waiting for AI functionality to be embedded in existing solutions.
Deciding which path to take requires that organisations reflect on their current maturity when it comes to building solutions. Only those organisations that can honestly demonstrate full development lifecycle capabilities and that have contemporary development tools and frameworks should expect anything but proof of concept success with DIY approaches to AI solutions.
05 July 2018
Conclusion: In seeking to achieve their vision, goals and objectives, organisations constantly evaluate internal and external factors in order to take action. Although tuned to the unique needs of each enterprise, there have been identifiable waves of factors and responding actions that have occurred since 2000 in the form of business and digital transformation.
Business transformation addressed the changing nature of markets in a connected and globalised world by focusing on delivering cost savings through new models of operation, while the subsequent wave of digital transformation sought to employ technology and exploit pervasive connectivity to increase the efficiency of internal processes and customer-facing interactions.
IBRS has identified a new wave we call “artificial intelligence-enabled (AI-enabled) transformation”, which is focused on optimising business operations through the use of emerging technologies that leverage “self-learning” algorithms to make predictions, respond to real-world objects and events, and possess user interfaces that mimic how humans communicate.
However, in order to successfully exploit this new wave of transformation, organisations must first understand what exactly AI is and how AI-enabled transformation differs from the waves that have come before it.
- Governance & Planning
01 June 2018
Conclusion: Due to years of tactical software deployments in response to urgent digital transformation uplifts, organisations have created a jungle of business intelligence (BI) technologies deployed in the absence of a well described and comprehensive approach to the challenges faced; challenges that will continue to increase with the shift to AI-enabled transformation.
Instead the majority of solution paradigms have centred around the application of emerging technologies with little articulation of a coherent architecture traceable to the underlying functional or non-functional requirements required to support a well governed and long lived data analytics platform. Instead, with each new trend in reporting and analytics, e. g. big data, results in a litany of partial solutions.
Enter Data Vault 2.0 (DV2.0) is the first well described architecture, methodology and modelling approach to emerge from the BI community in the last 5 years. DV2.0 provides a solid basis for organisations wishing to avoid the data sins of the past and adoption should be a top consideration for the inevitable expansion of BI that flows from business application transformation and as part of a clear DataOps strategy.
05 May 2018