Sam Higgins

Sam Higgins

Sam Higgins was an IBRS advisor between 2017 and 2020 with 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.

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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

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:

  1. DIY (Do It Yourself) – By adopting AI early as stand-alone services; or
  2. 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.


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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

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

"Proactive optical character recognition of incoming content will accelerate AI-enabled automation" IBRS, 2018-03-06 06:54:57

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.


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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.


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Conclusion: The development of AI-based solutions is heavily dependent on various types of data input in the form of either:

  • Large data sets used to conduct experiments to develop models and algorithms for predictive analytics, optimisation and decision recommendations; or
  • Enriched and tagged corpuses of images, audio, video and unstructured text used to train neural networks using deep learning techniques.

While at first the data management needs of AI-based solution development might leverage both data scientists and their existing business intelligence platforms to exploit these types of data, the actual lifecycle management needs of AI developers will expand quickly beyond the boundary of the traditional enterprise data warehouse.

Therefore, like the source code and configuration data underpinning transactional business applications, the raw data and algorithms of AI solutions must be managed by evolving DevOps practices towards a comprehensive “DataOps” model.


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Conclusion: Although online digital platforms are in ready supply, organisations remain unable to avoid the receipt of critical information in the form of paper documents or scanned images. Whether from government, suppliers or clients, organisations are faced with written correspondence, typed material, completed forms or signed documents that must be consumed. For a variety of reasons, it may be unreasonable or impractical to expect this information to be sent in machine-readable form.

However, machine-readable content from incoming information, both past and future, is emerging as a prerequisite to exploit artificial intelligence and machine learning as part of digital transformation. Therefore, organisations need to re-examine their data ingestion strategies and move proactively to the use of optical character recognition on incoming paper- and scanned image-based information.


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Conclusion: Organisations continue to emphasise their competitive differentiation based on the data they hold, and the insights gained from analysing this valuable resource. The rate at which organisations are shifting from traditional process-based to insight-oriented differentiation is being further accelerated by the adoption of Cloud-based data analytics services.

The combined result is an increasing portion of enterprise project activity that can be classified as extract, transform and load (ETL).

Despite ETL being the mainstay of data integration for decades, the cost of specialised skills and significant manual effort expended on integrating disparate data sources is now coming into sharp focus. In response, organisations are rightly seeking lower-cost solutions for data integration.

Although ETL exists in the form of at least one tool in almost every enterprise, the cost of ETL as a proportion of data analytics projects means organisations must decrease reliance on traditional ETL tools in favour of automated solutions that exploit machine learning techniques to reduce the need for ETL developers.


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