Strategic advice to leverage new technologies
Technology is at the heart of nearly every enterprise, enabling new business models and strategies, and serving as the catalyst to industry convergence. Leveraging the right technology can improve business outcomes, providing intelligence and insights that help you make more informed and accurate decisions. From finding patterns in data through data science, to curating relevant insights with data analytics, to the predictive abilities and innumerable applications of AI, to solving challenging business problems with ML, NLP, and knowledge graphs, technology has brought decision-making to a more intelligent level. Keep pace with the technology trends, opportunities, applications, and real-world use cases that will move your organization closer to its transformation and business goals.
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In this Advisor, I describe some important AI developments we are seeing with the IoT.
In this Advisor, we examine how fog architecture addresses cybersecurity for next-generation networks.
Agile-at-scale relates to scaling Agile methods for software development use enterprise-wide or on large software development efforts. Scaling has been key as organizations try to tap the benefits of Agile methods to deliver their software products quicker, more cheaply, and with higher quality. This Executive Update presents conclusions based on the analysis of data from over 5,000 software projects fielded by about 500 organizations.
In this Advisor, I introduce an Agile development framework (ADF); not a mainstream Agile offering like Scrum, but rather something I tend to carry around in my head when trying to think about the various views, products, roles, and tasks that a multidisciplined team can bring to bear for different types of Agile projects.
Your business architecture knowledgebase should include content for each business architecture domain that you have determined to be applicable for your organization (most or all usually apply). The content for each domain includes names, attributes, and relationships to business architecture domains as well as other domains (e.g., system applications). The knowledgebase can be created and refined over time.
Artificial intelligence (AI) has reached the point where most major organizations are now investing in the technology. But, a key question I have had for some time now is: to what extent are organizations deriving measurable benefits from the AI applications they have deployed? Fortunately, the latest results from our ongoing survey examining the adoption and application of AI technology in the enterprise offer some insight into this question.
Despite general agreement among researchers and academics of the need for board-level involvement in IT governance, it appears that in practice this is more the exception than the rule. Given the prevalence of this issue, we have sought to answer the question, “What is the state of the art of the research domain of board-level IT governance?” In this Executive Update, we share our findings on the various determinants, theories, and outcomes surrounding board-level IT governance and share some practical guidance.
In this Advisor, I highlight the need to understand the optimal granularity level in analytics to maximize business value. I also point to the need for business owners and strategists to incorporate context in analytics in a balanced manner in ascertaining the granularity levels. Granularity levels can vary dynamically depending on the needs of the business. Due consideration to such dynamicity ensures that time, cost, and corresponding use of resourcing in undertaking analytics are all utilized to provide maximum value to the business.