Strategic advice to leverage new technologies

The nature and complexities of crises in the financial market is gradually increasing, posing significant challenges, especially for large multinational banks, in managing risks and negotiating through a crisis successfully. Moreover, financial institutions must comply with strict regulations around risk management laid down by global financial regulators. Architects must harness various new technologies, such as big data, to help them build a next-generation architecture that complies with their enterprise’s business, IT, and external regulator needs. This Executive Update draws from my experience in developing, implementing, and governing the enterprise architecture for risk management for a large British multinational bank by leveraging big data technology.

Agile implementations at scale involve many teams working in a coordinated way with the intent of affecting a broader set of business priorities and capabilities. At the portfolio and program level of such an implementation, it is critical to ensure that the value produced at the team level is aggregated into a broader set of deliverables and business outcomes that support a value stream.

Any portfolio analysis technique should embody the interests of all agents or stakeholders. Product managers earnestly pursue strategic alignment; administrators seek a healthy balance between growth opportunities and business constraints; and implementers work to deliver high-quality products on time and within budget.

Big data analytics (BDA) is arguably the hottest information technology phenomenon today. Large and small organizations, in virtually every industry, are enamored with the ability to gather and analyze what would have seemed to be an obscene amount of data only a few years ago. This ability has led to the generation of insights that would not have been possible due to the complexity of the underlying data. As a result, these technologies are reaching the point of being fully diffused throughout public and private organizations worldwide. Even with this broad diffusion, we have only scratched the surface of what organizations can accomplish with analytics.

What is technical debt? Consider the metaphor of ­running through mud. There are two consequences of running through mud. One of them is low speed, because the mud has high friction; therefore it slows you down. The second consequence is that mud is much less stable, making it much easier to injure yourself, such as twisting your ankle or falling. These consequences are metaphors for developing with systems that have high technical debt. Everything else about the systems is harder to do, slower, and more dangerous — there is a higher risk of failure in production, and the systems will be harder to maintain.

During this on-demand webinar, Cutter Senior Consultants Murray Cantor and John Heintz introduce a process for applying next generation agile planning to your software delivery process, so you can gain an accurate view of your current status, make modifications where necessary, and improve your odds of success.

Developing cognitive applications requires training cognitive models to be able learn and reason from their interactions with data and their experiences with users and the environment they operate in.

During the first two quarters of 2016, Cutter Consortium conducted a survey that focused on the methods, tools, and techniques surrounding business adoption of disruptive technologies. We collected data across multiple industries and countries, but primarily from the US. There were just about as many business professionals as technology professionals who responded to the survey. The purpose of the survey was to understand how companies identify, pilot, and deploy specific emerging or disruptive technologies.