October 26, 2020

How to hold Three Amigos meetings in Agile development

Three Amigos meetings remove uncertainty from development projects, as they provide a specified time for everyone to get on the same page about what to -- or not to -- build. "The meeting exposes any potential assumptions and forces explicit answers," said Jeff Sing, lead software QA engineer at Optimizely, a digital experience optimization platform. "Everyone walks away with crystal-clear guidelines on what will be delivered and gets ahead of any potential scope creep." For example, a new feature entails new business requirements, engineering changes, UX flow and design. Each team faces its own challenges and requirements. The business requirements focus on a broad problem space, and how to monetize the product. The engineering requirements center on the technical solution and hurdles. The UX requirements define product usability. The design requirements ensure the product looks finished. All of these requirements might align -- or they might not. "This is why a formalized meeting needs to occur to hash out how to achieve everyone's goals, or which requirements will not be met and need to be dropped in order to build the right product on the right time schedule," Sing said.

Key success factors behind intelligent automation

For an intelligent automation programme to really deliver, a strategy and purpose is needed. This could be improving data quality, operational efficiency, process quality and employee empowerment, or enhancing stakeholder experiences by providing quicker, more accurate responses. Whatever the rationale, an intelligent automation strategy must be aligned to the wider needs of the business. Ideally, key stakeholders should be involved in creating the vision; if they haven’t, engage them now. If they see intelligent automation as a strategic business project, they’ll support it and provide the necessary financial and human resources too. Although intelligent automation is usually managed by a business team, it will still be governed by the IT team using existing practices, so they must also be involved at the beginning. IT will support intelligent automation on many critical fronts, such as compliance with IT security, auditability, the supporting infrastructure, its configuration and scalability. So intelligent automation can scale as demand increases, plan where it sits within the business. A centralised approach encompasses the entire organisation, so it may be beneficial to embed this into a ‘centre of excellence’ (CoE) or start moving towards creating this operating environment.,/div.

Why Most Organizations’ Investments in AI Fall Flat

A common mistake companies make is creating and deploying AI models using Agile approaches fit for software development, like Scrum or DevOps. These frameworks traditionally require breaking down a large project into small components so that they can be tackled quickly and independently, culminating in iterative yet stable releases, like constructing a building floor by floor. However, AI is more like a science experiment than a building. It is experiment-driven, where the whole model development life cycle needs to be iterated—from data processing to model development and eventually monitoring—and not just built from independent components. These processes feed back into one another; therefore, a model is never quite “done.” ... We know AI requires specialized skill sets—data scientists remain highly sought-after hires in any enterprise. But it’s not just the data scientists who build the models and product owners who manage the functional requirements who are necessary in order for AI to work. The emerging role of machine-learning engineer is required to help scale AI into reusable and stable processes that your business can depend on. Professionals in model operations (model ops) are specialized technicians who manage post-deployment model performance and are ultimately responsible for ongoing stability and continuity of operations.

Cybersecurity as a public good

The necessity to privately provision cyber security has resulted in a significant gap between the demand for cyber security professionals and the supply of professionals with appropriate skills. Multiple studies have identified cyber security as the domain with one of the highest skills gap. When a significant skills gap occurs in the market, it results in two things. The remuneration demanded by the professionals will sky rocket since there are many chasing the scarce resources. Professionals who are not so skilled will also survive — rather thrive — since lack of alternatives means they will continue to be in demand. ... Security as a public good involves trade-offs with privacy. Whether it is police patrols, or CCTV cameras — a trade-off with privacy is imperative to make security a public good. The privacy trade-off risks will be higher in the cyber world because technology would provide the capability to conduct surveillance at larger scale and also larger depth. It is crucial , delicate — and hence difficult — to strike the right balance between security and privacy such that the extent of privacy sacrificed meets the test of proportionality. However, the complexity of the task, or the associated risks with it, should not prevent us from getting out of the path down a rabbit hole.

The Art and Science of Architecting Continuous Intelligence

Loosely defined, machine data is generated by computers rather than individuals. IoT equipment sensors, cloud infrastructure, security firewalls and websites all throw off a blizzard of machine data that measures machine status, performance and usage. In many cases the same math can analyze machine data for distinct domains, identifying patterns, outliers, etc. Enterprises have well-established processes such as security information and event management (SIEM), and IT operations (ITOps), that process machine data. Security administrators, IT managers and other functional specialists use mature SIEM and ITOps processes on a daily basis. Generally, these architectures perform similar functions as in the first approach, although streaming is a more recent addition. Another difference is that many machine-data architectures have more mature search and index capabilities, as well as tighter integration with business tasks and workflow. Data teams typically need to add the same two functions to complete the CI picture. First, they need to integrate doses of contextual data to achieve similar advantages as those outlined above. Second, they need to trigger business processes, which in this case might mean hooking into robotic process automation tools.

Fintech Startups Broke Apart Financial Services. Now The Sector Is Rebundling

When fintech companies began unbundling, the tools got better but consumers ended up with 15 personal finance apps on their phones. Now, a lot of new fintechs are looking at their offerings and figuring out how to manage all of a person’s personal finances so that other products can be enhanced, said Barnes. “We are not trying to be a bunch of products, but more about how each product helps the other,” Barnes said. “If we offer a checking account, we can see income coming in and be able to give you better access to borrowing. That is the rebuild—how does fintech serve all of the needs, and how do we leverage it for others?” Traditional banking revolves around relationships for which banks can sell many products to maximize lifetime value, said Chris Rothstein, co-founder and CEO of San Francisco-based sales engagement platform Groove, in an interview. Rebundling will become a core part of workflow and a way for fintechs to leverage those relationships to then be able to refer them to other products, he said. “It makes sense long-term,” Rothstein said in an interview. “In financial services, many people don’t want all of these organizations to have their sensitive data. Rebundling will also force incumbents to get better.”

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