Daily Tech Digest - December 11, 2023

Enterprise Architecture – Supporting Resources on Demand

As the subscription economy grows, the market could become saturated with providers offering varying levels of service quality. Businesses should carefully evaluate their options, considering factors such as customer support, scalability, and the sophistication of available resources. The positive impact of selling EA as a subscription service, however, is clear. With more service providers offering cloud solutions, there is more competition for your business. You, as the business customer, have more options, which can lead to better services and pricing. Business customers of all sizes can get access to advanced technology and data storage capabilities through a subscription. This can open economic doors to developing nations, allowing business growth to more players who would otherwise not be able to participate in a digital transformation journey. This fosters a more inclusive and diverse tech landscape, where breakthroughs can emerge from unexpected corners of the business world. You can focus on growing your core business without the traditional burdens of upfront investment and the complexity of building and managing infrastructure from scratch.

Trends in Data Governance and Security: What to Prepare for in 2024

In 2023, many companies turned to do-it-yourself (DIY) data governance to manage their data. Yet, without seeking the help of data governance experts or professionals, this proved to be insufficient due to compliance gaps and the data security errors it leaves in its wake. While do-it-yourself data governance seemed like a cost-effective solution, it has serious consequences for companies leaving them exposed to data breaches and other security threats. This is because DIY data governance often lacks the comprehensive security protocols and expertise that professional data governance provides leading to both data breaches and other security threats. Worse, the approach often involves piecemeal solutions that do not integrate well with each other, creating security gaps and leaving data vulnerable to attack. As a result, DIY data governance may not be able to keep up with the constantly evolving data privacy landscape, including new regulations and compliance requirements. Companies that rely on do-it-yourself data governance are exposing themselves to significant risks and will see the repercussions of this in 2024. 

Generative AI is off to a rough start

One big problem, among several others that Duckbill Chief Economist Corey Quinn highlights, is that although AWS felt compelled to position Q as significantly more secure than competitors like ChatGPT, it’s not. I don’t know that it’s worse, but it doesn’t help AWS’ cause to position itself as better and then not actually be better. Quinn argues this comes from AWS going after the application space, an area in which it hasn’t traditionally demonstrated strength: “As soon as AWS attempts to move up the stack into the application space, the wheels fall off in major ways. It requires a competency that AWS does not have and has not built up since its inception.” Perhaps. But even if we accept that as true, the larger issue is that there’s so much pressure to deliver on the hype of AI that great companies like AWS may feel compelled to take shortcuts to get there (or to appear to get there). The same seems to be true of Google. The company has spent years doing impressive work with AI yet still felt compelled to take shortcuts with a demo. As Parmy Olson captures, “Google’s video made it look like you could show different things to Gemini Ultra in real time and talk to it. You can’t.”

CIOs grapple with the ethics of implementing AI

Even with a team focused on AI, identifying risks and understanding how the organization intends to use AI both internally and publicly is challenging, McIntosh says. Team members must also understand and address the inherent possibility of AI bias, erroneous claims, and incorrect results, he says. “Depending on the use cases, the reputation of your company and brand may be at stake, so it’s imperative that you plan for effective governance.” With that in mind, McIntosh says it’s critical that CIOs “don’t rush to the finish line.” Organizations must create a thorough plan and focus on developing a governance framework and AI policy before implementing and exposing the technology. Identifying appropriate stakeholders, such as legal, HR, compliance and privacy, and IT, is where Plexus started its ethical AI process, McIntosh says. “We then created a draft policy to outline the roles and responsibilities, scope, context, acceptable use guidelines, risk tolerance and management, and governance,” he says. “We continue to iterate and evolve our policy, but it is still in development. We intend to implement it in Q1 2024.”

Accenture takes an industrialized approach to safeguarding its cloud controls

Accenture developed a virtual cloud control factory to support five major, global cloud infrastructure providers and enable reliable inventory; consistent log and alert delivery to support security incident detection; and predictable, stable, and repeatable processes for certifying cloud services and releasing security controls. The factory features five virtual "departments". There's research and development, which performs service certification, control definition, selection, measurement, and continual re-evaluation; the production floor designs and builds control; quality assurance tests the controls; shipping and receiving integrates controls with compliance reporting tools; and customer service provides support to users after a control goes live. "What we decided to do was centralize that cloud control development, get all the needs into one place, start organizing them in a way that we could run them through a factory and get them out there so people can use common controls, common architecture that had a chance of keeping up with [our engineers'] innovation sitting on top of the [major cloud platforms'] innovation," Burkhardt says

Pressure on Marketers Will Drive Three Key Data Moves in 2024

Data clouds help achieve that goal. In both time and expense, organizations can no longer afford to jump between different systems to try to make sense of what a customer wants and formulate a real-time response in the moment of interaction. With a CDP sitting directly on top of a data cloud, it is easier and less expensive to build a unique customer profile and then activate that profile across multiple systems. Organizations recognize that first-party data is a valuable asset and is the foundation for delivering a personalized customer experience (CX), but for too long business users have been stymied by complex, unintegrated marketing stacks and time-consuming data transformations. That approach to making data actionable -- turning data into insight -- is no longer sustainable when customers expect real-time, personalized experiences that are consistent across channels. ... Moving to a data cloud and coupling it with a CDP’s automated data quality and identity resolution addresses these issues head-on, and that trend will continue -- particularly for customer-facing brands that see a data cloud with an enterprise-grade CDP as a relatively fast, inexpensive way to monetize their customer data.

Initial Agile Requirements and Architecture Modeling

Talk to most agilists, and particularly the purists, and they’ll claim that they don’t do any modeling up front. This of course is completely false, they just use different terminology such as “populate the backlog” rather than initial requirements modeling and “identify a runway” instead of initial architecture modeling. Sigh. Some of the more fervent agilists may even tell you about the evils of big modeling up front which is why they choose to eschew anything that smells like up-front thinking. ... The goal of initial architecture modeling on an agile team is to identify what the team believes to be a viable strategy for building the solution. Sufficiency is determined by your stakeholders – Can you exhibit an understanding of the existing environment, and the future direction of your organization, and show how your proposed strategy reflects that? Your initial architecture model should be JBGE in that it addresses, at a high-level, the business and technical landscapes that your solution will operate within. This modeling effort is often led, not dictated, by the architecture owner on your team.

Why are IT professionals not automating?

25% of participants highlighted cost and resource as potential obstacles. They wonder if they need to create a custom solution and, if so, whether it’s cost-effective or cheaper to continue with manual maintenance. They are also concerned about the resources required to maintain an automated solution. 20% admit that they and their teams lack the knowledge or expertise to choose an automated solution. They are not familiar with automation in general or the specific requirements of automating their systems. The survey results clearly indicate that many IT professionals are not familiar with or don’t see the value of certificate automation. Or is it that they didn’t think about it enough? After all, certificates have been part of our IT infrastructure for a very long time, while they are not exciting, they do work, so why fix something that is not broken? Unfortunately, when the 90-day Google edict eventually becomes reality, it will increase the need for renewal/replacement of SSL/TSL certificates by four times (4X) the current pace. IT professionals may be underestimating the burden that it will put on their teams. 

How Could AI Be a Tool for Workers?

The benefits for companies designing and using AI systems are vast and readily apparent. Tools that can complete work in a fraction of the time at a fraction of the cost are a boon for the bottom line. “The main beneficiaries of the technology are global technology giants primarily based in the United States,” says Michael Allen, CTO of enterprise content management company Laserfiche. He points out that these companies have the resources to accrue the massive amounts of data required to train AI models. Companies that adopt these powerful AI models can leverage them to cut costs. Allen points out that many companies will likely use AI to shift away from outsourcing. “A lot of firms outsource mostly routine clerical work to places like India, and I believe that's going to be threatened or impacted significantly by AI that will be able to do that work faster and cheaper,” he says. The way that AI devalues entry-level work is already being seen. Stephanie Bell is a senior research scientist at the nonprofit coalition Partnership on AI, which created guidelines to ensure AI economic benefits are shared. She offers examples in the digital freelance market. 

Bryan Cantrill on AI Doomerism: Intelligence Is Not Enough

Cantrill had titled his talk “Intelligence is not enough: the humanity of engineering.” Here the audience realizes they’re listening to the proud CTO of a company that just shipped its own dramatically redesigned server racks. “I want to focus on what it takes to actually do engineering… I actually do have a bunch of recent experience building something really big and really hard as an act of collective engineering…” Importantly, the common thread for these bugs was “emergent” properties — things not actually designed into the parts, but emerging when they’re all combined together. “For every single one of those, there is no piece of documentation. In fact, for several of those, the documentation was actively incorrect. The documentation would mislead you ... Cantrill put up a slide saying “Intelligence alone does not solve problems like this,” presenting his team at Oxide as possessed of something uniquely human. “Our ability to solve these problems had nothing to do with our collective intelligence as a team…” he tells his audience. “We had to summon the elements of our character. Not our intelligence — our resilience.”

Quote for the day:

“I'd rather be partly great than entirely useless.” -- Neal Shusterman

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