October 18, 2023

Implementing an Effective Data Strategy

What challenges must a data strategy overcome: Creating a data culture? Building the data business case? Or fixing data issues? Martin Davis, CIO of Southern Company, said, “It is all of those, but it starts with data ownership. Once you have the right business ownership, you can work on the culture, the business case, and other things.” Jim Russell, CIO for Manhattanville College, claimed that with ownership established, “What most organizations are lacking are foundational skills in the workforce. As competing knowledge requirements have intensified, fewer employees seem to have data literacy or data fluency. For this reason, I’ve been pushing data literacy as a foundational requirement with expertise resulting in data fluency which means different things in different campus communities. ... Obviously, a smart data strategy comes from business and digital strategy. For this reason, Russell said, “It is important to start with a common vision that spans data products and services. With this, CIOs should help teams define vision and create clear scaffolding that overarching vision...”

The evolution of deception tactics from traditional to cyber warfare

There are many concerns when determining the next steps in responding to a cyber incident or attack that require careful navigation of ethics, further underscoring the importance of international governance and regulations. An escalatory response to a cyberattack, such as a “hack back” or “attack back,” raises legal and ethical questions if such action could lead to a larger conflict. Because cyber attackers are becoming more skilled at hiding their true identities, there is indeed cause for concern about whether a response could lead to retaliatory actions and collateral damage against innocent parties. Additionally, the intentions of the original attacker could be misidentified by the victim, leading to disproportionate or unneeded attacks. ... This necessitates a cyber defense strategy that doesn’t just block or react, but one that is also designed to seek out attackers’ motives and identities. It’s a tale as old as time in the military world—if you understand your opponent’s motives, you have the upper hand.

Developers and the AI Job Wars: Here's How Developers Win

“Software development is less about writing software and more about understanding the problem you are trying to solve,” says Louis Lang, CTO and co-founder of Phylum. “While the likes of ChatGPT and Copilot might make the writing process quicker, it has a long way to go before it can reason through a novel problem domain. Making development faster with AI only applies to scaffolding new projects and writing well-trodden code and even this seems problematic from time to time. If you try to produce something that requires deep expertise, AI will not help you.” But what jobs it does destroy, it replaces with new roles. For example, AI is itself software and as such requires developers. “With the rise of generative AI, software developers play a pivotal role in designing, building, and maintaining the underlying infrastructure that powers AI applications,” says Adam Prout, CTO of SingleStore, a cloud-native database. “Their position is vital to implementing algorithms, creating data pipelines, and optimizing models in close collaboration with data scientists and machine learning engineers. The expertise of a software developer is integral to bringing AI projects from conceptualization to real-world deployment.”

It’s time for cloud tech to meet operational tech at industrial sites

Industrial sites’ challenges can be daunting, but advances in cloud computing—particularly in security and edge computing—have come a long way. Some industrial sites are already adopting standards in site data collection, such as OPC Unified Architecture (OPC UA), a machine-to-machine communication protocol that allows control systems to exchange data securely and consistently. ... Edge computing can store a subset of data at a site, and in some cases can even provide cloud compute capabilities, thus allowing sites to continue to use cloud capabilities even if network connectivity is lost. Of course, the corresponding edge computing architectures—the amount of computing needed to store and process data before sending it to the cloud—will vary based on the size of the connectivity gap, the amount of data to be transferred, and the use of digital assets, such as sensors and recording devices. Edge computing also manages data’s return trip from the cloud to sites, making cloud-dependent, on-site applications faster and more reliable, since it reduces reliance on network connectivity.

Opportunities and Limitations of Deploying Large Language Models in the Enterprise

The progress we’ve seen in the last few months is nothing short of impressive. While natural language understanding and processing is not net-new, it’s now much more accessible. Not to mention that models have gone from 0 to 60 in terms of depth and capabilities. But, for many CIOs, the value may not be immediately obvious. Many organizations have been slashing budgets in the last year and making blind investments is not in their agenda. ... Large Language Models (LLMs) like GPT-4 are based on neural networks, which are inherently probabilistic in nature. This means that given the same input, they might produce slightly different outputs each time due to the randomness in the model’s architecture or during the training process. This is what we mean when we say LLMs are “non-deterministic.” ... Despite these challenges, there are ways to manage the non-deterministic nature of LLMs, such as using ensemble methods, applying post-processing rules or setting a seed for the randomness to get repeatable results.

How to get internal employee poaching right

Even if your company has an open culture, it’s critical to develop cooperative relationships with managers in other departments because losing a top performer isn’t easy for anyone. Nevertheless, if a user department manager recognizes an employee’s interest in transfering to IT, and you have a strong working relationship with that manager, internal hiring can go a lot more smoothly. ... At some companies, poaching an employee from another departments is considered unethical and underhanded. Regardless, internal employee poachingcan certainly be an issue if you actively recruit another department’s employee without letting the other department manager know. It is vital to know up front the actions and behaviors that are acceptable within your company before you start recruiting another department’s employee. For instance, in some cases, it is acceptable for an employee to be “loaned out” from one department to another for the duration of a specific one-off project. Such a policy helps provide temporary resources for projects while enabling employees on loan to gain knowledge and cross-train in another discipline.?

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