Daily Tech Digest - March 08, 2023

How AI can help find new employees

AI-based recruitment platforms can find "more diverse talent pools, and [offer] a more accurate approach to qualifying candidates by matching skills rather than on a job title match or other signal,” said Forrester Principal Analyst Betsy Summers. Some of the use cases for talent acquisition platforms are efficiency-oriented, since they’re used for interview scheduling, managing the candidate application process, assisting recruiters with follow-ups, and managing the applicant pipeline. Other platforms also focus on bias mitigation such as adjusting language in job descriptions and candidate communications to be more inclusive. Still others include remote video capabilities that automate early interviews. ... Chatbots are typically employed by recruitment platforms to engage job seekers and ask them about their interests and skills; the bots can then present candidates with open positions for which they’re most qualified to apply.

The EU digital strategy: The impact of data privacy on global business

First, companies may need to assess the impact of the EU digital strategy on their business and their business model and need to identify where changes are required and where additional care needs to be taken with respect to current processes. This specifically applies to the four acts concerning data governance, digital services, AI, and data. Second, companies may need to investigate the possibilities for the applicability of the acts within their organization. This includes possible access to markets that other competitors led in the past through their access to end-user data. Finally, as the EU digital strategy continues to evolve, organizations may be able to further collaborate with governing bodies on the interpretation of the regulations. Specifically, in the case of AI, there are several companies that may find it very challenging to work with their current model in the new guidance. ... Additionally, companies should revisit their current processes for data collection and AI. 

5 best practices for scaling AI in the enterprise

One of the most important challenges of implementing AI is defining the business problem the enterprise is trying to solve. As the saying goes, don’t end up with an answer that’s looking for a question. Simply deploying new forms of technology isn’t the right approach. Next, examine the issues and determine if AI is the best way to tackle the problem. There are other digital technologies well adapted to simple problems. To help ensure success, define the business issue clearly and determine what course to take at the outset — some may not need AI. In automation, the end-to-end process is disaggregated and divided into smaller parts. Each part is then digitized, and the parts are then reaggregated into the value chain. ... So, AI-based transformation is as much about designing a new operating model, cross-skilling the workforce and integrating it into upstream and downstream processes as it is about neural nets and model management. It’s important to note that AI in the enterprise is 20% about technology and 80% about people, processes and data.

Data Privacy: A Public Policy Challenge

In today’s world, improved computational capabilities have enabled businesses and public and private organizations to better structure their data in the form of huge databases and leverage analytics to generate business intelligence and contribute value creation. With these computational and analytical capabilities, there are increasing avenues to develop profiles of humans’ behavior around their purchasing, spending and consumption habits, their genetic profiling, their travel history, medical history, etc. While these capabilities add value to the human society, they also come with risks of intruding into individuals’ privacy. Unfortunately, the discourse around personal data is only centered around its protection from leakage or prevention from breach. However, the primary objective to safeguard the personal data is to ensure that such data are not processed to create a more inequitable society and bring about unfair outcomes. The amount of discrete data available today allows us to bring more nuance and innovations into public policy, therefore aiding in ironing out any imbalances within the society.

Why Database Administrators Are Rising in Prominence

“Currently, there’s a very disjointed relationship between DBAs and the business problem they are solving for customers,” Neiweem says. He points out DBAs are often the last touch point for customers, but this is changing as business and marketing leaders glean deeper insights from customer data and look to achieve personalization at scale. “It’s no longer effective to go through this disconnected channel to get answers about customer data,” he says. “DBAs are now moving into a consulting role where they can take data, analyze and action it, enabling marketing and other internal teams to build stronger relationships with customers through those data insights.” Arun Chandrasekaran, product manager for ManageEngine, adds DBAs are often the first link in the chain of acquiring IT tools. “While the decision-makers decide on what to buy, DBAs can influence their decision,” he says. “Since the responsibility of managing the data warehouse falls on DBAs, they work with the stakeholders to understand the business requirements.

Designing For Data Flow

Put simply, the bottlenecks in designs are being defined by the type and volume of data, and the speed at which it needs to be processed. “SoCs are getting bigger and more complex, fitting everything in the actual chip,” he said. “So data exchange, which used to happen at a system level, is now happening within the IC. This means efficient circuit design for data transfer is required to achieve the overall expected performance. The data flow design at the logic level is quite abstract. In the past, the chips were smaller and mostly driven by specific functionality, so there were only a few stages required to plan for data flow. With bigger chips, this has changed, and more effort is needed to understand the data sampling and placement of the appropriate functional modules next to each other, to achieve optimal data flow.” Data integrity also is becoming a challenge. In addition to crosstalk and various types of noise, which are prevalent at advanced nodes, there are a variety of aging effects that can appear over longer lifetimes, thermal mismatch between increasingly heterogeneous components, and latent defects that can become real defects as the amount of processing required on a chip or in a package increases.

Interacting with Machines through IoT and AI: A Revolution in Home and Workplace Technology

The seamless integration of IoT and AI has completely revolutionized the way we interact with machines, offering novel and innovative solutions for both homes and workplaces alike. With the aid of cutting-edge technologies like machine learning, deep learning algorithms, gesture control, and wearable devices, the potential of IoT and AI to create value across a range of applications is colossal. As these technologies continue to advance, the potential for further groundbreaking advancements in the future is undeniable. It is my sincere hope that this blog has been informative and engaging, offering you valuable insight into the current and future state of these two fields. That said, it is also important to remain cognizant of the potential ethical and privacy concerns that come with their widespread adoption. As with any rapidly-evolving technology, it is essential that we consider and address these concerns to ensure that the development and application of these technologies align with our societal values and principles.

How Skyscanner Embedded a Team Metrics Culture for Continuous Improvement

Changing Culture was probably the part that we put the most effort into, because we recognised that any mis-steps could be misinterpreted as us peering over folks’ shoulders, or even worse, using these metrics intended to signal improvement opportunities to measure individual performance. Either of those would be strongly against the way that we work in Skyscanner, and would have stopped the project in its tracks, maybe even causing irreversible damage to the project’s reputation. To that end we created a plan that focused on developing a deep understanding of the intent with our engineering managers before introducing the tool. This plan focused on a bottom-up rollout approach, based on small cohorts of squad leads. Each cohort was designed to be around 6 or 7 leads, with a mix of people from different tribes, different offices, and different levels of experiences, covering all our squads. The smaller groups would increase accountability, because it’s harder to disengage in a small group, and also create a safe place where people can share their ideas, learnings, and concerns.

Managing data is the key to better citizen services

As important as cyber-resilience is, there are also other issues associated with the unchecked growth of a data estate. Top of mind for public sector CIOs is keeping an eye of the purse-strings and being accountable to taxpayers for the money they spend. Massive amounts of data cost a similarly large amount of funding to maintain, says Mr Hatchuel. “You have to put data somewhere and managing the cost is very challenging for CIOs.” A modern data protection and management solution allows CIOs to manage their data estates in a cost-effective way, as well as keep it secure. The solution should also protect and manage external data sources which, in a contemporary environment, could be a new public cloud service. “Data can pop up anywhere, so you need a holistic solution able to look across the whole data estate and manage and understand different data sources. CIO’s are also advised to understand the Shared Responsibility model of most public cloud services; for the majority of providers, that burden falls to the customer.” 

4 ways for CIOs to strike a balance between operation and innovation

“Striking the right balance between innovation and operations is essential for any organization to succeed and stay competitive. Innovation is about exploring new ideas, embracing change, and striving for progress. On the other hand, operations consist of taking those ideas and making them a reality, efficiently utilizing resources, and ensuring that all the necessary steps are in place to deliver the desired result. ... “The load that IT organizations carry with snowballing technical debt has a direct and tangible drain on IT innovation. While it’s obvious on the surface, every dollar spent on technical debt is a dollar that IT cannot invest in innovation and transformation. Maintaining, securing, and operating critical but aging applications and infrastructure is a boat anchor that drags down innovation and must be addressed continuously by IT leadership, architects, and CTOs before it blows up in a disaster. Start by eliminating the 'kick the can down the road' strategy of ignoring technical debt; instead, prioritize actual application modernization investments that can break the pattern and open up innovation cycles as part of a continuous modernization strategy.”

Quote for the day:

"Great leaders go forward without stopping, remain firm without tiring and remain enthusiastic while growing" -- Reed Markham

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