September 14, 2024
Kannan Subbiah
FCA | CISA | CGEIT | CCISO | GRC Consulting | Independent Director | Enterprise & Solution Architecture | BU Soft Tech | itTrident | Former Sr. VP & CTO of MF Utilities
Just as important as the front-end experience are the back-end operations that keep and build the customer relationship. Value-added digital services that deliver back-end operational excellence can improve the customer experience through better customer service, improved security and more. Emerging tech like artificial intelligence can substantially improve how companies get a clearer view into their operations and customer base. Take data flow and management, for example. Many executives report they are swimming in information, yet around half admit they struggle analyzing it, according to research by Paynearme. While data is important, the insights derived from that data are key to the conclusions executives must draw. Maintaining a digital record of customer information, transaction history, spend behaviors and other metrics and applying AI to analyze and inform decisions can help companies provide better service and protect their end users. They can streamline customer service, for instance, by immediately sourcing relevant information and delivering a resolution in near-real time, or by automating the analysis of spend behavior and location data to shut down potential fraudsters.
In a remote work setting, one of the biggest challenges for organizations remains in streamlining of operations. For a scattered team, the implementation of AI emerges as a revolutionary tool in automating shift and rostering using historical pattern analytics. Historical data on staff availability, productivity, and work patterns enable organizations to optimise schedules and strike a perfect balance between operational needs and employee preferences. Subsequently, this reduces conflicts and enhances overall work efficiency. Apart from this, AI analyses staff work duration and shifts that further enable organizations to predict staffing needs and optimise resource allocation. This enhances capacity modelling to ensure the right team member is available to handle tasks during peak times, preventing overstaffing or understaffing issues. ... With expanding use cases, AI-powered facial recognition technology has become a critical part of identity verification and promoting security in remote work settings. Organisations need to ensure security and confidentiality at all stages of their work. In tandem, AI-powered facial recognition ensures that only authorized personnel have access to the company’s sensitive systems and data.?
Adapting to the DPDPA will require tailored approaches, as different sectors face unique challenges based on their data handling practices, customer bases, and geographical scope. However, some fundamental strategies can help businesses effectively navigate this new regulatory landscape. First, conducting a comprehensive data audit is essential. Businesses need to understand what data they collect, where it is stored, and who has access to it. Mapping out data flows allows organizations to identify risks and address them proactively, laying the groundwork for robust compliance. Appointing a Data Protection Officer (DPO) is another critical step. The DPO will be responsible for overseeing compliance efforts, serving as the primary point of contact for regulatory bodies, and handling data subject requests. While it’s not yet established whether it’s mandatory or not, it is safe to say that this role is vital for embedding a culture of data privacy within the organisation. Technology can also play a significant role in ensuring compliance. Tools such as Unified Endpoint Management (UEM) solutions, encryption technologies, and data loss prevention (DLP) systems can help businesses monitor data flows, detect anomalies, and prevent unauthorized access.?
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To prevent potential issues, it is your responsibility to maintain a domain model that is uncomplicated and accurately reflects the domain. This diligent approach is important to focus on modeling the components of the domain that offer strategic importance and to streamline or exclude less critical elements. Remember, Domain-Driven Design (DDD) is primarily concerned with strategic design and not with needlessly complexifying the domain model with unnecessary intricacies. ... It's crucial to leverage Domain-Driven Design (DDD) to deeply analyze and concentrate on the domain's most vital and influential parts. Identify the aspects that deliver the highest value to the business and ensure that your modeling efforts are closely aligned with the business's overarching priorities and strategic objectives. Actively collaborating with key business stakeholders is essential to gain a comprehensive understanding of what holds the greatest value to them and subsequently prioritize these areas in your modeling endeavors. This approach will optimally reflect the business's critical needs and contribute to the successful realization of strategic goals.
With a new data-friendly CIO at the helm, Hidalgo was able to assemble the right team for the job and, at the same time, create an environment of maximum engagement with data culture. She assembled discussion teams and even a data book club that read and reviewed the latest data governance literature. In turn, that team assembled its own data governance website as a platform not just for sharing ideas but also to spread the momentum. “We kept the juices flowing, kept the excitement,” Hidalgo recalled. “And then with our data governance office and steering committee, we engaged with all departments, we have people from HR, compliance, legal product, everywhere – to make sure that everyone is represented.” ... After choosing a technology platform in May, Hidalgo began the most arduous part of the process: preparation for a “jumpstart” campaign that would kick off in July. Hidalgo and her team began to catalog existing data one subset of data at a time – 20 KPIs or so – and complete its business glossary terms. Most importantly, Hidalgo had all along been building bridges between Shaw’s IT team, data governance crew, and business leadership to the degree that when the jumpstart was completed – on time – the entire business saw the immense value-add of the data governance that had been built.
The researchers observed that varied practice, not repetition, primed older adults to learn a new working memory task. Their findings, which appear in the journal Intelligence, propose diverse cognitive training as a promising whetstone for maintaining mental sharpness as we age. “People often think that the best way to get better at something is to simply practice it over and over again, but robust skill learning is actually supported by variation in practice,” said lead investigator Elizabeth A. L. Stine-Morrow ... The researchers narrowed their focus to working memory, or the cognitive ability to hold one thing in mind while doing something else. “We chose working memory because it is a core ability needed to engage with reality and construct knowledge,” Stine-Morrow said. “It underpins language comprehension, reasoning, problem-solving and many sorts of everyday cognition.” Because working memory often declines with aging, Stine-Morrow and her colleagues recruited 90 Champaign-Urbana locals aged 60-87. At the beginning and end of the study, researchers assessed the participants’ working memory by measuring each person’s reading span: their capacity to remember information while reading something unrelated.