Technology Tales

The integration of data science and artificial intelligence is transforming biometrics careers, with employers now valuing candidates who possess hybrid skills, are familiar with newer platforms and can adapt to complex data environments. As organisations adopt more automated systems and predictive modelling tools, traditional biometric roles are being redefined, with a greater emphasis on interpretation, validation and system-level oversight. Biometrics teams must be able to work alongside automated systems, validate outputs and ensure that data meets regulatory standards, with skills such as programming fluency, experience with cloud tools and familiarity with machine learning libraries becoming increasingly important. Employers must prioritise candidates who understand regulated systems and can support traceability and inspection readiness and should provide training on audit trail review, output validation and documentation of overrides to upskill their teams. Ultimately, the most effective biometrics teams will combine strong analytical skills with a clear understanding of how automated outputs must be validated, interpreted and documented to meet regulatory standards.

Large language models are inherently non-deterministic, meaning they can produce different responses to the same input, which can lead to errors and inconsistencies. This lack of determinism can be problematic in enterprise software applications where reliability is crucial. To mitigate this issue, developers can implement measures such as sanitising inputs and outputs, observing the process as much as possible and ensuring that processes run once and only once. Additionally, using durable execution technologies can help save progress in workflows and prevent repeated calls to external services. By introducing these controls, developers can make large language models more reliable and trustworthy, which is essential for building robust enterprise software applications that organisations can rely on.

SingleStore is a distributed database platform built for low-latency SQL, designed to handle high user concurrency and petabyte-scale datasets while delivering fast query performance across analytics, transactions and search within a single unified engine. The platform includes an AI capability called Aura, which encompasses an AI-powered analyst tool that converts natural language into SQL queries, alongside functions for machine learning and large language model integration, enabling tasks such as sentiment analysis, summarisation and data classification directly within SQL.

The platform is used by a broad range of major organisations including Adobe, Cisco, Goldman Sachs and NVIDIA, with users citing its performance, ease of use and cost-effectiveness as key strengths. SingleStore integrates with existing data infrastructure and is positioned as a solution for industries with demanding real-time data requirements, including financial services, media and high-tech sectors.

Teradata is a technology company offering an AI and data platform designed to help large enterprises convert organisational knowledge into autonomous, real-time business decisions. Its platform combines agentic AI, connected data and scalable analytics to serve industries including financial services, healthcare, manufacturing, retail and telecommunications. The company counts among its clients ten of the top ten global banks, seven of the top ten airlines and seven of the top ten telecommunications providers.

Notable examples of its work include helping Mitsui Sumitomo Insurance increase contract rates by 250% and supporting OSF HealthCare in achieving a 520% increase in patient engagement. Teradata operates through a flexible, consumption-based pricing model and delivers its services via cloud, on-premises or hybrid deployments, supported by a partner ecosystem that includes major technology providers such as Microsoft, Google, AWS and NVIDIA.

Decisions in the Cloud from SAS

SAS hosts an ongoing webinar series aimed at data, analytics and IT professionals, focusing on AI, cloud computing and deploying analytical models for improved outcomes. Built around the latest release of SAS Viya, the series covers topics including future AI trends, driving business value and the integration of SAS Viya with Microsoft Azure. Featured speakers include futurist Bernard Marr, IDC Vice President of AI Research Ritu Jyoti and SAS Chief Technology Officer Bryan Harris, alongside a broader group of SAS product managers, advisors and marketing directors. The content is available on demand in short segments and is designed to serve a range of professionals, from data scientists and developers through to business analysts and executives.

SAS Viya

A cloud-native data and AI platform designed to streamline the development and deployment of analytical models, SAS Viya offers tools that enhance collaboration across teams, reduce cloud costs and improve productivity through features such as automated model governance, real-time data processing and integrated decision-making capabilities. It supports industries by enabling faster insights, reducing diagnostic and operational delays and improving accuracy in areas like fraud detection and customer management, with organisations reporting significant efficiency gains and measurable business outcomes. The platform is positioned as a trusted solution by numerous industry leaders, with a focus on transparency, scalability and compliance, and has been recognised for its comprehensive approach to data science and machine learning over multiple years.

Microsoft Azure and SAS

The collaboration between SAS and Microsoft Azure combines advanced analytics, artificial intelligence and cloud infrastructure to deliver scalable, secure solutions for enterprises. This partnership enables organisations to leverage deep integrations, intuitive tools for data analysis and robust security measures, supporting applications across industries such as healthcare, banking and manufacturing.

Case studies highlight its use in improving patient outcomes, reducing recidivism and optimising logistics, while the platform's flexibility allows for migration of existing workloads and tailored deployment models. By utilising Azure's infrastructure, SAS provides tools that enhance decision-making, streamline operations and adapt to varying organisational needs through cloud-based analytics and AI capabilities.

The Quartz guide to bad data is an extensive resource that helps journalists and data users recognise and address frequent issues found in real-world datasets. It details a wide variety of common data problems, such as missing or duplicated values, inconsistent spellings, ambiguous fields, problematic categorisations, and undocumented origins. The guide categorises issues according to whom is best placed to resolve them: the user, the data provider, an external expert, or a programmer. It also offers guidance for dealing with challenges like human data entry errors, non-random or biased samples, unclear margins of error, manual editing, inflation, seasonal variations, and manipulation of timeframes or reference points. More complex problems, such as those involving untrustworthy sources, opaque collection methods, unrealistic precision, outliers, misleading indices, statistical manipulation, or poorly aggregated data, may require the input of specialists or programmers. Overall, the guide emphasises a careful, questioning approach to data to help prevent mistakes and ensure more reliable analysis and reporting.

Elluminate Clinical Data Cloud from eClinical Solutions is a cloud-based platform that integrates various data streams, standardises complex information, and provides analytics capabilities, supporting decision-making throughout the clinical research process. It consolidates clinical and operational data into a single repository, eliminating traditional data silos and facilitating cross-functional collaboration. With built-in automation and study-agnostic machine learning, the platform supports AI integration, optimising data flow from initial acquisition to regulatory submission. The platform includes tools like the Elluminate Mapper, which allows non-technical users to perform intricate data transformations needed for regulatory compliance.

A blog post from Dataiku in November 2024 evaluates the performance of ChatGPT two years after its release, comparing its responses to those of AI professionals surveyed in May 2024. The survey involved 400 senior AI professionals from globally recognised companies, focusing on AI deployment trends. ChatGPT was tested with five questions presented to these AI leaders to assess its knowledge. The analysis revealed that large organisations typically adopt a Hub & Spoke or Centralised Center of Excellence model for AI initiatives, with most achieving a $2-5 return on each $1 spent on AI and data science. Key barriers hindering AI value include access to quality data and a shortage of data talent. ChatGPT achieved a score of 3.15 out of 5 in the test, demonstrating a close alignment with the survey findings and highlighting its potential as a useful tool for understanding industry trends, despite some nuances it may miss.