CIO CIO

The data that data scientists analyze draws from many sources, including structured, unstructured, or semi-structured data. The more high-quality data available to data scientists, the more parameters they can include in a given model, and the more data they will have on hand for training their models. Structured data is organized, typically by categories that make it easy for computers to sort, read, and organize automatically. This includes data collected by services, products, and electronic devices, but rarely data collected from human input. Website traffic data, sales figures, bank accounts, or GPS coordinates collected by your smartphone — these are structured forms of data. Unstructured data, the fastest-growing form of data, comes more likely from human input — customer reviews, emails, videos, social media posts, etc. This data is more difficult to sort through and less efficient to manage with technology, thus requiring bigger investment to maintain and analyze. Businesses typically rely on keywords to make sense of unstructured data to pull out relevant data using searchable terms. source

“Tech firms, especially those involved in AI training and inference, may experience delays and higher costs in acquiring these essential components,” Rawat said. “Similarly, server and PC chip shortages are exacerbated by restrictions on chipmaking tools, making it harder for Chinese manufacturers to produce advanced chips for servers and high-performance systems, potentially leading to delays or reliance on less advanced nodes.” The resulting supply constraints could drive up chip prices, squeezing profit margins for enterprise technology companies or increasing costs for customers, ultimately impacting competitiveness in the market. To navigate these challenges, firms may be forced to diversify their supply chains, identify alternative suppliers, and adapt procurement strategies. However, finding replacements for advanced semiconductors may be costly and difficult, further raising operational expenses. source

Keeping gen AI in check Despite internal excitement around the platform, Muthukrishnan says AI governance remained top-of-mind. The company adapted its Ally Technology Operating Model (ATOM) for gen AI. The model has five core pillars that guide the introduction of all new technology and provide a roadmap for assessments, evaluations, and approvals: Discover, Ideate, Elaborate, Execute, and Measure. The company also established an AI Working Group to work in conjunction with its existing Governance Group. Together, they formed an internal team of professionals in financial service fields including regulatory compliance, risk management, and audit, among others. The team reviews and advises on gen AI use cases. To help guide the team on implementation of both classic AI and gen AI, the AI Working Group also developed the Ally AI Playbook to empower Ally’s business lines to explore AI use cases, plan for pilot programs, and move them into production in a responsible, considered way. Muthukrishnan says the playbook creates a common language of understanding across the company. source

 “The strong resiliency posture allows two out of three regions to fail without customer impact, but geo-optimized routing also improved our overall customer experience,” Beer said. “Our strong partnership with AWS ensures that the infrastructure stack is frequently and automatically refreshed to improve our risk and resiliency posture and meet our security controls.” The cloud advantage JPMorgan Chase’s cloud journey with AWS began in 2020, and the launch of its consumer bank Chase in the UK was “built from the ground up on AWS,” Beer said. In 2023, the company had nearly 1,000 applications running on AWS, including core services such as deposits and payments. JPMorgan has also made use of AWS Graviton processors as part of the tightknit partnership. The 225-year-old company runs about 50% of all e-commerce transactions in the US, playing a major role during Black Friday and Cyber Monday mega sales this past weekend. “This is why we have been reinventing the way we build global banking and payments infrastructure, and why we’ve pushed the element of the art of the possible in the cloud,” she said. source

In today’s data-driven world, large enterprises are aware of the immense opportunities that data and analytics present. Yet, the true value of these initiatives is in their potential to revolutionize how data is managed and utilized across the enterprise. However, a significant challenge persists: harmonizing data systems to fully harness the power of AI. According to a recent Salesforce study, 62% of large enterprises are not well-positioned to achieve this harmony, with 80% grappling with data silos and 72% facing the complexities of overly interdependent systems. For chief information officers (CIOs), the lack of a unified, enterprise-wide data source poses a significant barrier to operational efficiency and informed decision-making. To overcome this, many CIOs originally adopted enterprise data platforms (EDPs)—centralized cloud solutions that delivered insights quickly, securely, and reliably across various business units and geographies. Now, EDPs are transforming into what can be termed as modern data distilleries. These distilleries refine raw data while also embedding pre-built and custom components directly into the data lifecycle. This shift streamlines operations, enhances business insights, and unlocks the full potential of data. Why data distilleries are a game-changer: Insights from the insurance industry Traditionally, managing data in sectors like insurance relied on fragmented systems and manual processes. For instance, in claims management, insurers would assess claims based on incomplete, poorly cleaned data, leading to inaccuracies in evaluating claims. Take, for example, a recent case with one of our clients. They had an AI model in place intended to improve fraud detection. However, the model underperformed, and its outputs showed discrepancies compared to manual validations. An analysis uncovered that the root cause was incomplete and inadequately cleaned source data, leading to gaps in crucial information about claimants. This issue resulted in incorrect risk assessments, where high-risk claims were mistakenly approved, and legitimate claims were wrongly flagged as fraudulent. Today, the introduction of data distilleries represents a significant departure from this conventional approach. These distilleries streamline data management by providing a unified, high-quality source of enterprise data. By integrating and refining data through these modern solutions, insurers can enhance the accuracy of risk assessments, reduce claims payout time by over 50%, and boost operational efficiency by more than 30%. To understand the impact of data distilleries, it’s essential to first recognize traditional data management challenges. Historically, insurers struggled with fragmented data sources, leading to inefficient data aggregation and analysis. Integrating advanced technologies like genAI often requires extensively reengineering existing systems. This approach consumed considerable time and resources and delayed deriving actionable insights from data. Today, data distilleries revolutionize this process by providing a centralized platform that streamlines data aggregation, facilitates access to genAI modules, and supports self-service data consumption in the cloud. Instead of overhauling entire systems, insurers can assess their API infrastructure to ensure efficient data flow, identify critical data types, and define clear schemas for structured and unstructured data. Incorporating custom knowledge graphs, enriched with domain expertise, further optimizes data consolidation. This shift allows for enhanced context learning, prompt augmentation, and self-service data insights through conversational business intelligence tools, as well as detailed analysis via charts. By focusing on these key factors, insurers can leverage the benefits of data distilleries, transforming raw data into valuable business intelligence and modernizing their operations. Driving operational efficiency and competitive advantage with data distilleries As organizations increasingly adopt cloud-based data distillery solutions, they unlock significant benefits that enhance operational efficiency and provide a competitive edge. Consolidating data and improving accessibility through tenanted access controls can typically deliver a 25-30% reduction in data storage expenses while driving more informed decisions. For insurers, actuarial teams can cut data preparation time from 8-10 days to just one. Claims processing can be reduced from 35-40 days to about a week. Selecting the right data distillery requires consideration. When evaluating options, prioritize platforms that facilitate data democratization through low-code or no-code architectures. These tools empower users with sector-specific expertise to manage data without extensive programming knowledge. Features such as synthetic data creation can further enhance your data strategy. Effective data governance and quality controls are crucial for ensuring data ownership, reliability, and compliance across the organization. A robust data distillery should integrate governance, modeling, architecture, and warehousing capabilities while providing comprehensive oversight aligning with industry standards and regulations. This approach minimizes risks, maintains customer trust, and ensures the delivery of reliable insights. From an implementation standpoint, choose a cloud-based distillery that integrates with your existing cloud infrastructure. The ideal solution should be scalable and flexible, capable of evolving alongside your organization’s needs. Opt for platforms that can be deployed within a few months, with easily integrated AI and machine learning capabilities. This ensures your organization effectively utilizes data, scales effortlessly, and stays agile and adaptable. Visit EXL’s website for more information on transforming processes with data. Ankur Jain, vice president of data, analytics & AI at EXL, a leading data analytics and digital operations and solutions company. source

4. Pizza algorithm competition  Pizza parties are particularly popular with HR departments in the USA when it comes to supposedly great benefits. To make this more interesting for local techies, developers could combine their passion for algorithms with their love of pizza once a month. The pizza algorithm competition is all about developing the perfect algorithm that determines the ultimate pizza — based on calories, toppings, delivery time and, of course, personal taste. The winner is rewarded with a 30-day pizza flat rate.  5. Official BRB clause  Sometimes you just need a break — and you need it now. With the official “BRB Clause,” developers would have the right to simply say “BRB” (Be Right Back) to critical requests or spontaneous meetings and withdraw for an indefinite period. No manager is allowed to ask where they are or what they are doing. An ideal solution to avoid the next slack bomb.  6. Headhunter defense spray  The job market has been developing in favor of employees for some time now and developers in particular are in high demand. This has the unpleasant side effect that headhunters are regularly on the mat for developers. What if there was a spray that protected developers from the constant onslaught of headhunters?  source

We workshopped it. We brought together 42 people from across the globe, from different positions, to detail the business capabilities, our pain points and what ERP would mean to Novanta. We highlighted all of those through many sessions, and then we started bringing different vendors in to see which software would work for us, moving forward to the integrator and so on. And as life happened between 2020 and now, here we are in 2024, kicking it off. It’s been a long journey, but what that says is don’t ever give up, because business is based on the economy and is based on business decisions.  That doesn’t mean that ERP or whatever other initiative you’re undertaking is not important. It’s just business. You have to be able to be flexible to move and pivot with the business.  How to improve the relationship between the CIO, the CEO and the board of directors  This might come as a surprise for some readers: Be honest. Tell the whole story. Because if you try to change the storyline, the next time you meet, the reaction might be ‘but that’s not what you told us last time.’ So, always be honest.  source