Data & Analytics

Legacy System Modernization: A Practical Framework

This article reflects insights from Sphere's engineering and consulting teams, drawing on 20+ years of experience modernizing enterprise platforms. Our approach is grounded in real project outcomes — 80+ applications optimized, 92% improvement in deployment speed, and 2x faster feature delivery after removing legacy bottlenecks.

The Rise of Physical AI: What Actually Works and What You Need to Know

Physical Intelligence raised $600 million at a $5.6 billion valuation for software that acts as a universal brain for robots. The hype is real, but so is the gap between lab demos and production reality. We break down what actually works in Physical AI today, the three hard problems nobody's solving yet, and why investors are betting billions on robot brains instead of robot bodies.

LLM Observability: Jagged AI, Real Economics, and the Work of Making It Real

LLMs aren’t “bad” or “overhyped” – they’re jagged: impressive on benchmarks, brittle in real workflows. This article explains why that gap shows up as real cost in production, and why LLM observability is the foundation for turning capability into predictable throughput. You’ll see how observability, evaluation-driven development, guardrails, RAG, and agentic checkpoints work together to make GenAI reliable, governable, and worth scaling.

NetSuite SuiteQL Schema Changes: What Breaks First and How to Fix It

NetSuite’s shift to the NetSuite2.com SuiteAnalytics data source introduces a new SuiteQL schema—and it’s already breaking reporting, integrations, and warehouse pipelines. Tables and fields move, joins behave differently, and permissions tighten, so “just fix the query” turns into a whack-a-mole exercise across workbooks, scripts, ETL jobs, and iPaaS flows. This guide explains what changed, what fails first, and Sphere’s playbook to regain control: Assess → Stabilize → Modernize.

AI Memory vs. Context Understanding: The Next Frontier for Enterprise AI

Most enterprise AI failures in 2025 had nothing to do with model quality. They failed because the systems didn’t understand context — who the user was, what problem they were solving, and how information related across departments and data silos. Adding more “memory” didn’t fix it. Persistent chat logs and vector databases only stored facts; they didn’t create meaning. The next generation of enterprise AI must treat context as a living system: continuously curated, governed, and shared across every model and agent in the organization. When context becomes a core design principle, AI stops guessing and starts reasoning. It stops recalling text and starts connecting knowledge. That’s when ROI appears — not from bigger models, but from smarter architectures that integrate data, identity, and governance into every answer.

Predictive Maintenance in Manufacturing: IoT Data to AI-Driven Cost Savings

Predictive maintenance is no longer a theory — it’s how modern manufacturers are keeping production lines running. By combining IoT sensor data with AI analytics, companies can predict equipment failures before they happen, cutting unplanned downtime by up to 50% and reducing maintenance costs by a quarter. In this article, Sphere explains how to move from reactive fixes to proactive intelligence — and what it takes to turn machine data into measurable ROI.

Contact Center Transformation and Modernization: From Cost Center to Loyalty Driver

Every interaction in your contact center shapes customer trust. Too often, companies treat it as a cost to cut rather than a strategic driver of loyalty and growth. This article explores how modernization—powered by AI, cloud migration, CRM optimization, and data unification—turns your contact center into a competitive advantage.

Automated Business Intelligence: How to Move Beyond Dashboards

Most dashboards end up ignored. The future of business intelligence is not about prettier charts, but about real-time decision feeds, AI copilots, and automated actions that drive results. This article explores how companies are moving from being simply data-driven to truly data-powered.

Successful AI Adoption for Your Organization

AI succeeds when people trust it, understand it, and see it improve their work. This guide outlines Sphere’s approach to enterprise AI adoption—pairing domain leaders with data talent, making systems explain themselves, and focusing on the last mile that differentiates your business. From clear rules to partner-led delivery, learn how to build AI solutions that teams embrace and results that last.

How to Prepare Your Healthcare Data for LLMs (Without Breaking Compliance)

Large language models hold transformative potential for healthcare — from clinical summarization to real-time risk detection — but only if used responsibly. In this guide, we outline a step-by-step roadmap to prepare your healthcare data for LLM use without risking compliance violations. From tackling data silos to securing PHI, and from model fine-tuning to governance best practices, discover how to move from fragmented data to safe, AI-ready infrastructure. Plus, learn how Sphere Data Agent helps organizations deploy LLMs up to 3x faster while staying HIPAA-compliant.

Synthetic Data: Fake With Benefits

Synthetic data promises better privacy, faster experimentation, and scalable AI training — but only when done right. At Sphere, we’ve seen that the real differentiator isn’t the generation technique itself, but how and where it’s applied. In this article, we unpack what makes synthetic data valuable, when it works best, and what to look for in a partner.

AI in Logistics and Transportation: 25+ Use Cases

AI in logistics reshapes how fleets move, warehouses operate, and supply chains respond. In this guide, we break down 25+ real-world AI use cases solving everyday challenges for logistics and transportation leaders. From predictive maintenance and route optimization to warehouse automation and emissions tracking, each example speaks the language of COOs, CTOs, and supply chain execs.

How AI Is Transforming Tech Debt, Data Modernization, and the Future of Engineering — Insights from Alex Ter-Zakhariants

In this episode of SphereCast, Field CTO Alex Ter-Zakhariants breaks down what engineering teams actually face when bringing AI into real systems: tech debt, disorganized data, and infrastructure that wasn’t built to scale. From data modernization to AIOps to AI copilots, Alex shares a practical roadmap for building systems that can adapt, not just react. No hype, no shortcuts—just clear thinking about what makes engineering work in an AI-driven world.

Agentic AI for Enterprise Transformation

Agentic and multiagent AI systems are changing how companies work. Software agents can now make decisions, coordinate tasks, and learn from data. These systems are already solving real business problems. Companies use them in finance, customer service, and supply chain operations. Adoption is growing. Smart organizations start small but plan to scale. The goal is not to replace people. It is to free them from routine work and let them focus on what matters. This article explains how to begin, what to watch out for, and how to build a strong AI foundation.

Edge AI Computing Explained: Key Concepts and Industry Use Cases

AI doesn’t need to live in the cloud anymore. From oil rigs with spotty internet to store shelves that restock themselves, Edge AI is quietly revolutionizing how enterprises think, act, and compete. In this piece, we break down what Edge AI Computing really is, why it’s gaining traction now, and how smart organizations are using it to reduce latency, boost privacy, and make better decisions—right where the action happens. If you’re wondering how to stay ahead in a world full of data but short on time, this is your blueprint.

Engineering Data Management Without The Headaches

Data is the fuel of modern engineering. Yet many organizations still struggle with silos, outdated files, and fragmented systems that slow down progress and innovation. In this guide, we explore how to streamline engineering data management—from strategy and governance to tools and cloud infrastructure. Whether you're dealing with massive CAD files or real-time IoT streams, this article shows you how to get your data under control and working for you.

Digital Transformation: Tech Investments for 2025

Leon Ginsburg, CEO at Sphere, shares his perspective on how 2025 will be a defining year for digital transformation. From bridging talent gaps to leveraging AI-driven solutions, Ginsburg highlights the critical factors shaping success, the risks of falling behind, and why ROI-driven tech investments will set leaders apart from laggards.

Migration from Dropbox to Microsoft 365: A Journey for Enterprises

Is your team juggling disconnected tools for collaboration, storage, and communication? Discover how migrating from Dropbox to Microsoft 365 can revolutionize your workflows with integrated tools, enterprise-grade security, and unmatched scalability. Say goodbye to siloed systems and hello to seamless productivity.

How Predictive Analytics in Healthcare is Transforming Patient Outcomes

Predictive analytics in healthcare is revolutionizing patient care by providing data-driven insights that allow healthcare professionals to anticipate medical issues and make proactive decisions. By identifying at-risk patients, optimizing hospital resources, and preventing costly equipment breakdowns, predictive analytics plays a key role in improving patient outcomes and reducing healthcare costs. Explore how hospitals and healthcare providers are using this technology to deliver personalized treatments, reduce readmission rates, and streamline operations. Learn how predictive analytics is driving the future of healthcare by enhancing patient care and operational efficiency.

Digital Twins: Use Cases, Technologies, and More

Stepping into the future with Digital Twins technology offers businesses an unparalleled advantage in operational efficiency and strategic foresight. At Sphere, we specialize in crafting bespoke Digital Twins solutions that not only replicate but also enhance your physical assets through real-time data and predictive analytics. Dive deeper into how our innovative approaches are helping industries from manufacturing to healthcare not just compete but dominate in their sectors.

AI and IoT in Construction: Unlocking the Value

The construction industry is experiencing a technological revolution driven by AI and IoT. These advancements are transforming traditional practices, enabling more efficient, safe, and cost-effective project outcomes. Companies like PCL Construction, Lendlease, and Turner Construction are leading the way, leveraging IoT sensors and AI algorithms to optimize operations, enhance safety, and improve project management.

Reuters Digital Health 2024 Conference: Key Takeaways

In a significant move towards enhancing operational efficiency and patient care, the conference showcased how Generative AI is being adopted within clinical settings. Experts like Sarah McKinley MD demonstrated Dyna AI's impact in reducing clinical response times by 20%, heralding a new era of intelligent healthcare solutions. Simultaneously, discussions led by Jessica Hauflaire emphasized the critical role of integrating clinical data to support value-based care, ensuring a comprehensive approach to patient management.

Future of Insurance USA 2024: Sphere Partners' Key Learnings and Insights

At the Future of Insurance USA 2024 in Chicago, Sphere Partners gained deep insights into the evolving landscape of the insurance industry. Key discussions highlighted the significant impact of AI in transforming insurance processes, from claims handling to underwriting. With detailed case studies from industry leaders and our own advancements in Gen AI solutions, Sphere demonstrated its commitment to leveraging technology to enhance operational efficiency and customer interaction. This event underscored the essential role of innovative technology in overcoming industry challenges and shaping the future of insurance. Join us as we continue to drive forward with strategic tech initiatives designed to modernize the insurance sector and deliver superior service to our clients.

Robotic Process Automation: An Insurance for the Insurance Industry

The global insurance industry is in the midst of a significant transformation, driven by technological advancements and evolving consumer demands. As InsurTech startups and tech-savvy competitors redefine the landscape, traditional insurers are compelled to embrace digital innovation to remain competitive. In this dynamic environment, Robotic Process Automation (RPA) emerges as a pivotal tool, empowering insurers to enhance operational efficiency and agility in an increasingly digital world.

Sphere Heads to the Future of Insurance USA 2024 in Chicago

At Sphere, we offer a comprehensive suite of services designed to revolutionize the insurance industry. From generative AI that transforms client interactions to advanced data modernization strategies and legacy software updates, our solutions significantly enhance operational efficiency and customer satisfaction. Our expertise is demonstrated through our successful partnerships with leading insurers and an impressive client retention rate. Discover how our cutting-edge technologies and custom software solutions are setting new standards at the Future of Insurance USA 2024.

Data+AI Summit: Visiting the Partners

Sphere's leadership, Leon Ginsburg and Mario Schwarts, will be at the Data+AI Summit 2024 to gather insights and share their expertise on the evolving landscape of data and AI. From platform health checks to advanced data engineering strategies, their mission is to enhance Sphere's offerings and drive digital transformation for clients.

Optimizing Wealth Management: Leveraging Automation, Analytics, and AI for Better Client Outcomes

The wealth management industry is embracing artificial intelligence (AI) and automation to drive better client outcomes. As the Wealth Management EDGE conference approaches in Florida, industry leaders are focusing on AI's role in transforming wealth management, with Gartner projecting a 23.8% compound annual growth rate (CAGR) for AI in wealth management through 2027. Sphere's GenAI Readiness Program prepares wealth management firms to leverage AI through educational workshops, technology assessments, and pilot projects. Key applications of AI in wealth management include "next best action" recommendations, personalized financial planning, real-time market insights, and AI-powered customer interactions. The adoption of AI is poised to offer competitive advantages, with firms that invest in digital capabilities expected to thrive in this rapidly evolving sector.

How to Use Gen AI for Your Clients: The Guide for Service/Portfolio Companies

Adopting Data and AI Governance in Healthcare

The transformative potential of data and AI governance is still creating a buzz in modern healthcare. Despite this excitement, many practitioners are uncertain about practical implementation and its significance. Through my interactions with clients and colleagues, I have identified several key aspects to address these concerns. In this concise guide, I am excited to share these insights.

Transformative Tech: Walmart Boldly Integrates Generative AI Into the Workplace

As businesses across industries rush to understand and embrace AI to help streamline operations and create new opportunities and efficiencies, Walmart’s move makes it clear that organizations will need partners like Sphere to help synergize human expertise with AI capabilities.

M&A Success Is Impossible Without Proper Data Integration

Mergers and acquisitions (M&A) have become the catalyst of business growth. But, a successful M&A requires far more than getting the financial numbers right

Data is Not a Gold Mine

Every patient, test, scan, diagnosis, treatment plan, medical trial, prescription and final health outcome produces a data point that can help improve how we deliver care in the future.

Learn with Sphere: Business Analysis: Common Mistakes and Best Practices

BI Tools Comparison: Choosing the Right BI Tool For Your Business

Data Governance Is More Important Than Ever: Best Practices And Examples

To create a strong, well-supported data governance strategy, you need to secure internal buy-in across the organization. Data governance also allows companies to make the most of the data they hold.

Data Maturity Models - Comparing Approaches to Data Maturity

An understanding of data maturity can guide a company's transformation from being clueless about data to taking advantage of its full potential. In this post we compare different approaches to data maturity.