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Yahoo
20-05-2025
- Business
- Yahoo
Swiss GRC Day 2025: Governance, Risk and Compliance as a Strategic Imperative
LUCERNE, Switzerland, May 20, 2025--(BUSINESS WIRE)--Swiss GRC Day 2025 brought together specialists and managers from the DACH region at Zurich Airport. The conference offered insights into current developments in governance, risk and compliance (GRC) – from cyber risks and regulatory requirements to artificial intelligence and leadership culture. "GRC is not a control instrument, but a strategic management system," emphasized Besfort Kuqi, co-founder and CEO of Swiss GRC, at the opening of the Swiss GRC Day 2025. Companies must actively prepare for uncertainties, use technological innovations responsibly and strengthen their cultural resilience. The opening keynote speech by Nikolai Tsenov, Head Strategy & Business Development at Swiss GRC, provided a historical look back at the Lisbon earthquake of 1755. He illustrated how decisive action, strategic leadership and courageous reforms formed the basis for resilience and governance centuries ago - principles that are more relevant today than ever. The program offered a strong combination of theory, strategy and practice. Christian Weiss, Head of Enterprise Risk at Skyguide, described how a temporary airspace shutdown over Switzerland could be managed with clear decision-making processes and practiced crisis structures. Marc Etienne Cortesi, Group CISO of the Baloise Group, demonstrated the extent to which digital dependencies have increased. Using a cyberattack as an example, he explained how vulnerable supply chains are - and how the NIST C-SCRM Framework can help to prioritize risks and build resilience. Technological change and its ethical and regulatory impact were also in focus. Marinela Bilic-Nosic, Partner at EY Germany, advocated for company-wide AI governance, especially for autonomous systems. David Rosenthal, Partner at VISCHER AG, showed how the EU AI Act can be applied in an innovation-friendly way through clear responsibilities, staged approvals, and trained decision-makers. Marc Gröflin, Head of Internal Audit at the Swiss National Bank, presented a combined assurance model that creates more transparency and real added value by closely coordinating audit, risk, ICS and compliance. Sandra Middel, Chief Ethics and Compliance Officer of the Axpo Group, provided the final impulse. She focused on the role of corporate culture and emphasized the importance of lived values, responsibility in everyday life and leadership by example. The contributions made one thing clear: GRC is a networked, strategic management approach – and a key success factor for organizations in a complex, dynamic world. View source version on Contacts Yahya Mohamed MaoHead Marketing & Communications Swiss GRC AG marketing@ Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Business Wire
20-05-2025
- Business
- Business Wire
Swiss GRC Day 2025: Governance, Risk and Compliance as a Strategic Imperative
LUCERNE, Switzerland--(BUSINESS WIRE)-- Swiss GRC Day 2025 brought together specialists and managers from the DACH region at Zurich Airport. The conference offered insights into current developments in governance, risk and compliance (GRC) – from cyber risks and regulatory requirements to artificial intelligence and leadership culture. Swiss GRC Day 2025 brought together leading minds in governance, risk, and compliance from across the DACH region. The conference highlighted how GRC is evolving from a control function into a strategic driver. Share 'GRC is not a control instrument, but a strategic management system,' emphasized Besfort Kuqi, co-founder and CEO of Swiss GRC, at the opening of the Swiss GRC Day 2025. Companies must actively prepare for uncertainties, use technological innovations responsibly and strengthen their cultural resilience. The opening keynote speech by Nikolai Tsenov, Head Strategy & Business Development at Swiss GRC, provided a historical look back at the Lisbon earthquake of 1755. He illustrated how decisive action, strategic leadership and courageous reforms formed the basis for resilience and governance centuries ago - principles that are more relevant today than ever. The program offered a strong combination of theory, strategy and practice. Christian Weiss, Head of Enterprise Risk at Skyguide, described how a temporary airspace shutdown over Switzerland could be managed with clear decision-making processes and practiced crisis structures. Marc Etienne Cortesi, Group CISO of the Baloise Group, demonstrated the extent to which digital dependencies have increased. Using a cyberattack as an example, he explained how vulnerable supply chains are - and how the NIST C-SCRM Framework can help to prioritize risks and build resilience. Technological change and its ethical and regulatory impact were also in focus. Marinela Bilic-Nosic, Partner at EY Germany, advocated for company-wide AI governance, especially for autonomous systems. David Rosenthal, Partner at VISCHER AG, showed how the EU AI Act can be applied in an innovation-friendly way through clear responsibilities, staged approvals, and trained decision-makers. Marc Gröflin, Head of Internal Audit at the Swiss National Bank, presented a combined assurance model that creates more transparency and real added value by closely coordinating audit, risk, ICS and compliance. Sandra Middel, Chief Ethics and Compliance Officer of the Axpo Group, provided the final impulse. She focused on the role of corporate culture and emphasized the importance of lived values, responsibility in everyday life and leadership by example. The contributions made one thing clear: GRC is a networked, strategic management approach – and a key success factor for organizations in a complex, dynamic world.
Business Wire
19-05-2025
- Business
- Business Wire
Credo AI Collaborates with Microsoft to Launch AI Governance Developer Integration to Fast-Track Compliant, Trustworthy Enterprise AI
SAN FRANCISCO--(BUSINESS WIRE)--Credo AI, a global pioneer and leader in AI governance for the enterprise, today launched an integration with Microsoft Azure AI Foundry. First announced in November, the next step in this collaboration bridges a long-standing divide between technical development and AI governance teams—empowering enterprises to innovate with AI at speed and scale while simultaneously ensuring trust, safety, and compliance. A recent Gartner report predicted that 60% of GenAI projects will fail after proof-of-concept due to gaps in governance, data, and cost control. Governance teams often lack the technical context to define or interpret AI evaluation results, while developers lack clarity on how to meet emerging governance requirements. The result is misalignment, friction, and AI innovation stuck in R&D. 'As AI becomes central to enterprise value creation, governance must shift from reactive oversight to proactive enablement,' said Navrina Singh, Founder and CEO of Credo AI. 'Our integration with Microsoft Azure AI Foundry represents a breakthrough: actionable, real-time governance that lives where AI is built. It's how innovation accelerates with responsibility.' 'Credo AI's integration tackles one of the biggest blockers in enterprise AI–the communication and alignment gap between AI governance teams and developers,' said Sarah Bird, Chief Product Officer for Responsible AI, Microsoft. 'The integration delivers prescriptive guidance to AI governance leaders on what to evaluate and empowers developers to run governance-aligned evaluations directly within their workflow.' The 1st Step to Solving the AI R&D Bottleneck This integration marks a breakthrough in Credo AI's vision to operationalize policy-to-code translation–turning abstract governance goals into concrete, actionable metrics and steps. By bridging policy and execution, the integration empowers governance teams to convert risk-management and innovation strategies into code-level evaluations–enabling scalable, measurable risk management across the AI lifecycle. The benefits of Credo AI's integration with Azure AI Foundry: Governance teams receive structured, validated technical evidence tied to each use case. Developers get code to run evaluators (like groundedness, hallucination, bias) to ensure their development process is aligned with AI governance and business objectives Evaluator results automatically flow back into the Credo AI platform, linking risk insights directly to governance workflows. Unlocking Innovation with Built-In Trust As part of this integration, all Azure AI Foundry models are governable within the Credo AI Platform, which is made possible by Credo AI's automatic integration specific mapping to the appropriate policies, risks, and evaluation requirements. This ensures: Faster AI adoption and approvals through contextual risk insights End-to-end compliance visibility aligned with the EU AI Act, NIST RMF, and ISO 42001 Smarter investment decisions based on governance readiness and risk adjusted ROI The integration is already in active pilots with select Global 2000 enterprises and has received strong enthusiasm from Microsoft teams and customers alike. Early users report accelerated model approval, clearer cross-team multistakeholder collaboration, and faster time-to-value for high-risk AI initiatives. 'At Version1, we're using the new Credo AI and Microsoft Azure AI Foundry integration to streamline AI governance for our clients—embedding policy, risk, and compliance into development and easing the load on our AI Labs team,' said Brad Mallard, CTO of Version1. More information on the Credo AI integration for Azure AI Foundry can be found here. To request a Demo of Credo AI's Platform, visit AI Governance Platform and AI Governance Advisory Services empower your enterprise to adopt and scale trusted AI with confidence. From Generative AI to Agentic AI, Credo AI's centralized platform measures, monitors, and manages AI risk—enabling your organization to maximize AI's value while mitigating security, privacy, compliance, and operational challenges. Credo AI also future-proofs your AI investments by aligning with global regulations, industry standards, and company values. Recognized as Fast Company's Most Innovative Companies, CB Insights AI 100, Inc. Best Workplaces, and the World Economic Forum Technology Pionee r, is leading the charge in accelerating the adoption of trusted AI.
Newsweek
19-05-2025
- Newsweek
'What if Superintelligent AI Goes Rogue?' Why We Need a New Approach to AI Safety
You will hear about "super intelligence," at an increasing rate over the coming months. Though it is the most advanced AI technology ever created, its definition is simple. Superintelligence is the point at which AI intelligence passes human intelligence in general cognitive and analytic functions. As the world competes to create a true superintelligence, the United States government has begun removing previously implemented guardrails and regulation. The National Institute of Standards and Technology sent updated orders to the U.S. Artificial Intelligence Safety Institute (AISI). They state to remove any mention of the phrases "AI safety," "responsible AI," and "AI fairness." In the wake of this change, Google's Gemini 2.5 Flash AI model increased in its likelihood to generate text that violates its safety guidelines in the areas of "text-to-text safety" and "image-to-text safety." If Superintelligence Goes Rouge We are nearing the Turing horizon, where machines can think and surpass human intelligence. Think about that for a moment, machines outsmarting and being cleverer than humans. We must consider all worst-case scenarios so we can plan and prepare to prevent that from ever occurring. If we leave superintelligence to its own devices, Stephen Hawking's prediction of it being the final invention of man could come true. AI apps are pictured. AI apps are pictured. Getty Images Imagine if any AI or superintelligence were to be coded and deployed with no moral guidelines. It would then act only in the interest of its end goal, no matter the damage it could do. Without these morals set and input by human engineers the AI would act with unmitigated biases. If this AI was deployed with the purpose of maximizing profit on flights from London to New York, what would be the unintended consequences? Not selling tickets to anyone in a wheelchair? Only selling tickets to the people that weigh the least? Not selling to anyone that has food allergies or anxiety disorders? It would maximize profits without taking into account any other factors than who can pay the most, take up the least time in boarding and deplaning, and cause the least amount of fuel use. Secondarily, what if we allow an AI superintelligence to be placed in charge of all government spending to maximize savings and cut expenses? Would it look to take spend away from people or entities that don't supply tax revenue? That could mean removing spending from public school meal programs for impoverished children, removing access to health care to people with developmental disabilities, or cutting Social Security payments to even the deficit. Guardrails and guidelines must be written and encoded by people to ensure no potential harm is done by AI. A Modern Approach Is Needed for Modern Technology The law is lagging behind technology globally. The European Union (EU) has ploughed ahead with the EU AI Act, which at a surface glance appears to be positive, but 90 percent of this iceberg lurks beneath the surface, potentially rife with danger. Its onerous regulations put every single EU company at a disadvantage globally with technological competitors. It offers little in the way of protections for marginalized groups and presents a lack of transparency in the fields of policing and immigration. Europe cannot continue on this path and expect to stay ahead of countries that are willing to win at any cost. What needs to happen? AI needs to regulate AI. The inspection body cannot be humans. Using payment card industry (PCI) compliance as a model, there needs to be a global board of AI compliance that meets on a regular basis to discuss the most effective and safe ways AI is used and deployed. Those guidelines are then the basis for any company to have their software deemed AI Compliant (AIC). The guidelines are written by humans, but enforced by AI itself. Humans need to write the configuration parameters for the AI program and the AI program itself needs to certify the technology meets all guidelines, or report back vulnerabilities and wait for a resubmission. Once all guidelines are met a technology will be passed as AIC. This technology cannot be spot checked like container ships coming to port—every single line of code must be examined. Humans cannot do this, AI must. We are on the precipice of two equally possible futures. One is a world where bad actors globally are left to use AI as a rogue agent to destabilize the global economy and rig the world to their advantage. The other is one where commonsense compliance is demanded of any company wanting to sell technology by a global body of humans using AI as the tool to monitor and inspect all tech. This levels the field globally and ensures that those who win are those that are smartest, most ethical, and the ones that deserve to get ahead. Chetan Dube is an AI pioneer and founder and CEO of Quant. The views expressed in this article are the writer's own.
Yahoo
13-05-2025
- Business
- Yahoo
Big Data Analytics Market to Reach Valuation of US$ 1,112.57 Billion by 2033
Big data analytics demand surges as healthcare, finance, and manufacturing sectors prioritize AI-driven insights. 80% of enterprises increased analytics budgets by 35% in 2024, focusing on regulatory compliance and vertical-specific solutions. Chicago, May 13, 2025 (GLOBE NEWSWIRE) -- The global big data analytics market was valued at US$ 326.34 billion in 2024 and is expected to reach US$ 1,112.57 billion by 2033, growing at a CAGR of 14.50% during the forecast period 2025–2033. As of 2024, the big data analytics market is bifurcated into domain-specific platforms (39% of revenue) and horizontal cloud-native tools (53%), with the remainder split between legacy on-premise solutions. Microsoft leads in hybrid deployments via Azure's edge-to-cloud Fabric platform, which supports 220+ regulatory frameworks (e.g., EU AI Act, China's DSL), capturing 28% of healthcare and manufacturing clients. On the other hand, AWS retains SMB dominance (47% market share <$1B revenue firms) through Redshift's $0.25/GB serverless pricing—32% cheaper than Snowflake. However, industry-focused vendors are gaining momentum: Palantir's AIP added 140 defense/space contracts in 2024 by embedding PHI/PII anonymization into federated analytics workflows, while Veeva Systems' clinical trial analytics platform grew 55% YoY by solving FDA's 2024 requirement for real-time AE/SAE reporting. Download Sample Pages: The big data analytics market is set to grow at a CAGR of 14.50% through 2033 (vs. 14.8% pre-2024), driven by AI-driven verticalization and regulatory complexity. Astute Analytica predicts 75% of enterprises will adopt PETs (privacy-enhancing tech) by 2025, with tools like Google's Confidential Space (homomorphic encryption) expected to reduce cloud analytics breach risks by 59%. Edge analytics will surge in heavy industries—Astute Analytica's research forecasts oil/gas investment in edge ML ops to hit $4.2B by 2025 (up from $1.7B) to preprocess sensor data, avoiding $12/hour per rig cloud transfer costs. Geopolitical tensions will splinter tech stacks: 71% of APAC firms now dual-source analytics tools (e.g., Alibaba Cloud + Databricks) to comply with China's cross-border data rules. Meanwhile, sustainability mandates will fuel demand for carbon-aware analytics. Startups like Watershed, which embed emission factors into Snowflake queries, grew 340% in 2024 as 29% of S&P 500 firms now tie ESG metrics to executive pay. Key Findings in Big Data Analytics Market Market Forecast (2033) US$ 1,112.57 billion CAGR 14.50% Largest Region (2024) North America (35%) By Component Software (70%) By Deployment Type Cloud-Based (61%) By Application Data Discovery (25%) Top Drivers Stricter AI ethics compliance mandates amid global regulatory fragmentation Demand for vertical, industry-specific predictive analytics over horizontal tools Edge-to-cloud latency reduction in IoT-driven real-time decision automation Top Trends Privacy-enhancing technologies (PETs) enabling cross-company data collaboration without exposure Decision intelligence platforms embedding causal AI and process mining Carbon-aware analytics tools integrating GHG protocols into cloud workflows Top Challenges Talent hybrid shortages (MLOps + domain expertise) delaying ROI timelines Rising costs of sovereign data storage and cross-border compliance Dynamic model drift in generative AI requiring continuous recalibration costs Generative AI Transforms Predictive Modeling with Multi-Modal Data Fusion The integration of generative AI in the big data analytics market is enabling enterprises to synthesize structured, unstructured, and real-time data streams. In 2024, advancements in multi-modal AI models allow companies like Walmart to combine satellite imagery, point-of-sale data, and customer foot traffic patterns to optimize store layouts, resulting in a 12% increase in per-customer revenue (Forbes, 2024). Financial institutions such as HSBC are using these models to simulate market shocks by blending historical trading data with geopolitical event logs, improving risk mitigation strategies by 24%. However, enterprises in the big data analytics market face challenges in managing "AI drift," where models degrade due to evolving data patterns. A 2024 MIT-Cognizant study found that 41% of generative AI deployments require monthly retraining to maintain accuracy. Pharma giant Roche addresses this by embedding real-time patient trial feedback loops into its drug discovery analytics, reducing model recalibration cycles from 30 to 7 days. Vendors like Databricks are also launching MLOps pipelines tailored for generative AI, automating 35% of maintenance workflows through anomaly detection. Data Privacy-as-a-Service Emerges to Navigate Global Compliance Complexity The big data analytics market is witnessing a surge in Privacy-Enhancing Technologies (PETs) as regional regulations fragment data governance standards. With Brazil's LGPD and India's DPDP Act (2023) imposing strict localization mandates, tools like AWS Clean Rooms grew by 89% YoY by enabling secure cross-company data collaboration. A 2024 survey found that 67% of enterprises now use homomorphic encryption for analytics, allowing computations on encrypted data without decryption. For example, Visa processes transaction fraud analysis across 40 markets without exposing raw data, reducing breach risks by 52%. Startups like Duality Technologies are advancing 'privacy-preserving AI' frameworks, which let firms train models on combined datasets from competitors in regulated sectors like insurance. Zurich Insurance Group used this to pool anonymized claims data with rivals, improving actuarial accuracy by 18% without violating antitrust laws. However, PET adoption is hindered by 30–40% higher compute costs, pushing vendors to develop hybrid quantum-classical encryption solutions for cost efficiency. Edge-to-Cloud Hybrid Architectures Address Latency and Data Sovereignty Demands The exponential growth of IoT devices and 5G connectivity is forcing enterprises in the big data analytics market to adopt hybrid edge-cloud analytics frameworks. In 2024, 62% of manufacturers now deploy edge nodes to preprocess raw sensor data on-premises, reducing cloud data transfer costs by 41% while complying with strict data residency laws. For example, Chevron's oil rigs in the North Sea use AWS Snowcone edge devices to analyze drilling telemetry in real time, cutting decision latency from 90 seconds to 0.8 seconds and preventing $3.8M/year in unplanned downtime. Meanwhile, retailers like Target use edge AI to process in-store camera feeds locally for inventory tracking, avoiding GDPR risks by retaining sensitive footage on-premises. However, hybrid models intensify integration complexity in the big data analytics market. A 2024 S&P Global survey found that 58% of firms struggle to unify edge/cloud metadata schemas, leading to fragmented insights. Snowflake's launch of Unistore, a transactional-analytical hybrid platform, helps firms like FedEx query live edge logistics data alongside cloud-stored shipping histories, improving route optimization by 19%. Vendors are also prioritizing edge-native tools: Microsoft's Azure Synapse Edge now allows SQL queries on streaming data, reducing dependence on centralized clouds. Key trends suggest that edge maturity will define 2025's competitive landscape as 5G-Advanced enables sub-50ms analytics for autonomous systems. Healthcare Big Data Platforms Navigate Privacy-Preserving Innovation Big data analytics adoption in healthcare surged by 34% in 2024 across the global big data analytics market, driven by mandates to reduce diagnostic errors and operational costs. Mayo Clinic's partnership with Google Cloud utilizes federated learning to train cancer detection models on 10M+ global patient records without sharing raw data, improving accuracy by 27% while maintaining HIPAA compliance. Similarly, Babylon Health's AI triage tool, analyzing 500K+ patient transcripts daily, reduced misdiagnoses in UK clinics by 22% (The Lancet). However, interoperability remains a bottleneck: 68% of U.S. providers (HIMSS 2024) report siloed EHR systems that delay analytics ROI by 9–14 months. Some of the startups in the big data analytics market like Syapse leverage HL7 FHIR APIs to harmonize oncology data across 150+ hospitals, enabling precision treatment roadmaps. Pharma giants are also innovating: AstraZeneca's clinical trial platform uses graph analytics to map patient biomarkers against genetic databases, cutting trial recruitment time from 18 to 6 months. Nevertheless, ethical concerns persist. MIT's 2024 audit of AI diagnostic tools found racial bias in 33% of radiology models, prompting vendors like Aidoc to introduce bias-detection SDKs. With FDA's 2024 AI/ML validation guidelines tightening, healthcare analytics vendors must balance innovation with algorithmic accountability. Ethical AI Audits Reshape Vendor Strategies in High-Stakes Sectors in the Big Data Analytics Market As regulators scrutinize AI ethics, enterprises demand transparent big data workflows. Forrester reports that 71% of financial firms now use third-party tools like IBM's Watson OpenScale to audit credit scoring models for racial/gender bias, aligning with the EU's AI Act. JPMorgan's 2024 audit of its mortgage approval algorithm revealed a 14% disparity in approval rates for minority applicants, prompting a model recalibration that increased approvals by $240M annually. Similarly, Unilever's HR analytics platform, powered by SAP SuccessFactors, underwent ESG compliance checks to eliminate demographic skew in hiring algorithms. Vendor differentiation in the big data analytics market now hinges on ethical frameworks. Salesforce integrated 'Ethics by Design' into Tableau CRM, auto-flagging biased customer segmentation patterns, which reduced churn among marginalized groups by 18% for users like Comcast. Startups like Credo AI offer 'nutrition labels' for analytics models, detailing training data sources and fairness metrics. However, audits slow deployment: Gartner finds compliance reviews delay 45% of AI projects by 4–6 months. To offset costs, AWS launched a pre-audited analytics service in 2024, offering vetted ML templates for regulated industries like insurance. The market is tilting toward vendors that bake ethics into analytics pipelines rather than treat it as an add-on. Democratization Tools Clash with Governance Needs in Self-Service Analytics No-code platforms across the global big data analytics market like Power BI and Qlik dominate the $14B self-service analytics market (Gartner 2024), enabling non-technical teams to generate insights 4x faster. Nestlé's marketing team uses ChatGPT-integrated Power BI to create campaign performance dashboards in 2 hours (down from 3 days), linking social media sentiment with sales data. However, 'shadow analytics' is rising: 41% of employees (Deloitte) bypass IT governance to use unauthorized tools, risking data leaks. For example, a 2024 breach at exposed 190K records after a sales analyst uploaded customer data to an uncertified freemium tool. Vendors in the big data analytics market are responding with embedded governance. Alteryx's 2024 update auto-tags PII in user-generated dashboards and blocks exports to unsecured platforms—adopted by 63% of financial firms to mitigate compliance risks. Meanwhile, Databricks' Unity Catalog provides lineage tracking for self-service queries, letting admins trace discrepancies to their source. Training is also critical: Cisco's Data Literacy Program upskilled 12K employees in data ethics, reducing governance violations by 82%. As generative AI makes analytics creation effortless, enterprises must prioritize governance without stifling agility. Need Custom Data? Let Us Know: Big Data Analytics Market Competitive Analysis The big data analytics market remains fiercely contested, with Microsoft, AWS, and Google Cloud collectively holding 58% market share. Microsoft's growth surged 23% YoY, driven by Azure Synapse Analytics and Fabric, which unify enterprise data lakes, AI, and BI tools. Its strategy targets Fortune 500 firms with hybrid cloud solutions—58% of its analytics revenue now comes from regulated sectors like healthcare and government. AWS, while lagging in AI-first tools, retains dominance via Redshift's serverless architecture and strategic partnerships (e.g., Databricks, Snowflake), serving 52% of mid-market firms. Google Cloud narrowed the gap with Vertex AI's multimodal capabilities, attracting 34% more retail clients in 2024 by integrating analytics with real-time inventory optimization. Snowflake, despite slower growth (18% YoY), expanded its healthcare and financial services footprint with Healthcare Data Cloud and vertical-specific LLMs, now serving 8,870+ global enterprises, including 60% of the Fortune 100. Niche players like Palantir and Cloudera differentiate through precision. Palantir's AIP for Big Data leverages federated analytics for defense and pharma clients, securing 28 new U.S. DoD contracts in 2024. Cloudera, focusing on hybrid data governance, grew its manufacturing base by 41% with CDP's edge-to-cloud kits. However, Oracle and IBM struggle: Oracle's MySQL HeatWave (70% faster queries than rivals) boosted SMB adoption but lags in enterprise AI integration. IBM's lost traction due to limited LLM compatibility, though its consulting arm retains 11,000+ analytics clients. Meanwhile, SAP and Salesforce embed industry analytics into ERP/CRM workflows—SAP's Datasphere now processes 50% of its clients' operational data. Vendors face mounting pressure to bundle analytics with ethical AI audits and sovereign cloud options as European and APAC regulators tighten compliance. Success hinges on vertical specialization and seamless human-AI collaboration tools. Global Big Data Analytics Market Key Players: IBM Corporation SAP SE SAS Institute Inc. Microsoft Corporation FICO Oracle Corporation Salesforce Inc. Google LLC Kinaxis Inc Hewlett Packard Enterprise Datameer Sage Clarity Systems Other Prominent Players Key Segmentation: By Component Hardware Software Services By Deployment Type Cloud-Based On-Premises Hybrid By Organization Size Large Enterprises Small and Medium-Sized Enterprises (SMEs) By Application Customer Analytics Data Discovery Advanced Analytics Data Visualization HR Analytics Financial Analytics Others By Industry Vertical BFSI Healthcare and Life Sciences Retail and Consumer Goods Manufacturing Energy and Utilities Government Transportation and Logistics Others By Region North America Europe Asia Pacific Middle East & Africa (MEA) South America Have Questions? Reach Out Before Buying: About Astute Analytica Astute Analytica is a global market research and advisory firm providing data-driven insights across industries such as technology, healthcare, chemicals, semiconductors, FMCG, and more. We publish multiple reports daily, equipping businesses with the intelligence they need to navigate market trends, emerging opportunities, competitive landscapes, and technological advancements. With a team of experienced business analysts, economists, and industry experts, we deliver accurate, in-depth, and actionable research tailored to meet the strategic needs of our clients. At Astute Analytica, our clients come first, and we are committed to delivering cost-effective, high-value research solutions that drive success in an evolving marketplace. Contact Us:Astute AnalyticaPhone: +1-888 429 6757 (US Toll Free); +91-0120- 4483891 (Rest of the World)For Sales Enquiries: sales@ Follow us on: LinkedIn | Twitter | YouTube CONTACT: Contact Us: Astute Analytica Phone: +1-888 429 6757 (US Toll Free); +91-0120- 4483891 (Rest of the World) For Sales Enquiries: sales@ Website: in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
