Big Data Management
Ocient | September 25, 2023
Ocient, a renowned hyperscale data analytics platform, has recently announced the release of its second annual industry report, titled "Beyond Big Data: Hyperscale Takes Flight." The 2023 report, which is the result of a survey conducted among 500 data and IT leaders responsible for managing data workloads exceeding 150 terabytes, sheds light on the escalating significance of hyperscale data management within enterprises. It also underscores the critical requisites of time, talent, and cutting-edge technology necessary for the effective harnessing of data at scale.
Building on the foundation of Ocient's inaugural Beyond Big Data survey, the 2023 report delves into the minds of IT decision-makers, seeking to unravel the challenges, investment priorities, and future prospects occupying the forefront of their agendas for 2023 and beyond. The report offers valuable year-over-year comparisons, drawing on trends identified in 2022 while also presenting fresh, timely insights that mirror the immediate concerns of enterprise leaders in the United States.
Among the pivotal insights featured in this year's report are the following:
Immediate Emphasis on Data Quality: Organizations committed to leveraging their hyperscale data for critical business decisions are placing paramount importance on ensuring the highest data quality standards.
Data Workload Growth as a Driving Force: Data and IT leaders are increasingly recognizing data warehousing and analytics as pivotal elements of their IT strategies, a sentiment vividly reflected in their budget allocations.
AI Readiness at the Forefront: Leaders are eager participants in the AI revolution, yet they grapple with concerns surrounding security, accuracy, and trust.
Innovation Hindered by Talent and Technology Gaps: Many leaders continue to struggle with the challenges of optimizing their toolsets and scaling their teams swiftly enough to meet the demands posed by hyperscale data volumes.
Stephen Catanzano, Senior Analyst, Enterprise Strategy Group, commented:
It's clear enterprises are investing in data analytics and warehousing, especially given their costs are being driven up so high with older systems that can't handle the data that's being pushed to them.
[Source – Business Wire]
Chris Gladwin, Co-Founder and CEO of Ocient, stated that data was not slowing down, and he emphasized that the results of the 2023 Beyond Big Data report confirmed the significance of hyperscale data workloads for enterprises across various industries. He also noted that data volumes were on the rise, as was the importance of comprehending one's data. Nevertheless, the challenges related to data quality, the proliferation of tools, and staffing constraints persisted and were impeding progress in the industry.
Furthermore, Gladwin mentioned that the frontier beyond big data had arrived and that Ocient's annual report illustrated the challenges and opportunities that were shaping the enterprise data strategies of the future.
About Ocient
Ocient is a pioneering hyperscale data analytics solutions company dedicated to empowering organizations to unlock substantial value through the analysis of trillions of data records, achieving performance levels and cost efficiencies previously deemed unattainable. The company is entrusted by leading organizations around the globe to leverage the expertise of its industry professionals in crafting and implementing sophisticated solutions. These solutions not only enable the rapid exploration of new revenue avenues but also streamline operational processes and enhance security measures, all while managing five to 10 times more data and significantly reducing storage requirements by up to 80%.
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Big Data Management
Microsoft | September 22, 2023
AI models rely heavily on vast data volumes for their functionality, thus increasing risks associated with mishandling data in AI projects.
Microsoft's AI research team accidentally exposed 38 terabytes of private data on GitHub.
Many companies feel compelled to adopt generative AI but lack the expertise to do so effectively.
Artificial intelligence (AI) models are renowned for their enormous appetite for data, making them among the most data-intensive computing platforms in existence. While AI holds the potential to revolutionize the world, it is utterly dependent on the availability and ingestion of vast volumes of data.
An alarming incident involving Microsoft's AI research team recently highlighted the immense data exposure risks inherent in this technology. The team inadvertently exposed a staggering 38 terabytes of private data when publishing open-source AI training data on the cloud-based code hosting platform GitHub. This exposed data included a complete backup of two Microsoft employees' workstations, containing highly sensitive personal information such as private keys, passwords to internal Microsoft services, and over 30,000 messages from 359 Microsoft employees. The exposure was a result of an accidental configuration, which granted "full control" access instead of "read-only" permissions. This oversight meant that potential attackers could not only view the exposed files but also manipulate, overwrite, or delete them.
Although a crisis was narrowly averted in this instance, it serves as a glaring example of the new risks organizations face as they integrate AI more extensively into their operations. With staff engineers increasingly handling vast amounts of specialized and sensitive data to train AI models, it is imperative for companies to establish robust governance policies and educational safeguards to mitigate security risks.
Training specialized AI models necessitates specialized data. As organizations of all sizes embrace the advantages AI offers in their day-to-day workflows, IT, data, and security teams must grasp the inherent exposure risks associated with each stage of the AI development process. Open data sharing plays a critical role in AI training, with researchers gathering and disseminating extensive amounts of both external and internal data to build the necessary training datasets for their AI models. However, the more data that is shared, the greater the risk if it is not handled correctly, as evidenced by the Microsoft incident. AI, in many ways, challenges an organization's internal corporate policies like no other technology has done before. To harness AI tools effectively and securely, businesses must first establish a robust data infrastructure to avoid the fundamental pitfalls of AI.
Securing the future of AI requires a nuanced approach. Despite concerns about AI's potential risks, organizations should be more concerned about the quality of AI software than the technology turning rogue.
PYMNTS Intelligence's research indicates that many companies are uncertain about their readiness for generative AI but still feel compelled to adopt it. A substantial 62% of surveyed executives believe their companies lack the expertise to harness the technology effectively, according to 'Understanding the Future of Generative AI,' a collaboration between PYMNTS and AI-ID.
The rapid advancement of computing power and cloud storage infrastructure has reshaped the business landscape, setting the stage for data-driven innovations like AI to revolutionize business processes. While tech giants or well-funded startups primarily produce today's AI models, computing power costs are continually decreasing. In a few years, AI models may become so advanced that everyday consumers can run them on personal devices at home, akin to today's cutting-edge platforms. This juncture signifies a tipping point, where the ever-increasing zettabytes of proprietary data produced each year must be addressed promptly. If not, the risks associated with future innovations will scale up in sync with their capabilities.
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Big Data Management
PR Newswire | September 27, 2023
Congruity360, a leading unstructured data management and risk mitigation provider, announces the addition of data mobility in Enterprise Insights.
As unstructured data grows at the annual rate of 55% to 65% and accounts for more than 80% of all enterprise data, businesses must find a way to identify, classify and move data intelligently and automatically during its lifecycle. As enterprises grow, their valuable data must mature with their business. This may require a journey to the cloud, SLA changes which optimize storage costs, classification to mitigate risk, and moving the right data to additional key AI platform initiatives.
A simple, scalable, high-performance data classification engine, Enterprise Insights delivers next-generation data lifecycle management for storage optimization, security and risk optimization, and IT business optimization.
Enterprise Insights Approach to Successful Data Optimization:
Identify – Securely analyze PBs of unstructured data across on premises (NAS & object) and cloud (files/objects & SaaS) sources by harnessing the power of the platform's rapid insights and auto-discover technologies, which can reduce data identification times by 1,000%.
Classify – Quickly identify key client data attributes for cost savings, risk mitigation, and business impact with simple to consume dashboards and drill down capabilities.
Review – Confidently create and take actions by leveraging the comprehensive search engine to quickly find and preview data for movement without ever leaving the platform.
Remediate – Seamlessly take action (migrate and tier) on classified data to ensure it's properly protected, optimally stored, and most effectively serving the business.
Enterprise Insights offers three use case-driven insight analysis modules:
Storage and Migration Optimization – Insights into over 35 file data attributes including systems' aged, stale, obsolete, redundant, trivial, and types of systems files.
Business Optimization – Insight into and classification by business units' or cost centers' aged, stale, obsolete, redundant, trivial, and types of files.
Data Security and Risk Optimization – Insights into files containing PII and SPII, financial, legal, security, and risk data, as well as open shares and other network & storage security vulnerabilities.
By leveraging Enterprise Insights, clients can classify data for simple and secure migration both on premise and in the cloud. Equally important is Insights data tiering capabilities, enabling users to match data storage costs to data usage.
Powered by the Classify360 Platform, Enterprise Insights' secure hybrid approach to data analysis scales capabilities to exabyte levels at unmatched speed. Enterprise Insights is the industry's most powerful weapon to tackle the costs, time, and complexity of cloud migration projects, backup modernization, storage tiering, hardware refresh, and security posture management. By providing users with dashboards highlighting their existing storage costs and risks, Enterprise Insights frees clients from hidden, legacy, CapEx and OpEx expenditures, performance, and scalability bottlenecks while discovering and acting on sensitive and risk data.
Unstructured data insanity is treating all data equally with zero insights into its business impact, said Brian Davidson, Chief Executive Officer and Managing Partner of Congruity360. Enterprise Insights is the first step in implementing optimized data lifecycle management. With historically high data growth and new business uses for unstructured data, it is essential to attack the costs and risks inherent in unmanaged data. Our customers have realized 7-10x returns on their data lifecycle management implementations while reducing risk in an auditable compliance framework. As AI continues to gain steam, don't overpay by moving useless data to your expensive AI platforms.
The Classify360 Platform is comprehensive, simple to implement, scale, and operate. Businesses leverage the Classify360 Platform for unstructured data discovery, classification, business workflows, remediation actions, and insightful reporting. Congruity360 continues to tackle additional data governance challenges through innovations to the Classify360 Platform to continue delivering revolutionary data governance and classification, at scale, to the enterprise world.
ABOUT CONGRUITY360
Congruity360 delivers the only data life cycle management solution built on a foundation of classification, by expert data storage engineers alongside expert data privacy consultants. The Classify360 Platform is easy to implement, requires no outside consultants, and quickly analyzes your data at the petabyte scale in days, not weeks or months.
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