As ransomware attacks ravage data systems and operations across industries, data protection and cybersecurity leaders are finding that their recoverability falls short in the face of this threat. Recent research by TechTarget’s Enterprise Strategy Group finds most organizations have a long way to go in bringing ransomware service-level agreements (SLAs) in line with those of traditional recovery operations to protect their critical data assets and reputations.
Data Management, Analytics & AI
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Ransomware has burdened data protection and cybersecurity professionals alike since its inception in the modern threat landscape. Recent research by TechTarget’s Enterprise Strategy Group showed that this now-mainstay threat is no longer an anxiety for only IT professionals tasked with managing its mitigation but also affects roles and lines of business throughout organizations that must combat it.
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As organizations seek the optimal mix of security, agility, and cost efficiency in their data environments, they increasingly find themselves working with both on-premises and public cloud technologies. These hybrid environments provide numerous benefits for differing data types and workloads, but they can also cause operational complexity for the IT professionals tasked with deploying, managing, and maintaining a variety of technologies across numerous vendor products. Recent research by TechTarget’s Enterprise Strategy Group found that IT teams are looking to solidify their cloud experiences as these hybrid trends evolve.
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With data powering everything from product development and tailored customer experiences to compliance with legal and ethical standards, most organizations are open to collecting as much data as they can to fuel competitive advantage and keep their operations humming. But funneling in data from every source available can cause challenges for the IT teams tasked with operationalizing this data and keeping it safe. Recent research by TechTarget’s Enterprise Strategy Group shows that most organizations are aware of this mounting problem and carefully planning solutions.
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Department heads the world over are hearing mandates to infuse data science and machine learning (ML) into their operations as quickly as possible. However, ascertaining the goals of these projects and securing the requisite budget are not simple tasks. Recent research by TechTarget’s Enterprise Strategy Group revealed that funding and confusion follow ML and data science initiatives almost everywhere they appear.
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The use of artificial intelligence (AI) and machine learning (ML) to supercharge business operations has captured the imaginations of stakeholders from every business line and department, especially with the rapid popularity of generative artificial intelligence (GenAI) tools. Along with all this excitement comes myriad concerns regarding the ethical use of such tools. TechTarget’s Enterprise Strategy Group recently surveyed data professionals to find out how they’re balancing AI use with ethical and governance standards.
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Informing business decisions with the valuable insights hidden within troves of data can only be accomplished with robust data science practices. And those data science practices cannot get off the ground without educated, motivated, and well-supported data scientists. Recent research by TechTarget’s Enterprise Strategy Group revealed several key factors can improve (or hinder) the development of these teams and professionals in their pursuit of data science goals.
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Data science has established itself as a critical factor to success in today’s competitive business climate. Recent research by TechTarget’s Enterprise Strategy Group found that organizations purchasing supporting data science technologies are looking for help with several critical stages of the data lifecycle.
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Paramount to the success of data science initiatives is enabling as many users as possible within the organization to work with the data and tools that make these projects actionable. Recent research by TechTarget’s Enterprise Strategy Group examined the experiences of data science stakeholders and where they see room for improvement in terms of team collaboration and technology support.
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Organizations across industries are making data science a strategic charter as they seek new operational efficiencies and competitive advantage. Recent research by TechTarget’s Enterprise Strategy Group revealed that agreeing on the foundational elements of data science and how to measure success are critical yet evolving elements of this consequential discipline.
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As businesses come to understand the benefits of streamlining the machine learning (ML) lifecycle to faster achieve their data-driven goals, their focus turns to optimizing ML operations (MLOps). Recent research by TechTarget’s Enterprise Strategy Group revealed vast room for improvement in terms of deploying models to production and overcoming various challenges with MLOps.
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Cohesity recently introduced its new Gaia capability, bringing AI-powered enterprise search to its data backup environments.
This is not an obligatory AI-washing announcement, as many vendors feel they must keep making these days. In contrast, Gaia uses AI to maximize the return on your secondary data assets.
In 2019, TechTarget’s Enterprise Strategy Group introduced a maturity model that predicted the evolution of the traditional backup market into the data intelligence market, including the use of automation and AI/ML. In the model, we identified that the requirement for context and content about the data is becoming more acute as new regulations and the need for data to support digital transformation change the role of data in the enterprise. People, processes, and workflows must use data more intelligently. It’s really about business outcomes and the notion of data as a true asset that can be leveraged to create a return on investment or avoid costs and risks. Our model identified the critical role of AI/ML in reaching data management nirvana.
That’s what Gaia delivers in many ways, but there is more than meets the eye.
Why? It uses retrieval-augmented generation (RAG) to give end users conversational and context-rich responses to gain insights from their secondary enterprise data.
Cohesity’s new tool combines a conversational interface, AI-powered search—on indexed enterprise secondary data—and LLM response. It is designed to help organizations improve their decision-making speed and accuracy while securely searching enterprise data in a compliant fashion. Key features include natural language queries, role-based access, RAG capabilities, and a SaaS-based platform.
The use case Cohesity is putting forward relates to investigative compliance research, such as detecting violations of personally identifiable information exposures—in other words, searching data from your backups for compliance issues. It is easy to imagine adding outside data to identify the penalties or other requirements associated with this internal data. The key is that this happens securely as “your” data is never exposed externally but can be augmented by or augment external data.
Our initial view, with the caveat that our research firm has not functionally or technically validated the solution, is that Gaia is a timely platform that extends Cohesity’s capabilities, making it faster, more automated, and more efficient. Its AI assistant prioritizes privacy, risk mitigation, data protection, and security.
It is designed to change end users’ ability to unlock value in their company’s data assets without a Ph.D. in data science.
We see the platform’s user-friendly interface and expandable ecosystem as critical to allow for external data integration and complex tasks to be performed with ease. It’s an ideal solution for enterprises seeking to maintain regulatory compliance, and its simple question-and-answer feature makes it an efficient tool for additional business and technical processes in time.
While it might be a little early to say with Gaia, one of the significant benefits of using generative AI for conversational search on enterprise data is its ability to deliver a personalized experience. Every person accessing the data will ask different questions, and it’s impossible to predict them all. For instance, someone could ask about compliance while another group asks about security. The variety of questions presents an exciting opportunity for Cohesity to provide a comprehensive, context-rich and tailored solution to its users.
Beyond the compliance use case, we expect Cohesity to deliver what we call “generative data protection” capabilities through Gaia, focusing on cyber resilience and cyber recovery. These are inherently more complex than simply discovering data misuse or ineffective data governance policies, but they are also well-known territory for Cohesity’s platform.
Also, we appreciated how Cohesity showed some transparency in the pricing model, and organizations will need guidance based on what they expect from their user base. There will likely be a gradual process as they navigate this, with limitations based on the number of answers and the amount of storage indexed. Monitoring this pricing model is crucial, as it may set the stage for the entire market.
We believe that Cohesity has an excellent opportunity to establish itself as a leader in the market with this announcement, as it is capacity- and answer-based. Over time, the number of answers and capacity will likely determine the subscription price or pricing structure.
While it might seem like the result of years of work in intelligent data management of non-production/backup data for Cohesity, it’s just the beginning of the next phase of their technological evolution. This new phase involves integrating technologies with a broad ecosystem of cyber and AI partners and the elephant in the room: Veritas. This will happen when the acquisition deal closes in the next few quarters. With this in mind, Cohesity must execute on all fronts to ensure the continued success of Gaia and the future “new” Cohesity.
Competitors are constantly working on their AI-focused initiatives, so Cohesity cannot afford to stand still. The year 2024 is sure to be an exciting year for the company and the market at large.
Enterprise Strategy Group is a division of TechTarget. Its analysts have business relationships with technology vendors.