AI的制作推力将数据平台挂在钩上求结果
企业数据平台必须将AI试点转化为可衡量的业务回报，同时克服实验和生产之间的数据获取、主权和成本障碍。
对话正在从AI所能做的转向公司如何让它得到回报。 一个数据平台提供商的回应是，将售后团队折叠在一个单一的领头人之下，根据Cloudera Inc公司首席客户官Patricia Bogoevic(图)的说法，客户从登机到续机都给一个声音。 现在的任务是使这些小组符合客户的业务目标，并帮助他们更好地利用他们已经支付的技术。
“当我从客户的角度看待事物时，对我来说，技术只有在客户使用的时候才有意义，”Bogoevici 称。 “我衡量客户如何与Cloudera实现价值。”
Bogoevici在CUBE, SiliconANGLE Media的直播室独家播出时，他们讨论了将AI推向生产，改善客户的采纳和衡量业务价值的问题。 (* 下文披露。 )
为什么企业数据平台决定AI的生产道路？
根据Bogoevici的说法，客户面部障碍分为三类，从数据访问开始。 对1 300多名信息技术专家进行的一项研究发现，约有80%的人将数据访问列为最大障碍。 接下来是施政和主权问题，因为数据分散在各个筒仓，对数据可以生活的地方有不同的规则。
“数据不能全部移到云中。 她的称。 “这是保护、主权、前期和空隙环境。”
最后的障碍是规模。 她解释说，一个在少数数据上运行的试点项目很少是显示，而全面部署将产生什么成本或回报，从而使所有权和投资收益的总成本不明。 与Mistral AI SAS合作，带来开放量模型进入平台。
Bogoevici 称。 了解人工智能模型的结果，了解其价值和计算方法
以及CUBE的Cloudera EVOLVE26报导：
(*披露：CUBE是EVOLVE26 2026赛事的付费媒体合作伙伴。 CUBE活动报道的发起人Cloudera和其他赞助者对CUBE或SiliconANGLE上的内容都没有编辑控制。 ).
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AI’s production push puts data platforms on the hook for results
Enterprise data platforms must turn AI pilots into measurable business returns while overcoming the data access, sovereignty and cost barriers that stand between experimentation and production.
The conversation is shifting from what AI can do to how companies make it pay off. One data platform provider has responded by folding its post-sales teams under a single leader, giving customers one voice from onboarding through renewal, according to Patricia Bogoevici (pictured), chief customer officer of Cloudera Inc. The mandate now is to align those teams with customers’ business objectives and help them make better use of the technology they have already paid for.
“When I look at things in terms of customers, for me, technology makes sense only when customers are using it,” Bogoevici said. “I am measured on how customers realize value with Cloudera.”
Bogoevici spoke with theCUBE’s Rebecca Knight at Cloudera EVOLVE26, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed moving AI into production, improving customer adoption and measuring business value. (* Disclosure below.)
Why enterprise data platforms shape AI’s path to production
The barriers customers face fall into three categories, starting with data access, according to Bogoevici. A study of more than 1,300 information technology specialists found that roughly 80% named data access as the biggest obstacle. Governance and sovereignty come next, because data sits scattered across silos with differing rules on where it can live.
“Not [all] data can be moved to cloud. Not [all] data needs to be in every place,” she said. “It’s about protection, it’s about sovereignty, it’s about on-prem and it’s about air-gap environment.”
The final hurdle is scale. A pilot running on a subset of data rarely shows what a full deployment will cost or return, which leaves total cost of ownership and return on investment unclear, she explained. Cloudera’s answer is Anywhere Cloud, which it plans to release in November, alongside a partnership with Mistral AI SAS that brings open-weight models into the platform.
“It’s one thing to run the pilot in a subset of data,” Bogoevici said. “It’s a completely different thing to run it [at] full scale and understand the results you get from the AI models and understand the value and how you calculate it.”
Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of Cloudera EVOLVE26:
(* Disclosure: TheCUBE is a paid media partner for the EVOLVE26 2026 event. Neither Cloudera, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
Photo: SiliconANGLE
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