在数字时代，假新闻、人工智能改变的内容以及不断变化的出版道德和质量标准占主导地位——信任很难获得——很容易理解为什么高级管理人员应该天生信任自己的数据。
成熟的数据基础架构和文化的特点是数据源的透明度、轻松跟踪沿袭（谁开发以及如何开发）的能力以及促进数据质量、隐私和安全性的高级数据治理实践。
HFS Research的一份报告发现，虽然十分之八的高级管理人员信任其组织的数据，但不到一半的人表示他们认为大多数企业数据都可以操作。数字化转型网szhzxw.cn
一、可信数据的优势
通过强大的数据实践积极最大化信任的价值，企业可以增强其竞争优势，并与客户和利益相关者建立长期关系。数字化转型网szhzxw.cn
相反，未能维护数据信任的价值可能会导致声誉受损、客户流失以及潜在的法律和监管后果，因此企业必须优先考虑数据完整性、安全性和负责任的数据使用。
“组织在消费者活动、金融交易、运营和市场研究的数据之上收集、存储和构建流程，”Space and Time 的首席执行官兼联合创始人 Nate Holiday 说。“数据代表了企业对时间和资源的重大投资，并作为其决策和战略的基本事实来源。数字化转型网szhzxw.cn
然而，他指出，操作这些数据带来了无数的挑战，从处理收集的数据量到有效管理数据，再到从中获得可操作的见解。
“有效的数据管理需要一系列工具和技能，这些工具和技能很难、耗时且成本高昂，”Holiday 说。
SHI International的技术解决方案工程师Michael Heath表示，虽然实施强大的数据治理实践可以确保数据的一致性、准确性和安全性，但这并不意味着它会自动运行。数字化转型网szhzxw.cn
“数据监控和实现业务目标创造了信任和温暖而模糊的感觉，但最终这是一种数据使用的错误感觉，”他说。“事实是，80%的数据处于休眠状态，因为数据授权不佳或使用工具将其从休眠状态转变为行动。
随着数据从管理到治理的生命周期，最遗漏的部分之一是数据授权。
“你有正确的数据策略、管理和结构，但它被锁定了，”他说。
从他的角度来看，关键是通过提供数据并授权公民开发人员社区使用它为企业提供真正的价值和构思，将数据从分析转化为行动。数字化转型网szhzxw.cn
“将业务用户与数据管理员融合在一起，那么你就会拥有一些神奇的东西，”Heath说。
二、数据信任始于全面
Komprise总裁兼首席运营官Krishna Subramanian解释说，要真正获得对内部数据的信任，它不仅必须是高质量的，还必须是完整的。数字化转型网szhzxw.cn
她说：“满足现代执行要求的数据基础设施不能再仅仅关注来自 ERP、CRM、HR、财务和其他运营系统的结构化和半结构化数据。数字化转型网szhzxw.cn
跨云、边缘和混合数据中心堆积的大多数企业数据都是非结构化数据，从文档、文本和聊天到成像、视频、音频、物联网和研究数据。数字化转型网szhzxw.cn
这些非结构化数据经常被存储起来，很少作为商业智能工作流的一部分进行分析或执行。
她说：“发现非结构化数据源并向工程和研究团队提供正确的数据，以便进一步准备和探索数据，对于整体、可信的数据服务平台至关重要。数字化转型网szhzxw.cn
三、改进数据周期管理
Heath说，为了确保有效的数据周期管理，公司可以采取关键步骤，其中第一个步骤是建立一个全面的数据治理框架，定义角色，职责和数据质量标准。
“其次，投资数据监控和分析工具，以主动识别异常并跟踪数据性能，”他解释道。“最后，实施数据集成和安全措施，使数据在整个组织中可访问、可靠并受到保护。数字化转型网szhzxw.cn
对于所有步骤，了解并考虑循环中的人员非常重要组成组织的人员驱动数据。
他说：“有一套特定的独特行为和规范构成了组织内创建和使用数据的方式，确保推动治理和管理的数据文化是培育健康数据生命周期的原因。数字化转型网szhzxw.cn
Subramanian说，对于高管来说，这一切都归结为在需要时访问高质量数据的能力。
这需要人工智能驱动的分析和商业智能解决方案来分析实时和历史数据，以了解现在正在发生的事情以及与一段时间内的趋势相比如何。数字化转型网szhzxw.cn
这应与数据管理技术相结合，通过持续索引数据并提供搜索、查询和数据移动性，无论数据位于何处，跨数据中心和云中的不同数据孤岛，为这些分析解决方案提供正确的数据。
“这确保了用户能够找到正确的数据，并立即将其发送到商业智能和AI / ML解决方案，”她说。“非结构化数据分析领域刚刚兴起。
四、挖掘 AI 的力量
Subramanian指出了新的人工智能和机器学习技术，这些技术正在改善各种类型的非结构化内容的标记。
此外，数据管理工具正在不断发展，以提供统一的全球数据索引、深度分析以跨 PB 级数据查询和搜索正确的数据，以及数据工作流功能以自动分析和操作非结构化数据。
Heath说，在他们的组织中，他们成功地解决了数据信任挑战，建立了一个强大的数据治理框架，首先由文化驱动，其次由技术驱动，以满足这些需求。数字化转型网szhzxw.cn
“通过与业务利益相关者合作，我们将数据战略与业务目标保持一致，”他说。
Holiday表示，他同意随着世界深入数字时代，人工智能等技术越来越多地推动业务流程，可验证的数据比以往任何时候都更加重要。数字化转型网szhzxw.cn
“高管们需要知道，支持业务决策的数据是准确、防篡改和可操作的，”他说。
英文原文：
Trust is hard to come by in the digital age — where fake news, AI-altered content and shifting standards of ethics and quality in publishing reign. It’s easy to see why senior executives should inherently trust their own data.数字化转型网szhzxw.cn
A mature data infrastructure and culture is marked by transparency in the data source. The ability to easily track lineage (who developed it and how). And advanced data governance practices fostering data quality, privacy and security.
A report by HFS Research found while eight in 10 senior executives trust their organization’s data. Less than half said they believe that most enterprise data can be operationalized.
The Benefits of Trusted Data
By actively maximizing the value of trust through robust data practices, businesses can strengthen their competitive advantage and build long-term relationships with their customers and stakeholders.
Conversely, failing to uphold the value of trust in data can lead to reputational damage, loss of customers, and potential legal and regulatory consequences. Making it crucial for businesses to prioritize data integrity, security, and responsible data use.数字化转型网szhzxw.cn
“Organizations gather, store, and build processes on top of data derived from consumer activity, financial transactions, operations, and market research,” says Nate Holiday, CEO and cofounder of Space and Time. “Data represents a significant investment of time and resources for a business and serves as a base source of truth for its decision making and strategy.”数字化转型网szhzxw.cn
However, he notes operationalizing this data presents a myriad of challenges. From dealing with the sheer volume of collected data, to managing it effectively, to deriving actionable insights from it.
“Effective data management requires an array of tools and skills that are difficult, time consuming, and expensive to bring together,” Holiday says.数字化转型网szhzxw.cn
iIt doesn’t mean it will be automatically operationalized.
Michael Heath, technical solutions engineer at SHI International. Says while the implementation of robust data governance practices ensures data consistency, accuracy. And security, it doesn’t mean it will be automatically operationalized.数字化转型网szhzxw.cn
“Data monitoring and achieving business objectives creates trust and a warm and fuzzy feeling, but in the end it’s a false sense of data usage,” he says. “The fact of the matter is that 80% of data lies dormant because of poor data empowerment or usage of the tools to take it from dormancy to action.”
As data moves through its own lifecycle from management to governance, one of the most missed pieces is data empowerment.数字化转型网szhzxw.cn
“You’ve got the correct strategy, management and structure for the data, but it’s locked up,” he says.
From his perspective, the key is to take data from analysis to action by making the data available and empowering a community of citizen developers to use it to deliver real value and ideation to the enterprise.数字化转型网szhzxw.cn
“Meld the business users with the data stewards, then you have something magical,” Heath says.
Data Trust Starts with a Complete Picture
Krishna Subramanian, president and COO of Komprise, explains to truly gain trust in internal data, it must not only be of high quality — it also must be complete.数字化转型网szhzxw.cn
“The data infrastructure to deliver modern executive requirements can no longer solely focus on structured and semi-structured data from ERP, CRM, HR, finance and other operational systems,” she says.
Most enterprise data that is piling up across clouds, edge and hybrid data centers is unstructured data, from documents and texts and chats to imaging, video, audio, IoT, and research data.
This unstructured data is too often stored away and rarely analyzed or executed as part of a business intelligence workflow.数字化转型网szhzxw.cn
“Discovering unstructured data sources and delivering the right data to engineering and research teams for further data preparation and exploration is essential to a holistic, trusted data services platform,” she says.
Improving Data Cycle Management
Heath says to ensure effective data cycle management, companies can take crucial steps, the first of which is to establish a comprehensive data governance framework that defines roles, responsibilities, and data quality standards.数字化转型网szhzxw.cn
“Secondly, invest in data monitoring and analytics tools to proactively identify anomalies and track data performance,” he explains. “Lastly, implement data integration and security measures to make data accessible, reliable, and protected across the organization.”
With all steps, it’s important to understand and to account for the human in the loop–the people that make up your organization drive the data.数字化转型网szhzxw.cn
“There are a specific set of unique behaviors and norms that make up the way data gets created and used within the organization and ensuring a data culture that drives governance and management is what nurtures a healthy data lifecycle,” he says.
Subramanian says for executives, it all comes down to the ability to access quality data when they need it.
This requires AI-driven analytics and business intelligence solutions to analyze real-time and historical data to understand what’s happening now and how that compares with trends over time.
This should be combined with data management technologies that feed the right data to these analytics solutions by continually indexing data and providing search, query and data mobility no matter where the data lives, across different data silos both in datacenters and the cloud.
“This ensures that users can find the right data and send it to business intelligence and AI/ML solutions without delay,” she says. “The field of unstructured data analytics is just emerging.”
Tapping the Power of AI
Subramanian points to new AI and machine learning techniques that are improving the tagging of unstructured content of various types.数字化转型网szhzxw.cn
In addition, data management tools are evolving to provide a unified global data index, deep analytics to query and search for the right data across petabytes of data and data workflow capabilities to automate the analysis and operationalization of unstructured data.
Heath says in their organization. They successfully addressed data trust challenges by establishing a strong data governance framework driven first by culture and second by technology to meet those needs.
“By collaborating with business stakeholders, we aligned data strategies with business objectives,” he says.数字化转型网szhzxw.cn
Holiday says he agrees as the world moves deeper into the digital era and technologies like AI increasingly drive business processes, verifiable data is more important than ever.
“Executives need to know that the data powering business decisions is accurate, tamperproof, and actionable,” he says.数字化转型网szhzxw.cn
本文由数字化转型网（www.szhzxw.cn）翻译而成，来源于informationweek.com；编辑/翻译：数字化转型网默然。
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