数据治理

Enabling Consistent Data Management Through Data Assets Governance

有效的数据治理对于企业从数据中获得有价值的见解和减轻与数据质量相关的风险至关重要, 隐私, 和安全.

数据质量: Data quality is a key element of data governance that ensures that data is accurate, 一致的, 而且质量高. 这涉及制定明确的数据质量标准，并确保定期监测和维护数据.
数据所有权: 数据所有权是数据治理的另一个重要元素，它定义了谁负责管理和维护数据. This involves establishing clear roles and responsibilities for data ownership, and ensuring that data is properly 安全d and protected.
主数据管理: 主数据管理是管理对组织运营至关重要的核心数据元素的过程.
数据的可发现性: 数据可发现性是使数据易于被组织内的涉众访问和发现的过程.

By effectively managing these core elements of data governance, 企业可以确保他们的数据准确无误, 安全, 、有序. This enables them to make informed decisions, mitigate risks, and drive growth and innovation.

什么是数据治理?

的数据治理研究所将数据治理定义为 a system of decision rights and accountabilities for information-related processes, 根据商定的模型执行，该模型描述了谁可以根据什么信息采取什么行动, 当, 在什么情况下, 用什么方法.

数据治理 是一套政策吗, 程序, 以及管理数据收集方式的标准, 存储, 和管理.

数据质量维度vs. 指标

我们利用 企业数据管理框架 to gain understanding around your current people, process, technology and data.

企业可以设置基准来简单地衡量数据的质量.
这些维度和指标允许业务领导者根据最重要的业务因素(数据及时性)确定合理的数据质量级别, 缺失的数据, 数据类型不正确).

维数据质量问题的类别是否具有共同的重要原因，并且通常具有相似的潜在原因.

为什么我仓库里的数据不是最新的?
Why is the data in my operational tool six hours behind?
为什么我的仪表盘需要这么长时间才能刷新?

指标描述一个尺寸是如何测量的, 数量上的或质量上的, 并且可以随时间追踪.

Difference between dashboard access time and latest refresh time of data
Number of hours in which service level agreement was not met
Average latency between ELT load and reverse ETL operationalization

什么是主数据管理(MDM)?

Enabling Consistent Data Management Through Master Data Management

主数据管理(MDM) is the process of creating, maintaining, and utilizing master data across an organization. This process includes identifying and defining master data, creating and maintaining a central repository of master data, and ensuring data quality and consistency across all systems that use the master data.

数据一致性和数据质量: MDM有助于确保数据在不同的系统和应用程序之间保持一致和准确. 它提供了一个, 关键业务数据的权威来源, which reduces the risk of data inconsistencies and errors.
节约成本: MDM can help 降低成本 by eliminating data redundancies and improving data quality. This results in more efficient processes, fewer errors, and lower operational costs.
法规遵从性: MDM可以通过确保数据的准确性和完整性来帮助组织遵守特定于行业的法规.
统一的视图: MDM helps organizations create a unified view of their customers, patients, facilities…etc. 允许组织从一个全面的角度来理解他们，并最终, 更好的业务成果.

整体, MDM is critical for organizations looking to establish a solid data foundation, 降低成本, 遵守法规, 改善决策, 推动商业成功.

Why is Managing Control Over Data Availability Crucial to 数据治理?

Managed control over your data availability leads to consistency in its governance, 哪一点对其可用性至关重要, 完整性, 和安全, supporting the growth and flexibility of your enterprise.

Establishing a governing body or council with a defined set of mandates, 目标, and processes will help to ensure that consistency is 维护.
自动化您的过程和执行已经建立的过程将支持一致性.
Consistency in the data shared across departments, 例如客户号或零件号, 或供应商和员工id, 将删除重复数据并使数据标准化吗, reducing the amount of incorrect data entering your core enterprise systems. 这也将大大减少由于公司内部共享的数据和信息不一致而导致的混乱和困惑.

一致的数据治理将允许您的部门管理层做出明智的运营决策, 而由公司管理层做出的战略决策将基于可信任的信息，这些信息是对运营的清晰和正确的快照. One version of the truth will benefit your entire enterprise.

What are the 好处 of Consistent 数据治理?

你的私募股权组织中的每个小组或部门都需要他们自己对你的数据的看法. 例如, 如果他们关注的是年终销售, the focus will be on all the business your sales team closed in the previous year. 的 group controller is going to have different line-of-business needs, and will be looking at all closed business that has been booked to the ledger. 投资组合公司的首席执行官在询问有关年终销售数据的问题时，会得到不同的答案，这取决于他或她问的是谁. 私募股权管理合伙人可能希望查看每个单独的投资组合公司或其整个投资组合的基准指标. 建立一套整个组织都遵循的通用数据参数并不简单, but 一致的 data governance will have a huge beneficial aspect to your organization.

A few of the benefits of 一致的 data governance are:

Less time spent on report reconciliation and in一致的 performance results.
Less data entry errors due to data being manually entered into multiple systems
A 一致的 set of definitions for key performance indicators across your enterprise
Data being distributed across your organization 一致的ly
Better management of key corporate master data, such as adds/deletes/changes
消除变更管理过程中可能延迟添加/删除/变更批准的瓶颈
它将确保与监管和遵守活动有关的准确程序，并增加这些领域所需的透明度.
数据的价值将会增加, and resolution of past and current data issues will be supported.
与其他数据管理领域相关的成本将会减少，企业的整体收入将会增加.

Why Do You Need a Good Policy for 数据治理?

您的企业将需要一个强有力的数据治理策略，以便围绕数据资产管理实施培训和教育实践, direction and compliance within the organization is clear and 一致的. 当涉及到管理业务的所有信息时，良好的数据治理策略将有助于维护有效的流程.

当你分析所有可能影响你业务目标的内部和外部因素时, 可以制定一个数据强大且有效的治理策略，这将决定在处理数据时策略的实现，并将帮助企业更好地控制所有数据资产, 包括方法, behaviors and technologies that surround the proper management of data.

A strong data governance policy considers your 目标. Establish 目标 and performance metrics for each area listed here:

Define key roles throughout your business that are related to data. 对于每一个系统, choose stewards to manage that data and focus on quality; the owners who will make decisions and define quality standards, and the IT team who will provide technical support and monitor all compliances.
记录和盘点你所有的数据来源，并定期检查库存，以确保它是最新的.
你收集数据的目的应该被确定，并与你的团队和客户沟通，并定期审查.
Policies should be in place that define how your records will be created, 维护, 然后处理掉.
质量和准入. 应指派适当的工作人员负责数据质量并进行定期审计，同时应明确界定所有许可和访问权限.
Data security policies need to be defined with respect to access and sharing of data. Include a risk management section that outlines risk and probabilities.

您希望知道组织中所有数据资产的正式管理是有效的和具有成本效益的，并为您的投资带来最大的回报. 一个经过深思熟虑的策略将通过确保所有团队成员知道谁可以采取哪些行动来做到这一点, 要用什么方法呢, 在什么情况下, reducing wasted time due to unclear direction or incomplete sets of information available.

How your policies define your business will influence your ability to manage it, 让每个人都保持专注和同步, 朝着同样的目标努力.

LBMC数据分析领导力

乔恩希尔顿

股东、咨询 & 商业智能

纳什维尔

布莱德米尔纳

高级主管，医疗保健分析