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市場調查報告書

根據商業目的的資料數據品質管理提升資料可信度

Restore Trust in Your Data Using a Business-Aligned Data Quality Management Approach

出版商 Info-Tech Research Group 商品編碼 603327
出版日期 內容資訊 英文 130 Pages
商品交期: 最快1-2個工作天內
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根據商業目的的資料數據品質管理提升資料可信度 Restore Trust in Your Data Using a Business-Aligned Data Quality Management Approach
出版日期: 2017年04月21日 內容資訊: 英文 130 Pages
簡介

推動事業策略或致力於聚焦領域時,組織多重視資料數據、活用重要的見解,以期協助組織願景、主要目標、方針的實現。

然而低質量的資料數據將影響獲得所需見解的時間,亦會損害有關顧客體驗改善、實現創新產品及服務、提升業務效率、風險及合規管理等的組織行動。此外,從資料中找出見解以協助決策時,其見解的質量也無法超越原本資料的品質。

為了提升資料品質,需要整備一個能夠持續可期成果、並適合資料運用的資料品質管理體制,才能跟上不斷變化的商業及資料環境步調、搶得先機。此時重要的是,並非使用龐大的資源及時間逐次修改每個數據集,而是製作能夠鎖定損害資料品質一致性的部分、改善來源的數據程序。

本報告以正在考慮提升資料數據品質、降低複雜性、整備能確保資料數據品質體制的CIO、資料數據管理負責人、肩負資料數據品質提升任務者、整合目前資料數據相關概念的資料數據管理負責人等為對象。

本報告能協助您:

  • 提升資料數據品質使概念與商業目標一致,傾注全力以創造符合目的的構想。
  • 迴避改善資料數據品質時遇到的一般性風險及課題。
  • 找出組織資料數據品質提升中所欠缺的關鍵。
  • 思考資料數據品質問題根源,於其解決問題。
目錄
Product Code: 74289

Get ahead of the data curve by conquering data quality challenges.

Regardless of the driving business strategy or focus, organizations are turning to data to leverage key insights and help improve the organization's ability to realize its vision, key goals, and objectives.

Poor quality data, however, can negatively affect time-to-insight and can undermine an organization's customer experience efforts, product or service innovation, operational efficiency, or risk and compliance management. If you are looking to draw insights from your data for decision making, the quality of those insights is only as good as the quality of the data feeding or fueling them.

Improving data quality means having a data quality management practice that is sustainably successful and appropriate to the use of the data, while evolving to keep pace with or get ahead of changing business and data landscapes. It is not a matter of fixing one data set at a time, which is resource and time intensive, but instead identifying where data quality consistently goes off the rails and creating a program to improve the data processes at the source.

This research is designed for:

  • A CIO or data management executive looking to improve data quality, reduce data complexity, and build a data quality practice.
  • Data owners and stewards who are tasked with the duty of improving data quality and/or currently managing data initiatives.

This research will help you:

  • Align your data quality initiative with the business, exercising just enough effort to making it fit for purpose.
  • Avoid common pitfalls and challenges that derail data quality initiatives.
  • Recognize any organizational data quality gaps and deficiencies and improve them.
  • Get to the root of data quality issues to fix data quality issues where they start.

Executive Summary

Situation:

With the business demand for useful data and the rate of data proliferation showing no signs of slowing down, users are struggling with getting quality data to meet their business needs and to support timely decision making. Even when the data gets to users, they don't trust the data and complain about getting different answers while running the same report.

Complication:

  • IT is struggling to define what quality means in the context of meeting the needs of data users. Data quality is not an absolute. Perfect data quality is unattainable and a waste of time.
  • Organizations lack a systematic and sustainable way to establish and ensure data quality because of the lack of integration of data quality into the organization's data management and data governance program.

Resolution:

Our four-step, practical approach helps you to improve the organization's enterprise data quality practices while systematically addressing specific data quality improvement initiatives:

  • 1. Define - This step identifies the essential concepts around data quality and gives you a plan to improve IT's core capabilities for fixing data quality on an enterprise scale.
  • 2. Analyze - To begin addressing specific business-driven data quality projects, you must identify and prioritize the data-driven business units. This will ensure that data improvement initiatives are aligned to business goals and priorities.
  • 3. Fix - After determining whose data is going to be fixed based on priority, determine the specific problems that they are facing with data quality, and implement an improvement plan to fix them.
  • 4. Sustain - Without being embedded into the organization's long-term data management program, data quality will remain a band-aid fix. Sustain data quality improvements by incorporating data quality practices into the data governance program.
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