![Global Enterprise Data Management Market](https://res.cloudinary.com/devmoveo/image/upload/v1706192375/skyquest/report/phpNasryt.jpg)
Report ID: SQMIG45B2084
Report ID:
SQMIG45B2084 |
Region:
Global |
Published Date: July, 2001
Pages:
165
|
Tables:
87 |
Figures:
76
In November 2022, IBM Corp. unveiled Business Analytics Enterprise, a comprehensive suite of tools designed to enhance business intelligence capabilities. This suite includes features such as planning, forecasting, budgeting, and dashboard functionalities, aimed at empowering enterprises to dismantle data and analytics silos. By breaking down these barriers, organizations can make informed decisions based on data insights more efficiently and effectively. This suite enables enterprises to navigate through unpredictable disruptions and respond promptly with data-driven strategies.
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REQUEST FREE CUSTOMIZATIONThe key element of enterprise data management (EDM) is data governance. Data governance is the set of policies, processes, and structures that ensure that data is managed effectively within an organization. It includes activities such as data quality management, data security, and data compliance.
Enterprise data management (EDM) and Infrastructure as a Service (IaaS) are two different approaches to managing data. EDM is a holistic approach that encompasses the entire data lifecycle, from collection to storage to analysis. IaaS is a more focused approach that provides the underlying infrastructure for storing and managing data.
Achieving enterprise data management can be challenging due to various factors. Some of the main challenges include ensuring data quality, integrating data from different sources, protecting data security, establishing data governance policies, designing a scalable data architecture, developing data analytics capabilities, and ensuring compliance with data privacy regulations.
Enterprise data management initiatives typically focus on key activities such as data governance, data quality management, data integration, and master data management. Data governance involves establishing policies and procedures for managing data. Data quality management ensures that data is accurate, complete, and consistent. Data integration involves integrating data from different sources into a single view. Master data management creates a single source of truth for key data elements. These activities are critical for effective enterprise data management.
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Report ID: SQMIG45B2084