Dalam arsitektur data yang tersusun rapi maka akan mudah mengkoneksikan dengan jalur jalur yang mendistribusikan secara bolak balik dari sumber data kepada penerima data dan dengan mudah memberikan fitur fitur aksesibilitas tambahan yang diinginkan

Thank for the excellent knowledge

🎯 Key Takeaways for quick navigation: 00:57 *🧠 EDM programs aim to uplift data management capabilities, often seeking external validation to ensure alignment with best practices.* 02:57 *🔄 Fundamental practices of enterprise data management are consistent across different industries, highlighting common challenges and solutions.* 04:31 *📚 Dharma and the Data Management Body of Knowledge (DMBOK) are valuable resources for guiding data management initiatives, offering comprehensive frameworks and guidance.* 06:22 *🔍 Enterprise Data Management (EDM) programs focus on targeted uplifts in data management within large-scale projects, regardless of specific project names.* 07:15 *🎯 Metadata and data governance are considered critical capabilities in EDM, serving as indicators of long-term project success.* 09:19 *📊 Successful data governance and metadata management are prerequisites for effective data quality, reference data, and master data initiatives.* 10:21 *⛔️ Avoid the "cart before the horse" approach in data management projects by prioritizing metadata consolidation and data cataloging before integrating data.* 11:40 *🎯 Early focus on critical capabilities like metadata and data governance is crucial for the success of data management projects.* 12:50 *🌐 Clearly defining the scope and boundaries of data management initiatives helps prevent scope creep and ensures alignment with organizational goals.* 14:28 *📈 Prioritization and capability development roadmaps are essential for managing large-scale data management initiatives, focusing efforts on areas of greatest need.* 16:13 *🚫 Avoid expressing capabilities solely in terms of proprietary products and technologies, maintaining focus on data management practices outlined in industry standards.* 18:16 *🏛 Clear governance structures with defined accountabilities and responsibilities are critical for the success of data management programs.* 20:06 *📑 Effective metadata management through an enterprise information catalog is essential for supporting data governance and ensuring data integrity.* 21:45 *📖 Enhancing data literacy across organizations is crucial for improving data understanding, quality, and decision-making at all levels.* 22:27 *📊 Data literacy is crucial at all levels of an organization, not just IT; everyone needs to understand data to run the business effectively.* 23:34 *🛠️ Data stewards are advocates of proper data management within the business, embedded in various departments, ensuring data management practices are followed correctly.* 25:40 *🧭 Stewards act as intermediaries between business units and data management specialists, knowing when to seek expert guidance for complex issues.* 26:06 *📚 Stewards should understand the connection between business processes and data, ensuring alignment between them for effective data management.* 27:42 *🔄 Standardized data domain definitions are vital for managing structured data and ensuring consistency across the organization.* 29:46 *🏢 Establishing a common framework for data management practices, derived from industry standards like DM BoK, enables consistent improvement across the organization.* 30:27 *📊 Defined metrics and key performance indicators are essential for measuring the success of data management programs and processes.* 31:48 *🔄 Managing metadata in heterogeneous environments requires specialized tools and vendors with expertise in managing diverse data sources.* 35:15 *🌐 While some variability in business practices is acceptable, maintaining uniformity in core data management capabilities like metadata, data governance, and quality across the organization is crucial.* 39:25 *🏗️ Building success in enterprise data management often starts with identifying and highlighting small victories or "silver threads" to demonstrate progress and business value.* 45:47 *🛠️ Utilizing metadata is crucial for sensitive data privacy analytics, but it requires proper collection through quality metadata tools.* 46:53 *🔄 Good metadata management tools connect throughout the data lifecycle, aiding in understanding data lineage and provenance.* 48:57 *🧵 Building a "silver thread" requires not only metadata but also understanding executive pain points and tracing lineage to pinpoint data issues.* 51:26 *💰 Focus on data management capabilities, not just buying software, to address business problems effectively.* 52:53 *📊 Start with prescriptive metadata, defining data standards and definitions, to establish a strong foundation for data governance.* 56:49 *💼 Use past episodes of data governance failures to emphasize the importance of data governance to executives and drive organizational change.* 58:39 *📉 Tailor data governance practices to fit the scale and budget of your organization, focusing on principles rather than size.* 01:00:52 *📋 Start addressing data quality issues by identifying episodes that cause concern at the board level, and use profiling along with root cause analysis to understand and improve data quality.* 45:47 *🛠️ Implementing a sensitive data privacy analytics tool relies on collecting metadata first.* 46:53 *🔄 Good metadata management tools should cover the entire data lifecycle journey, from source systems to analysis and reporting.* 47:34 *🔍 Understanding executive pain points is crucial for leveraging metadata and lineage to address business concerns.* 49:37 *💡 Focusing solely on purchasing shiny new tools often leads to failed data management programs.* 51:39 *🎯 Effective data management involves focusing on capabilities rather than just purchasing software.* 52:53 *📊 Starting with prescriptive metadata, such as data definitions, is essential for large organizations to lay a foundation for effective data management.* 56:36 *💼 Using real-life episodes where data governance failures had significant consequences can effectively educate executives on the importance of data governance.* 57:57 *📉 Data governance principles apply to organizations of all sizes, with adjustments made based on scale and resources available.* 01:00:52 *🛠️ Addressing data quality issues often starts with identifying episodes that resonate with senior leadership, followed by root cause analysis involving both technical and business perspectives.* Made with HARPA AI

Thank you, valuable presentation

Thanks for the presentation. Is it possible to have the slides please?

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this is very helpfull.

really insightful!

First, I do this all for my job. Thank you for validating I should be worth a lot more.

I do this all. For my job.

Thank you Sir, it is very helpful for me.

Excellent background which the senior execs and board members could relate to

la1kee #von.ong

A very good presentation clearly explaining both structured and unstructured data and the use of NLU. Thanks Andrew

Hi Jon, very interesting and honest discussion, totally agree tools are not a magic spell that solves anything!

Great talk, thanks!

Informative & useful talk, Jon Gray & team :)

Any chance of reducing the background noise?