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41 items found for ""

  • RESOURCES | Anything Awesome

    Data Resources Below these are a number of resources that others have found helpful. I owe my success to the kindness and generosity of others so if there are other items that I can share please let me know! (Rebuilding slowly underlined links are populated.) The data doctrine Data Governance Frameworks Collection Introduction to Data Governance (Chapter Preview) Modernizing Data Governance (30 minute talk) Repository in a Week House of Structure (Article by Warren Keuffel) A TDWI talk on Data Literacy CDO Job Description What is data? John Zachman's Framework V3 (mirror site) Four data truths Understanding the 2018 federal Data law: Much more than open data Seven Sisters Video BigCo Case Study Download (JDQ) Reverse Engineering of Data (IBM Systems Journal) A panel discussion of various aspects of data practice maturity The Anything Awesome YouTube Channel

  • Anything Awesome | improving your data and data practices

    [ Bad Data ] + Anything Awesome [ will always yield ] Bad Results Data must be the heart and foundation of all organizational planning and operational efforts Since 1981, I have been helping people and organizations around the world with data and data leadership challenges. I have shared my accumulated knowledge far & wide and provided opportunity for others to do so also. [ Uttering my name three times generally causes me to appear ]. Some modest contributions in the form of data management resources collection exist at this site but of more use is our community of collaborators . Join us for monthly DataEd webinars . Anything Awesome's purpose is to connect your organization with needed data resources. ​ These have ranged from student/class research projects at Virginia Commonwealth University ( where I am tenured ) to sponsored research to my immersions. I have participated in ten, multi-year immersions with organizations ranging from Walmart to Deutsche Bank to Wells Fargo to the Department of Housing and Urban Development to Nokia to the US Army–in the process literally saving organizations a total of more than $1.5 billion (USD)! ​ Anything Awesome is partially owned by VCU and exists to help organizations connect with resources that can assist them with their data challenges . Please do continue to bring me your data challenges of all shapes and sizes and I will connect you with capabilities that can help. Book an office 1/2 hour or just pick up the phone . If this seems vague, it is intentional because there are literally so many options . This is the best way to keep attention focused on the fuel that awesome things consume–your data ! Because it is always true that ...

  • TDWI Data Literacy Talk | Anything Awesome

    I am proud that some of these have been translated into Dutch, Italian, Spanish, Portuguese, and Mandarin. Other freely available data program resources are available to download here . Some books that may be of interest TDWI Data Literacy Talk FORT LAUDERDALE Chapter Business: 00:01 Talk Begins: 23:23 More than half of work is accomplished by knowledge workers–usually defined as those who must “think for a living” [Davenport, 2005]. I contend that all knowledge workers work with data. Since most learn about data individually (if at all), the opportunity to gain from communal or best practices learning has not been present. Most refer to this as a lack of data literacy. Whether applied at the individual or organizational level, literacy is a binary concept and our data needs are more varied. Data proficiency and data acumen are more descriptive/useful terms and these should also be used to describe today's organizational data knowledge requirements. This program will describe five specific data knowledge requirement levels and objective behaviors that must be demonstrated by those operating at each level. Lack of this data knowledge has so far hindered society from fully realizing our collective potential benefits. More importantly, organizations adopting these data knowledge requirements can directly and immediately improve organizational knowledge worker productivity. Delegates will: • Learn why the term data literacy is insufficient to describe the challenge and how the progression from literacy ➜ proficiency ➜ acumen is more operationally viable • Understand five data knowledge requirements levels in terms of their data leverage type, data skills type, ethical perspective and behavioral focus • Be able to match data knowledge requirements levels with types of organizational requirements • Begin to estimate the dollar ranges of potential knowledge worker productivity improvements in their organizations

  • Why 'Anything Awesome"? | Anything Awesome

    I am proud that some of these have been translated into Dutch, Italian, Spanish, Portuguese, and Mandarin. Other freely available data program resources are available to download here . Some books that may be of interest Why "Anything Awesome?" that this is a concise [ ~ 1 minute] explanation as to why I find working on this end of these challenges is most rewarding! (you should also enjoy the accompanying audio-sampled in homage to deep thoughts on complex challenges)-so don't forget to turn up the sound!) (you may need to click play below)

  • Data Doctrine | Anything Awesome

    The Data Doctrine™ V2 Objective measures for improving data outcomes It is fine to say that "we want to be data-centric, data-first, data-driven, data provocateurs but without commonly agreed upon definitions and objective measures, it is all just hand waving, isn't it? ​ Directly building on the original agile manifesto . Todd and Peter spent some time producing the first version. Published in 2017, hundreds have joined various efforts to improve our collective understanding of what are the concrete steps required to achieve better data outcomes. We have updated the original and are anxious for feedback from the community. We are uncovering better ways of developing IT systems by doing it and helping others do it. Through this work, we have come to value: • data programmes - driving IT programs • informed information investing - over technology acquisition activities • stable, shared organizational data - over IT component evolution • data reuse - over the acquisition of new data sources While there is value in the items on the right, we value the things on the left more Several of our colleagues have had complimentary thoughts in this area also, please check their efforts out as well (links and more below) Visit and learn more about the data-centric manifesto The Data-Centric Manifesto Dave McComb and his team have been at this for many years. His need for this change is an excellent straight forward argument for clearer thinking on this subject. Visit and learn more of the leader's data manifesto The Data-Leader's Manifesto A group including Nina Evans, John Ladley, Danette McGilvary Kelle O'Neal James Price, and Tom Redman published in 2017 and have garnered hundreds of signatures of support. ​ Data-centric computing - Wikipedia article Database-centric architecture - Wikipedia article Introduction to Data-Centricity by Kevin Doubleday - The Data-Centric Architecture treats data as a valuable and versatile asset instead of an expensive afterthought. Data-centricity significantly simplifies security, integration, portability, and analysis while delivering faster insights across the entire data value chain. This post will introduce the concept of Data-Centricity and lay the framework for future installments on Data-Centricity. Data-centric Architecture — A Different Way of Thinking - Data-centric architecture (or model) is a solution that addresses the issues of conventional capital project methodology and delivers positive results. Data-centric execution architecture has been around for a few years and is becoming more popular in the energy sector, where many owners and operators work with a specialized system integrator. Why and how to adopt a data-centric architecture - Data has become one of the most valuable assets in the enterprise. IT teams must make changes -- both culturally and technically -- to ensure their strategy reflects that. The Difference Between Data-centric and Data-driven by Carol Dunn - The recommendation that companies become more data-centric sounds like a great idea. Most companies have the ability and means to accumulate data – in some ambitious cases that turns out to be LOTS of data. Basically, such companies are driven by data. But that’s not the same as being data-centric. Additional recommended reading

  • CDO Job Description | Anything Awesome

    I am proud that some of these have been translated into Dutch, Italian, Spanish, Portuguese, and Mandarin. Other freely available data program resources are available to download here . Some books that may be of interest CDO Job Description Reporting to senior leadership , the CDO is the data leader responsible for evolving data practices to better support the organizational mission. Improving organizational data practices extends the CDO’s responsibilities to every knowledge worker in the organization. Empowering knowledge workers with better data practices is the single most important productivity improvement that organizations can make. The CDO is responsible for growing not just an organizational data team but for operationalizing an organization-wide conversation and focus on data innovation, improvement, and value. The CDO establishes, fiduciary responsibilities through stewardship, aimed at leveraging data assets and organizational capabilities and creating a climate of data sharing. Some of this can be accomplished by leading the organizational data governance program to effectiveness. The data leader will be required to understand how to appropriately incorporate change management capabilities to the substantive people, process, and ethical challenges that will support the new data focus. As an organization’s sole, non-depletable, non-degrading, non-rivalrous strategic asset, its data has likely been suffering from data debt . The CDO must nurture programs to improve useful subsets of organizational data and simultaneously reduce the impact of data debt. Data volume and debt necessitate prioritization and the CDO must incorporate a strategic approach to improving the value of an organization’s data. For data’s true value to become apparent, it needs to be understood as a defined part of the organizational value chain . The CDO is responsible for appropriate aspects of monetization to the organizations data. This requires architecting organizational data requirements in the context of present and future business operations. These requirements identify data products directly supporting business value. (There are more Chief Digital Officers than there are Chief Data Officers.)

  • Office Hours | Anything Awesome

    Book a call w/ Peter! To book a call w/ Peter to discuss anything data, click a link, schedule a session! How do you think I have met so many interesting collaborators over the years? Click here to book a virtual session on my calendar. Zoom or TEAMS are automatically available as conferencing options. ​ if the available hours do not work for you, then please call, text or email me for other options

  • Live DataEd Webinars | Anything Awesome

    Data Stewards: Defining and Assigning 12 March 2024 19:00 UTC (2:00 pm NYC) Register for this free Webinar DataEd YouTube Channel Review last month's Webinar Chat with Peter Data stewardship is a relatively new role. Likely they also possess the best window into the business. What most data stewards are not provided in advance is a roadmap to eliminating data debt as a necessary prerequisite to smoothly functioning data governance. Well thought out plans crash and burn on the rocks of reality–the existing data environment! Several issues are illustrative of these types of challenges: – The specific role of data ownership – Full time versus part time support/resourcing – Generally low data literacy everywhere – The confounding complexity of data debit Data stewards are the implementation arm of data governance. They are also the first line of defense against bad data practices. Whether it’s data profiling or in-depth root cause analysis, data stewards ensure the organization shared data is reliably interconnected. Whether starting or restarting your data stewardship program, success comes from – Understanding the cadence/role of foundational data practices supporting organizational operations – Proving value with tangible ROI – Improving effectiveness/efficiencies using organization-wide insight – Comprehending how stewards need to be multifunctional and dexterous, especially at first – Integrating the role of Data Debt fighting

  • Data (What is?) | Anything Awesome

    I am proud that some of these have been translated into Dutch, Italian, Spanish, Portuguese, and Mandarin. Other freely available data program resources are available to download here . Some books that may be of interest What is Data? (based on definitions by Dan Appleton from Data-Driven Prototyping Datamation November 1983) 80% of data is ROT ​ The only argument I ever get is that "Our data is closer to 90% ROT" With 5 to 10 times as much chafe as wheat, organizations can benefit tremendously by taking a data centric approach to organizational improvement. Turns out to be all about demonstrating what happens in the organization when better data practices yield improvements . Some just do not understand data! I took this picture of a door at a resturant one day - it just doesn't seem to be the right combination? The Hitchhikers Guide to the Universe has a subplot that revolves around the fact that the meaning of life is 42. Let’s start at the most granular level–data is any combination of a fact and a meaning. For years we have asked groups: what does the number 42 mean? The answer is usually shouted back as: it's the meaning of life! A bit of explanation to the group and now we all share an understanding that 42 (fact) can be paired with a (meaning) “it (42) is the meaning of life.” That combination of fact and meaning constitutes a bit of data. Data is not the new Oil ​ Applying these same concepts to data makes little sense. The relative cost of acquiring data is most expensive the first time one uses it. Costs diminish with each subsequent reuse. Therefore, organizations are encouraged and financially rewarded to reuse data. (While Stylumia were not the first to coin this phrase, I did grab their very nice picture.) Oil’s value is based on scarcity. Data does not work this way, and applying these same concepts to data makes little sense in the information economy (see Barlow 1994 and the Atlantic's excellent roundtable on the subject). The relative cost of acquiring data is most expensive the first time one uses it. Costs diminish with each subsequent reuse. Data increases in value the more it is reused. Therefore, organizations are encouraged and financially rewarded to reuse data. Image from: https://medium.com/stylumia/data-is-not-the-new-oil-its-the-new-soil-212cf9ae2e4f

  • BOOKS | Anything Awesome

    I am proud that some of these have been translated into Dutch, Italian, Spanish, Portuguese, and Mandarin. Other freely available data program resources are available to download here . Some books that may be of interest Some books that may be of interest I am proud that some of these have been translated into Dutch, Italian, Spanish, Portuguese, and Mandarin. Other freely available data program resources are available here . Click any title below to learn more.

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