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It Runs on Data

@ItRunsOnData

Where people and data meet

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The peril of treating social media as an employment source dataheretics.substack.com/p/the-peril-of…


IT thinking: Logs and log trails are there to check when things go wrong. Auditor thinking: Unchecked logs are not a control. (Auditors think a lot about controls, which are things that either stop bad things, detect bad things, or fix bad things). dataheretics.substack.com/p/unchecked-ou…


Can you be the best in the market without being the best possible? Leapfrogging as a technology strategy. dataheretics.substack.com/p/leapfrogging…


Keep these 20 things off your CV to get a job. dataheretics.substack.com/p/keep-these-2…


When trying to design a system, deciding in advance what characteristics matter to you is nearly always going to be wildly more successful than arbitrarily deciding that your favorite thing is exactly what you need for a task. dataheretics.substack.com/p/yes-virginia…


Administering IQ tests is a fearful stance and working for a terrified company is awful. dataheretics.substack.com/p/iq-tests-sig…


Early approaches to data governance focused primarily on data quality and technical controls, but contemporary frameworks recognize the importance of organizational culture, stakeholder engagement, and business alignment.


The concept of data governance has evolved significantly over the past two decades, transitioning from basic data management practices to sophisticated frameworks that encompass strategic, operational, and tactical dimensions.


If you’re a candidate for a job, and they’re asking for an IQ test? They’re testing for conformity in two ways, up front: Are you desperate enough to take the test? Are you conformant enough to score well on the test? dataheretics.substack.com/p/iq-tests-sig…


Data governance has emerged as a critical organizational capability in the digital age. Successful data governance requires a holistic approach that integrates people, processes, and technology while maintaining alignment with business objectives.


Organizations that prioritize the development of data stewardship excellence will be better positioned to leverage their data assets for competitive advantage in an increasingly data-driven business environment.


Many good candidates will not play the IQ test game Some will refuse the IQ tests outright, even if they’re good at them. Why should they take an IQ test for a job? dataheretics.substack.com/p/iq-tests-sig…


The interdisciplinary nature of data stewardship suggests that diverse educational backgrounds can contribute to excellence in this field. Professionals with backgrounds in computer science and business administration bring valuable perspectives to data stewardship roles.


Excellence in data stewardship requires continuous professional development. Formal education in data management, information science, or related fields provides foundational knowledge, professional certifications offer specialized training in specific tools and methodologies.


IQ tests reward conformity of answers, not creativity. dataheretics.substack.com/p/iq-tests-sig…


Financial services organizations face stringent regulatory requirements and complex data integration challenges. Data stewards in this sector must understand financial regulations, risk management principles, and the critical importance of data accuracy and decision-making.


Healthcare requires specialized knowledge of medical terminologies, clinical workflows, and specific regulations such as HIPAA. Healthcare data stewards must understand the unique challenges of managing patient data while supporting clinical research and improvement initiatives.


The skills required for research data stewardship include understanding of research methodologies, familiarity with academic publication processes, and knowledge of research data sharing requirements.


Either IQ tests are biased or Angolans are stupid. dataheretics.substack.com/p/iq-tests-sig…


Academic and research environments present unique challenges and requirements for data stewards. Research data stewardship involves managing complex data lifecycles, supporting open science initiatives, and navigating intricate intellectual property considerations.


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