Article 74
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The book provides an overview of the key topics, but any printed material is always bound to go out of date. That is exactly why we feel it is necessary to make a number of new articles available here to update readers on the most recent thinking and advances. If you would like to submit an article we would be happy to hear from you, please contact us using the form below.
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Article 74 | Counting Edwards: A precise definition overcomes unexpected complexities |
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Article 73 | Flying over Yugoslavia: Having an elegant data architecture will pay dividends in the long run |
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Article 72 | The slowest soldier: Why the handling of technical data is more important than your CEO thinks it is |
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Article 71 | CDO: Data or Digital?: What does CDO stand for, data or digital? |
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Article 70 | Core Competencies: Is Data Management a core competency for Oil Companies? |
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Article 69 | A mountain of pots: Failing to innovate to avoid impacting vested interests has been a theme for some time |
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Article 68 | Training Unicorns: If skills are not available perhaps we should put more effort into training our people? |
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Article 67 | Declaring ignorance: Admitting what we don't know can be hard for most people |
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Article 66 | Dreams of integration: If integration is such a good thing why is there so little of it in the upstream oil industry? |
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Article 65 | The right tool: Knowing which tool to select is an important skill, |
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Article 64 | Good leaders making stupid decisions: One reason rational executives sometimes make bad choices |
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Article 63 | Different levels of impact: Data and people have more impact than tools and processes |
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Article 62 | Model free data: Big data doesn't remove the need for thinking |
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Article 61 | The dragon of data quality: Data quality relies on many different concepts |
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Article 60 | The gap between intent and execution: Implementing things is often hard, so we should always document what was intended as well |
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Article 59 | How hard can it be...: Writing a workable data standard is really hard |
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Article 58 | The joy of posters: Thinking about complex topics in the form of a poster can give good insights |
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Article 57 | Pick a project, any project...: Selecting from different opportunities requires data |
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Article 56 | Investment's Impact: In the long term its easy to see what was important, how do we find out sooner? |
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Article 55 | A home of our own: Do we need a society to regulate data handling professionals? |
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Article 54 | Holding back the tide: The data environment is determined by the actions of data users |
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Article 53 | Data Management tools: The current data mess is a people issue, new technology won't ever solve it |
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Article 52 | Telling tales: Every presentation tells a story, there's no reason to be boring |
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Article 51 | Cost cutting versus investment: Should oil companies focus on expansion or costs? |
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Article 50 | Finding what isn't there: In order to see what is missing we must know what to expect |
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Article 49 | Body of Knowledge: What should every oil industry data manager know? |
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Article 48 | Size isn't everything: No single metric can every provide the level of detail needed to assess data handling |
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Article 47 | On groking mundanes (again): If you want communication to be clear you must worry about the words specialists use |
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Article 46 | Franchising TV shows: A body of knowledge must be extensive rather than brief |
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Article 45 | The professional's tale: What does it take to be professional? |
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Article 44 | I only changed one line...: When manipulating data paranoia is your friend |
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Article 43 | Modelling oil company data processes: The best models get into details |
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Article 42 | Corporate strategy models: Sometimes ommiting the details can improve a model |
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Article 41 | Decisions and decision trees: The 'value of information' process has limited application to working out overall business value |
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Article 40 | Why time budgets don't work: Basing a business case on the time saving when looking for data is questionable at best |
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Article 39 | Proving value: How can you prove the value data delivers? |
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Article 38 | Wasting time with pictures: Why you should worry more about perfecting pictures |
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Article 37 | Giant robots and small teams: Changing the sizes of robots, software teams and oil companies means changing the way things are done |
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Article 36 | Being certifiable: Just because there is no ethics committee doesn't mean we can reveal client details |
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Article 35 | Adopting standards: Getting a new standard adopted requires more than just proving it will improve things |
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Article 34 | Having your cake: Being able to describe things at different levels is an essential skill |
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Article 33 | Lumpers and Splitters: Do you mentally join things together or split them apart? |
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Article 32 | Planning and being wrong: Failure to plan because of fear of analysis is always a bad approach |
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Article 31 | The value of paella: Working out the costs and added value can be tricky even for simple things |
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Article 30 | Little prospects for big data?: How does the 'new' concept of big data apply to the upstream oil industry? |
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Article 29 | New paradigms: Learning new concepts, new ways of seeing the world, is almost always a good thing |
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Article 28 | Convergent Explanations: Understanding interactions is harder and more rewarding |
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Article 27 | Pursued by a bear...: How much is it worth to be slightly better than your competition? |
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Article 26 | IT or not IT, that is the question: Should data management be part of the IT group? |
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Article 25 | Which data is 'managed'?: Why does traditional data management focus on the least valuable data? |
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Article 24 | Self fixing data: Some data users have strange beliefs |
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Article 23 | T-Shaped People: Good data managers need to understand a wide range of topics |
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Article 22 | A bucket of crabs: Not everyone who blocks your change is being spiteful, but an experiment suggests how to deal with those that are |
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Article 21 | Better than average: Boasting about past success is part of the job |
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Article 20 | Good and Bad Hype: Overselling simple ideas is necessary to get new things built |
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Article 19 | Proving Yourself Wrong: Geological data is used forensically rather than assembled |
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Article 18 | Data, Information and Knowledge: Understanding the difference between data, information and knowledge |
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Article 17 | The maturity of Maturity: The concept of Maturity can now be considered mature |
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Article 16 | Spreading Unhappiness: Ensuring everyone is equally unhappy can deliver great results |
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Article 15 | Deal or No Deal: TV Game Shows can provide great lessons about risk |
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Article 14 | The treachery of data models: A data model of a thing is not the thing it describes |
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Article 13 | Bumps on the Head: Is Data Management a real topic? |
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Article 12 | Benchmarking: How can we compare data handling efficiency across companies? |
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Article 11 | Only what will be enforced: Should data managers change their whole outlook? |
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Article 10 | The Flood of Data: Is the deluge of data about to overwhealm us? |
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Article 9 | Test Yourself: A quick test of your oil industry data management knowledge |
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Article 8 | We need a Map: A good map helps understanding, even when its not perfect |
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Article 7 | A Tale of Long Ago...: Why are oil industry data standards so hard to define? |
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Article 6 | We need a Library: If we want interesting conferences we need to make old papers easy to find |
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Article 5 | Return of the Notorious Iceberg: Is it true that 70% of company data is unstructured? |
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Article 4 | The Joy of Checklists: Checklists make it easier to be consistent, why would you not use them? |
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Article 3 | The Perils of Flexibility: Is having flexible software always a good idea? |
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Article 2 | Competitive Advantage: Is it time to stop telling other oil companies how you are managing your data? |
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Article 1 | Data Quality, Fish and Ska music: A short article about data quality dimensions, and how they relate to fish and ska music |
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Previous Comments (newest first)
20 May 2014
camiseta espaċ¸½a 2014
Greetings! Very useful advice in this particular post! It is the little changes that will make the greatest changes. Thanks for sharing