On 26 September 1983, Stanislav Petrov took a stand against what his systems were telling him and he may have changed the course of history. Petrov was working as a duty officer at the command center for the Oko nuclear early warning system. This is the place where the Soviets monitored incoming attacks, much like the US command center you remember from War Games. Earlier that month, the Soviet Union shot down a Korean commercial jetliner over the Sea of Japan, claiming that it was on a spy mission. 269 people died in that incident, including a US Congressman. Some at the Soviet Union were fearful of a retaliation strike by the US. Cold War tensions were high.
At the command center, Petrov was getting data that a launch of five missiles had been made in the US towards the Soviet Union. But instead of just reading that dashboard and acting he actually used his own inner analytics system to process the data and decide not to report or react.
Had Petrov reported incoming American missiles, his superiors might have launched an assault against the United States, precipitating a corresponding nuclear response from the United States. Petrov declared the system’s indications a false alarm. Later, it was apparent that he was right: no missiles were approaching and the computer detection system was malfunctioning. It was subsequently determined that the false alarms had been created by a rare alignment of sunlight on high-altitude clouds and the satellites’ Molniya orbits, an error later corrected by cross-referencing a geostationary satellite.
Petrov later indicated the influences in this decision included: that he was informed a U.S. strike would be all-out, so five missiles seemed an illogical start; that the launch detection system was new and, in his view, not yet wholly trustworthy; and that ground radars failed to pick up corroborative evidence, even after minutes of delay.
- Wikipedia contributors. "Stanislav Petrov." Wikipedia, The Free Encyclopedia. Wikipedia, The Free Encyclopedia, 26 Sep. 2012. Web. 26 Sep. 2012.
I’ve always wondered if the system he was using had a bunch of fancy dashboard features, like shiny 3D pie charts, moving average lines and drill down capable reports if he would have been able to not trust the data. I’ve seen this sort of over-trust of data with data model diagrams. It seems the prettier or more advanced the presentation of the data is, the more people want to believe it is right. In fact, I’ve learned to present draft documents to people on my teams with hand-written notes/comments on them to sort of "break the ice" to show people that they are drafts. A modern solution might have included some sort of decision making guidance that say "Confidence Factor of Attack: 99%" or something like that. And it would have been highlighted by some sort of red bar, showing just how confident the system was based on the data – bad data, it turns out.
More details about Petrov and his actions in the video above from History.com
You can now download the recording from this past week on Modeling What Matters – Data Modeling Throughout, Beyond the Enterprise. We had a great time chatting about the state of data modeling in a world of data sharing, standards, business alignment and NoSQL.
A wonderful set of panelists, too.
Registration is required, but you really should be registered on the Information Management website already. Do it.
Also, at the end of the show I got a shout out for my presentation with Thomas LaRock (@sqlrockstar), Database Design Throwdown: The Blunder Games at ERWorld coming up in October. You should go register now for that, too.
…can you read the formulae?
This Thursday, 20 September 2012, I’ll be joining a great group of data professionals to talk about balancing the need for project speed with data modeling efforts. This is a topic near an dear to heart — I’m developing courseware right now for advanced data modeling concepts on modern development efforts. DM Radio, hosted by Eric Kavanagh and Jim Ericson, is always a fun and thoughtful take on modern IT practices. Plus a few of us are known for our snarkish insights into the data world.
The value of data modeling continues to grow in new directions. This is partly due to the lure of cloud computing, but also because of the increasingly interconnected world of enterprise partnerships. As always, the need for speed and efficiency prevails, as well as the desire to reduce redundancy and thus provide a clean view of an organization’s information architecture. Fine-tuning Master Data Models is another goal of the modern enterprise. What to do? Register for this episode of DM Radio to hear Hosts Eric Kavanagh and Jim Ericson interview data modeling expert Karen Lopez of DAMA, plus Donna Burbank of CA Technologies, David Dichmann of SAP Sybase and Lovan Chetty of Kalido
Registration is required, but it’s free. Chat with you on Thursday!
I’ll be leading two data modeling courses in Des Moines, Iowa the first week of October. Register now as spots are limited.
Basic Data Modeling: For New Data Modelers
Registration and Networking: 7:45 AM Monday
Monday and Tuesday 8:00 AM to 5:00 PM and Wednesday 8:00 AM to Noon
In this introductory workshop, Karen Lopez covers the theory and skills required to being working on data models in an enterprise environment on modern project teams.
With demonstrations and several exercises, this training course provides the basics and tests attendees to demonstrate their new skills. This course follows a workshop format, with both individual and team exercises.
…but this isn’t your average "Here are all the Normal Forms, now go create a model" course. Our goal is to make data modeling relevant to modern development practices and tools. We’ll talk about some of the pain points modelers feel, why developers and DBAs sometimes don’t see the same beauty in our data models and how to ensure everyone, IT and business, sees the value of data modeling and data modelers.
As a basic course, it is not expected that attendees will significant data modeling experience. However, we do expect them to have more than two years of hands-on information technology skills at the enterprise level. We offer advanced courses for those who do have data modeling experience. It is also common for attendees of the Basics course to sit in on the Advanced courses to get a better understanding of the issues that experienced data modeler see on a regular basis.
Advanced Data Modeling: Be Happier, Add More Value and Be More Valued
Registration and Networking: 12:45 PM Wednesday
Wednesday 1:00 PM to 5:00 PM and Thursday 8:00 AM to 5:00 PM
In this advanced workshop, Karen Lopez covers how to make data modeling more relevant in 2012 and removing pain points for modelers and other team members. It includes how to be successful in an agile/scrum environment, how to make models valuable in a NoSQL project, how to better work with DBAs, Developers, Project Managers and how to sound and be more valuable.
With demonstrations, exercises and peer-discussions its goal is to give attendees the attitude and skills to add value and be more valued on modern project teams. This course follows a workshop format, with both individual and team exercises.
As an advanced course, it is expected that attendees will have more than two years of hands-on data modeling and database design skills at the enterprise level. We offer a basics course for those who do not have this experience or feel they need a refresher before participating in this course.
Karen Lopez is a Sr. Project Manager and Architect at InfoAdvisors. She has 20+ years of experience in project and data management on large, multi-project programs. Karen specializes in the practical application of data management principles. She is a SQL Server MVP, an advisory board member for Zachman International, and an Advisory Committee member of DAMA International.
Karen is also the ListMistress and moderator of the InfoAdvisors Discussion Groups at www.infoadvisors.com.
Intro class: $500 for DAMA members, $550 for non-members
Advanced class: $300 for DAMA members, $350 for non-members
Important: Karen strongly recommends purchasing a copy of Data Modeling Essentials 3rd Edition by Graeme Simsion and Graham Witt. These classes will be based on this book.
See DAMA Iowa event page for information about registration and cancellation policies.
For more than a decade I’ve worked on teams that accredit college and university programs in computer science, information systems, and technology. For the most part the criteria we use for computer science programs has been traditional: algorithms, programming, math, software engineering, components and architectures, models of computation, analysis of algorithms, fundamentals of program specification and verification, computational complexity, automata, etc. There are requirements for humanities and other subjects, but it is rare to see programs remain unaccredited if they were missing them. A sample set of criteria can be found on the CIPS website.
One of the things that annoyed me during computer science accreditation visits were the all too common references to women not being able to succeed in CS programs. When I’d ask why, I was usually given one of these types of answers:
- Women are incapable of thinking of complex topics
- Women just don’t want to learn computer science
- Women don’t want to study in programs where they are outnumbered
- We’d have to dumb down the programs too much (see point 1).
It took all my might to simply record their responses and not fight it out. I figured their answers might be a reflection of their program administration and management than of the women they are running out of their programs. For instance, a computer science program chair told me directly that if he had to dumb down his program enough to get women to stay, "no one would be able to log in". Tell me what sort of rewarding student experiences the females in his classes have on a daily basis?
Applied vs. Research Programs in Computing
One of the issues computer science programs have is managing the fact that they often exist as a research program but many students are more interested in studying computing at an applied level. In other professions, applied means just that – learning to apply sciences in a practical, real world environment. Other professions produce professionals just that way: lawyers, doctors, engineers, teachers. For the most part, they study in applied programs. But in the research world, applied is the equivalent of dumbed down. So many computer science programs are designed to produce researchers even though the vast majority of students are there not to become researchers, but practitioners. And yet most women are drawn to professions where they can see a direct link to studying and working on projects that will change the world.
I was thinking about this while speaking on the #SQLSat157 San Diego WIT panel this past weekend. When I got home, I found this great interview with Maria Klawe, president of Harvey Mudd College: Q&A What Women Want in the Communications of the ACM. One of the questions was exactly what I experienced when choosing a program of study all those years ago:
You’ve talked before about the importance of teaching practical applications from the start, rather than waiting until students have mastered the building blocks.
We know from research that for women and minorities, the attraction of computer science is what you can do with it. It doesn’t mean they are not interested in complexity theory or other esoteric parts of the field, it just means that that tends to be the driving motivation. And in our experience, it’s not like women take one course or go to the Hopper conference and say, "I want to be a computer science major." It’s more like, you take one course or go to the Hopper conference, and you take the next course. And then you take the course after that, and by then you’ve taken three courses and you’re going, "Oh, I’m actually good at this, and it gets me summer jobs. Maybe I should be a CS major."
The curmudgeon computer science chair and his colleagues also had thoughts on programs that shifted their marketing and delivery, but not their content, to appeal more to women and minorities: it was cheating. As an IT professional, I say "Let’s cheat, then". Let’s ensure that computers science and other technology programs can step up their game to be more appealing. As a business person and someone who interviews candidates for jobs, I want to see people who understand theory AND application of it all. Cost, benefit , risk and all. Saving the world. Making a difference.
Information systems and technology programs are generally applied programs of study. However, we tend to see them as lesser siblings of computer science. Maybe we shouldn’t, especially as employers for organizations that don’t directly hire researchers.
Do we need theoretical, research-only computer science programs? ABSOLUTELY! But we also need IT professionals who can fit solutions into a corporate environment. One that can’t just think in terms of theory. And I want a more diverse, educated workforce available to hire from. Not just for the numbers, but because we get better solutions. But in order to get this, our programs of study need to step up.
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- Karen Lopez on Strutting: We all Know When You are Doing It. So Stop.
- Joey D'Antoni on Strutting: We all Know When You are Doing It. So Stop.
- Karen Lopez on Strutting: We all Know When You are Doing It. So Stop.
- Thomas LaRock on Strutting: We all Know When You are Doing It. So Stop.
- Karen Lopez on Strutting: We all Know When You are Doing It. So Stop.
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