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I typically start off each new year with a more personal blog topic, something that’s occupied my thoughts over the holidays, but this year I instead opened with a couple of NASA-themed posts. This turned out to be fortunate in that it now allows me to get somewhat personal on this, my penultimate blog post for the Value Alley. We’ll be retiring the Value Alley after my final, closing post next week, and I’ll be moving my analytical musings over to SAS Voices, our thought leadership focal point, where I'll join the rest of our talented staff addressing a broader analytics conversation to a wider audience. But until then, Leo still has a few remaining things on his mind.
The title for this post comes from my oldest, Garik, who midway through his college experience decided that he wasn’t getting what he wanted and needed from the standard curriculum. He not only took it upon himself to call “time out” and rework his final two years, but he also decided to share his insights with his fellow students in a one-credit course he developed and taught, based somewhat on this TEDx talk he gave at NC State: “How LSD changed my life – Students taking responsibility for their own education”; “LSD” in this case standing for Life Style Design, the formal name of his course.
One of the class exercises consisted of reimagining the typical K-12 program, with a focus on “teaching to the problem rather than to the tool”, Read More
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You’ve likely heard the news that the Google DeepMind “AlphaGo” computer not only beat a human expert at the game of Go, defeating the European Go champion, Fan Hui in five straight games, but also beat the reigning world champion grandmaster, South Korea’s Lee Sedol, 4 games to 1.
Go is considered to be a significantly more difficult game for a computer to tackle than chess, if only because of the vastly greater number of possible moves over a much larger playing field. Chess has on the order of 1040 possible legal and realistic positions in a 40-move game; Go can have up to 10360, give or take a few tens of orders of magnitude (as a point of reference, there are approximately 1080 particles in the visible universe).
When Deep Blue beat world chess champion Gary Kasparov back in 1997, it did it with a brute force approach – a massively parallel computer that would typically search to a depth of between six and eight moves, and up to a maximum of about twenty moves in some situations. It was an Expert System (not AI), with separate programing modules/libraries for openings, end games, and middle game strategy and tactic evaluation. All the legal moves and rules had to be programmed into it, and it could not learn as it went (although its programmers made adjustments after each game).
AlphaGo, however, is a true AI machine, Read More
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Do you like a good horror story? Then may I suggest “Future Crimes” by Marc Goodman. When it comes to this genre, Wes Craven, John Carpenter and Stephen King have got nothing on Goodman, primarily because Goodman’s story is non-fiction.
Scene 1: The present – Your workstation or data center
Whether it’s your personal or corporate data that’s at risk, the magnitude of that risk is far greater than the uninitiated are currently aware. We’ll skip right over the now mundane cyber threats of zero-day exploits, ransomware and spoofing, and get right to a few of the more vile and contemptable approaches currently on the crime market:
- “Girls Around Me”: a downloadable app that uses geolocation data from mobile devices and social apps – a favorite of stalkers.
- “SpyEye”: A man-in-the-middle screen spoof that not only captures all your log-in credentials and drains your bank account, but records exactly how much was taken and adds that amount back into your fake current balance before presenting it to you, so as to buy enough time to clear the settlement period.
- Mobile POS scanners, like the one carried by this guy on public transit, which, when pressed close to your wallet, will automatically charge your contactless debit card.
- GPS signal spoofing that can redirect an 18-wheeler to the wrong warehouse or a cargo ship to the wrong berth, where the bad guys are ready and waiting to unload it.
Scene 2: The future – Rising action and danger
Cybercrime is a big business, run by professional looking organizations Read More
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When the likes of Elon Musk and Stephen Hawking go on record warning about the dangers of AI, it’s probably prudent to take notice. However, before rushing off into full panic mode, some definitions and perspective would be in order.
The type of artificial intelligence Musk and Hawking are referring to is known as Strong AI, or AGI (Artificial General Intelligence). This is the level at which a machine could readily pass itself off as indistinguishable from a human in cognitive, perceptual, learning, manipulative, planning, communication and creative functions - a thinking machine that can pass the Turing Test. We’ll close with some perspectives on Strong AI, but first let’s take a look at Weak AI, also known as Applied AI or Enhanced Intelligence (EI). Read More
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Like most boys my age at that time, I wanted to be an astronaut. Fate, however, intervened, in the form of nearsightedness, so I had to find an alternative occupation. Coming to my rescue for the launch of Apollo 11 was my father, who presented me with a huge booklet that broke down the entire mission into each of its key components in great detail. I had my answer – I was going to be a flight controller, and maybe even one day a flight director like Gene Kranz. I had the entire Saturn V launch sequence memorized from the transfer to internal power at the T-minus fifteen minute mark down to liftoff, and would impress my father by beating the public affairs announcer to the punch by announcing, “initiating automatic sequence in 5, 4, 3 …”
ISS Mission Control, courtesy of NASA
A fascinating book came out last year called, “Go, Flight!: The Unsung Heroes of Mission Control". In the process of telling the personal stories, recounting the heroic events, and describing the various roles in the mission control center (MCC), one recurring theme caught my attention – that of the communications (comms) loop.
The three primary roles in the MCC are: Read More
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Discussing strategy, and what we mean by it, can be a confusing and sometimes unproductive undertaking. Considering its different uses as a noun and an adjective, defining our terms is a good place to start:
- Strategic thinking: Characterizing the environment, identifying and assessing risks, and developing and evaluating options.
- Strategic goals: High level goals immediately derived from the organization’s vision and mission.
- Strategy: The general approach to how a specific goal / objective is achieved, with consideration given to core values, vision and mission, boundaries and limits to action, and options.
- Tactics: The specific actions taken to achieve the objective.
- Strategic plan: The aggregate of the organization’s vision, mission, goals, objectives and strategies.
- Vision: A roadmap to a projected future, what the organization wants to become.
- Mission: What the organization does, its purpose, and its core competencies.
With this as a starting point, I’d like to relate the key points of a remarkable presentation that contributed greatly to the clarification and simplification of our conception of the strategic planning process. Read More
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There’s more than one way to make a poor decision. Bad data, inappropriate assumptions and flawed logic are just three of the missteps you can take on your climb up the Ladder of Inference, a concept first developed by Chris Argyris, professor of business at Harvard, in 1974, and later popularized by Peter Senge in his 1990 book, “The Fifth Discipline”. If we’re not mindful of these mental pitfalls, we’re likely to use our automated business processes to simply make bad decisions faster.
The Ladder of Inference, an oldie-but-goody, is likely familiar to you, although you may not have run across it in some time. A quick summary of the ladder’s seven rungs would be (starting at the bottom):
- Observation: The world of observable data and experience
- Filtering: The selection of a subset of this data for further processing
- Meaning: Assigning meaning / interpretation to the data, through semantics or culture
- Assumptions: Associated context, often from your Framework (below), of what you already know and the new meanings you’ve assigned
- Conclusions: Drawn based on the assumptions and meaning applied to the filtered data
- Framework: You alter, adjust or adapt your belief system / knowledge framework based on your conclusions
- Action: You take action based on the meaning of the data and your updated belief system
A simple example of the ladder in action might be: Read More
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What is information? The lack of a working definition plagued both science and the emerging telecommunications industry until the arrival of Claude Shannon and his famous 1948 paper, “A Mathematical Theory of Communication”, based on his cryptography work during WWII while at Bell Labs. The landmark article is considered the founding work of the field of information theory, and would augment Shannon’s earlier groundbreaking research at MIT into the design of digital circuits and digital computers.
Shannon interpreted his formal definition, H = -∑ pi log (pi), in a number of counterintuitive ways:
- As a measure of entropy (the formula exactly mirrors Boltzmann’s definition of thermodynamic entropy)
- As the resolution of uncertainty
- As a measure of surprise
While that first definition has captured the attention of the likes of physicist Stephen Hawking and has implications for cosmology, black holes and a holographic universe, it’s the latter two that are of interest to us for the moment. Read More
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Having a mentor is the number one factor in increasing the steepness of your personal learning curve. So says my oldest, Garik, a Park Scholar at North Carolina State University (class of 2012), during a discussion he recently had with the incoming Park Scholar class of 2019.
To accept the value of mentoring first requires one to understand the centrality and importance of the learning curve. Garik asked the students to imagine plotting the characteristics of two people on a simple X-Y axis. Person A comes to the game with only a moderate amount of resources at their disposal, but importantly, also a relatively steep learning curve, such that a plot of their capabilities has them crossing the Y-axis at an intercept of 1 and with a slope of one-half. Person B, in contrast, has much greater resources at their current disposal: time, talent, smarts, money, education, experience, etc …, but for whatever reason, has a shallower learning curve, such that their plot on the graph intercepts higher up the Y-axis at 2 but with a shallower slope of only one-quarter.
Unless you think you’re going to die before the two lines cross, you’d of course be better off as Person A. Based on his domestic and international experiences as an undergrad and grad student, as a researcher and an employee, and as part of two start-ups (so far), Garik’s conclusion is that, while there are several factors impacting the steepness of that learning curve, none is more important than that of having chosen good mentors.
Businesses can be said to have learning curves as well, and my discussion with my son got me to thinking about what factors would have the greatest bearing on organizational learning curve steepness. Read More
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Viewing data as an asset implies there are benefits to taking a supply chain approach to data management. It’s not just inventory that needs to be at the right place at the right time in the right format and quantity. An end-to-end information supply chain approach, from sourcing/acquisition through transformation and storage, to end users, analytics and insights, allows you to keep your focus on the business problems to be solved, and avoid having that ‘big honking data cube’ become a bottleneck instead of an enabler.
Here are a few best practice supply chain mindsets to consider applying to data management:
- Inventory turns / Data turns: How fast can you acquire and get the needed data into the hands of the decision makers? The whole point of analytics, BI and corporate performance management is to make better decisions faster, and how you structure your information supply chain will have a big impact on that “faster” part. This could mean anything from self-service BI to data visualization to better and faster data prep and data quality procedures. Critical decision support data delivered too long after the problem surfaces is like not having the inventory you need in the stores until the holiday shopping season is half over.
- Analytics / Decisions at the edge: Taking “faster” to the extreme can sometimes mean taking action on the data BEFORE you store it, acting on streaming data via event stream processing, which has applications in financial services (e.g. credit scoring), cybersecurity and fraud (e.g. detecting unusual network connection patterns), quality (e.g. process and product quality control), and asset maintenance (e.g. sensor data from critical equipment). Whether it’s humans or machines making those decisions at the edge, real-time and near-real-time decision support capabilities are becoming competitive differentiators across a number of industries.
- Visibility / Control Towers: Reacting quickly to fluctuating demand requires visibility into your entire physical supply chain, from what’s in which store or warehouse, which production lines are down for maintenance, and which suppliers have additional capacity at the ready. In the same way, better business decisions means having immediate access to ALL the relevant data – a worthy integration challenge for both inventory and data, with a commensurately worthy business outcome.
- ABC inventory / data classification: Not all inventory is equally important, a maxim understood by every supply chain professional. “A” inventory is high in value but not necessarily volume, warranting tighter controls and monitoring than low-value, high volume “C” material. Your data can likely be similarly categorized, with revenue, cost, employee and customer data no doubt in the “A” category, with perhaps production, quality and transaction details falling lower in priority. Such a classification approach, based on what data is used most often in the most critical decision support processes can help your prioritize your data quality and myriad other IT activities and investments.
- Data Management for Analytics: Designing a physical warehouse that maximizes the efficiency of receiving and storage is not necessarily the best overall approach to supply chain management, where access to raw materials and WIP at the right place and time on the factory floor is more critical to meeting cost, revenue, customer satisfaction and business goals than simply minimizing storage and handling costs. Likewise, a data warehouse built to minimize data storage cost may make it difficult for business and decision support users to access what they need, in the format they need for rapid analysis and insight. This gets back to point number one above about data turns: it’s not about how fast and cheaply you can get data into the warehouse, it’s about how quickly you can turn that data into valuable insights across the entire information supply chain.