Welcome!

Related Topics: @ThingsExpo, @CloudExpo, @DXWorldExpo

@ThingsExpo: Blog Feed Post

Understanding Chaos Theory | @ThingsExpo #DX #BI #IoT #M2M #Analytics

Chaos Theory is the branch of mathematics that deals with complex systems whose behavior is highly sensitive to slight changes

Why Understanding Chaos Theory Is Important to Your Business

“Chaos: When the present determines the future, but the approximate present does not approximately determine the future.”

– Edward Lorenz

We all probably remember the movie (“Jurassic Park”), even if we don’t remember this exact scene: Dr. Malcolm, played by Jeff Goldblum is explaining Chaos Theory to Dr. Ellie Sattler, played by Laura Dern.

Dr. Malcolm is explaining how random, seemingly negligible events can disrupt even the most carefully laid out plans.

Dr. Ian Malcolm: [after the T-Rex failed to appear for the tour group]. “You see a Tyrannosaur doesn’t follow a set pattern or park schedules, the essence of chaos.”

And then later in the movie…

Dr. Ian Malcolm: “Oh, yeah. Oooh, ahhh, that’s how it always starts. Then later there’s running and um, screaming.”

Yes, running and screaming. That’s what happens when even the most carefully developed plans eventually succumb to the compounding of all these “random, seemingly negligible” events. And understanding the ramifications of Chaos Theory is becoming even more relevant as we look to machines to take over increasingly complex tasks.

What Is Chaos Theory?
Chaos Theory
is the branch of mathematics that deals with complex systems whose behavior is highly sensitive to slight changes in conditions, so that small alterations can give rise to unintended consequences. Chaos Theory is the science of surprises, and not always pleasant surprises. While traditional science deals with predictable phenomena like gravity, electricity, or chemical reactions, Chaos Theory deals with nonlinear things that are mostly impossible to predict, calculate, or control, like turbulence, a bar brawl, the stock market futures, debris flying out of the bed of a truck, or a child darting onto the street[1].

There are several underlying principles of Chaos Theory, including:

  • Butterfly Effect: This is probably the most common principle; that a butterfly flapping its wings in one part of the world can eventually cause a hurricane in another part of the world. Here is a more realistic way to describe the Butterfly Effect: small changes in the initial conditions can lead to drastic changes in the results. Our lives are an ongoing demonstration of this principle.
  • Unpredictability: Because we can never know all the initial conditions of a complex system in sufficient detail, we cannot hope to predict the ultimate fate of a complex system. Even slight errors in measuring the initial state of a system could be amplified dramatically, rendering any prediction useless or even wrong.
  • Mixing: Turbulence describes how two adjacent points in a complex system could eventually end up in very different positions after time has elapsed. Examples: Two neighboring water molecules may end up in different parts of the ocean or even in different oceans. Or a group of helium balloons that launch together eventually landing in drastically different locations.
  • Feedback: Systems often become chaotic when there is feedback. A good example is the behavior of the stock market. As the value of a stock rises or falls, people are inclined to buy or sell that stock. This in turn further affects the price of the stock, causing chaotic, unpredictable stock price movements.

Chaos Theory Meets the Autonomous Car
Complex systems
(sometimes called complexity theory) are systems whose behavior is intrinsically difficult to model due to the dependencies, relationships, or interactions between their parts and its environment. Complex systems are impacted by factors such as non-linearity, emergence, spontaneous order, adaptation, and feedback loops. And while an autonomous car may not be the most complex system in the world, it is certainly more complex than playing checkers, chess or Grand Theft Auto.

So what happens when Complex Systems run into the world of Chaos Theory?

Complex or dynamical systems – that is, systems whose state evolves over time –display dynamics that are highly sensitive to initial conditions (butterfly effect). These sensitivities manifest themselves as an exponential growth of perturbations in the initial conditions over time. As a result, the behavior of these complex systems grows more and more random (unpredictable). This happens even though these systems are deterministic, meaning that their future dynamics are fully defined by their initial conditions[2].

Summary
So while we can’t avoid the impact of Chaos Theory, especially as we employ Machine Learning and Artificial Intelligence to a growing body of complex systems and environments, here’s what we can do to mitigate the impact of Chaos Theory:

  • Build a plan for the continuous testing and refinement of the analytics. The performance of analytic models decay over time; they have a half-life because the world changes such as public sentiments driven by widening wealth gaps, corporate acts of malfeasance, government scandals, rising burden of student debt, growing ranks of the under-employed, wage stagnation, mounting populace divide on social media, legalization of marijuana, rising media sensationalism, climate change, terrorism, Cubs win the World Series, etc.

Figure 1: Half-life Calculation

Even apparently stable analytic models constantly need to be evaluated and fine-tuned.  Think of it as the “Data Scientist Life-time Employment Act!”  See the blog “2016 Presidential Election: Did Big Data Just Get Lazy” for more details on the analytics half-life effect.

  • Create a dashboard to monitor the performance of your analytic models. A dashboard is the perfect monitoring device to ensure that business managers and the data science team are proactively monitoring the performance and state of the analytic models.  Heck, maybe even integrate some data science into the dashboard to predict when a model is starting to fail (i.e., predictive maintenance).  See the blog “Big Data Dashboards” for some ideas about how to transform your “monitoring” dashboard into a “predictive” dashboard.
  • Understand the costs and liabilities associated with Type I and Type II errors and use those costs and liabilities to prioritize your investments in data acquisition, data quality, metadata enhancements and model tuning. See the blog “Understanding Type I and Type II Errors” for an approach for quantifying the costs and liabilities associated with Type I and Type II errors.

To borrow again from the movie “Jurassic Park”, don’t get caught with your pants down with respect to the potential ramifications of Chaos Theory on your complex analytic projects.

  1. http://fractalfoundation.org/resources/what-is-chaos-theory/
  2. https://en.wikipedia.org/wiki/Dynamical_systems_theory#Chaos_theory

The post Why Understanding Chaos Theory Is Important To Your Business appeared first on InFocus Blog | Dell EMC Services.

Read the original blog entry...

More Stories By William Schmarzo

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting strategy and defining the Big Data service offerings for Hitachi Vantara as CTO, IoT and Analytics.

Previously, as a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata.

Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications.

Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

Latest Stories
With more than 30 Kubernetes solutions in the marketplace, it's tempting to think Kubernetes and the vendor ecosystem has solved the problem of operationalizing containers at scale or of automatically managing the elasticity of the underlying infrastructure that these solutions need to be truly scalable. Far from it. There are at least six major pain points that companies experience when they try to deploy and run Kubernetes in their complex environments. In this presentation, the speaker will d...
While DevOps most critically and famously fosters collaboration, communication, and integration through cultural change, culture is more of an output than an input. In order to actively drive cultural evolution, organizations must make substantial organizational and process changes, and adopt new technologies, to encourage a DevOps culture. Moderated by Andi Mann, panelists discussed how to balance these three pillars of DevOps, where to focus attention (and resources), where organizations might...
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-c...
When building large, cloud-based applications that operate at a high scale, it's important to maintain a high availability and resilience to failures. In order to do that, you must be tolerant of failures, even in light of failures in other areas of your application. "Fly two mistakes high" is an old adage in the radio control airplane hobby. It means, fly high enough so that if you make a mistake, you can continue flying with room to still make mistakes. In his session at 18th Cloud Expo, Le...
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by ...
As Cybric's Chief Technology Officer, Mike D. Kail is responsible for the strategic vision and technical direction of the platform. Prior to founding Cybric, Mike was Yahoo's CIO and SVP of Infrastructure, where he led the IT and Data Center functions for the company. He has more than 24 years of IT Operations experience with a focus on highly-scalable architectures.
CI/CD is conceptually straightforward, yet often technically intricate to implement since it requires time and opportunities to develop intimate understanding on not only DevOps processes and operations, but likely product integrations with multiple platforms. This session intends to bridge the gap by offering an intense learning experience while witnessing the processes and operations to build from zero to a simple, yet functional CI/CD pipeline integrated with Jenkins, Github, Docker and Azure...
The explosion of new web/cloud/IoT-based applications and the data they generate are transforming our world right before our eyes. In this rush to adopt these new technologies, organizations are often ignoring fundamental questions concerning who owns the data and failing to ask for permission to conduct invasive surveillance of their customers. Organizations that are not transparent about how their systems gather data telemetry without offering shared data ownership risk product rejection, regu...
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...
Dhiraj Sehgal works in Delphix's product and solution organization. His focus has been DevOps, DataOps, private cloud and datacenters customers, technologies and products. He has wealth of experience in cloud focused and virtualized technologies ranging from compute, networking to storage. He has spoken at Cloud Expo for last 3 years now in New York and Santa Clara.
Enterprises are striving to become digital businesses for differentiated innovation and customer-centricity. Traditionally, they focused on digitizing processes and paper workflow. To be a disruptor and compete against new players, they need to gain insight into business data and innovate at scale. Cloud and cognitive technologies can help them leverage hidden data in SAP/ERP systems to fuel their businesses to accelerate digital transformation success.
Containers and Kubernetes allow for code portability across on-premise VMs, bare metal, or multiple cloud provider environments. Yet, despite this portability promise, developers may include configuration and application definitions that constrain or even eliminate application portability. In this session we'll describe best practices for "configuration as code" in a Kubernetes environment. We will demonstrate how a properly constructed containerized app can be deployed to both Amazon and Azure ...
Poor data quality and analytics drive down business value. In fact, Gartner estimated that the average financial impact of poor data quality on organizations is $9.7 million per year. But bad data is much more than a cost center. By eroding trust in information, analytics and the business decisions based on these, it is a serious impediment to digital transformation.
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
Predicting the future has never been more challenging - not because of the lack of data but because of the flood of ungoverned and risk laden information. Microsoft states that 2.5 exabytes of data are created every day. Expectations and reliance on data are being pushed to the limits, as demands around hybrid options continue to grow.