Category Proxy Metrics

Why Cloud Computing and Data Analytics Enable Digital Transformation

From the inception of the Industrial Revolution several core ingredients enabled the transformation and growth of industry.  Among these core building blocks of the Industrial Revolution namely: access to risk capital, visionary entrepreneurs, available labor, technology, resources and energy.  Technology and energy play a crucial role in not only growing industry but enable scale.   Technology can open new markets and provide advantage through product differentiation and economies of scale.  Energy is literally the fuel that scales operations.

Today technology, built from knowledge and data, is how companies compete. Energy now emerges as even more integral in scaling operations. Just as James Watt developed the first steam powered engine in 1606 commencing the Industrial Revolution, it was the access to available coal with the use of the steam powered pump, invented by Thomas Savery in 1698, that allowed greater access to coal that gave scale to industry.

Most recently, the pending transaction of Salesforce’s (CRM) acquisition of Slack (WORK) after acquiring Tableau last year serves as a reference in valuing the importance of technology is to sustaining market value.  The market value of seven companies accounts for 27% of the approximately $31.6 trillion for the S&P 500.  Evaluating the industry and market impact of innovative technologies can be viewed through the lens of stock valuations, particularly as it applies to mergers and acquisitions.  This article reviews the companies and the technologies from the perspective of market sales opportunity and the economic impact of the technologies based on the price/performance disruption to the industry.

So why are we focusing on energy and data today?  Energy, predominantly hydrocarbon fuels such as oil, natural gas and even coal is how people heat their homes and buildings, facilitate transportation, and generate electricity to run lights, computers, machines and equipment. In addition, there is substantial investment focus on the digital economy, Environmental and Social Governance (ESG), and innovative technologies. A common thread among these themes is energy and data.

Data and Energy are the pillars of the digital economy. Energy efficiency can reduce carbon emissions, thereby improve ESG sustainability initiatives. Innovative technologies around energy and data are opening new markets and processes from formulating new business models to structuring and operating businesses.

The climate imperative and investing in energy infrastructure and environmental ESGs are predicted on energy efficiency and relevant performance metrics to evaluate investment allocation decisions. Therefore, our initial emphasis begins with a background on energy consumption with focus on electric consumption trends, carbon footprint, Green House Gas (GHG) emissions, sustainability, electric grid resilience, and technologies that impact energy including Electric Vehicles (EV), energy storage, and Autonomous Driving (AD).  Data technologies encompass cloud architecture, Software as a Service (SaaS), Machine Learning (ML) analytics, and the importance of data as the digital transformation gives rise to the digital economy. 

Digital Economy Performance Metrics

Before we dive into the financial and competitive analysis, let’s review business models that are disruptive to the status quo. That is are innovative technologies capable of rapid scale and efficiency gains that change the economics of the market and business profitability.  In addition, disruptive events, driven primarily by technology, often appear as waves as the adoption of innovative technologies expands through the market.

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Analytics platforms transform technology adoption

Engendering a data analytics framework culture to optimize process innovation will lead to improving productivity. The adoption of new technologies is often challenging with lagging productivity gains. Investments into business processes contribute to faster adoption of new technologies and higher market valuations. For example, workflow processes provide a framework to better leverage new technologies by shortening the time to productivity gains. In addition, investments into business processes, intangible assets, contribute to higher equity valuations and are often reflected in growing levels of goodwill generated with technology company acquisitions.

As a core process we suggest a data analytics framework using feedback loops to optimize outcomes and deliver a better approach to leveraging technology adoption. This approach ensures that technology adoption strategies and implementations are based on data and driven by process optimization. In addition, employing an analytics roadmap to manage disruptive technology adoption with defined feedback loops set to optimize successful outcomes further improves value.

Technology and the Productivity J-Curve Paradox

As mega trends unfold; such as cloud architecture, 5G cellular, big data, IoT sensors along with machine learning, a successful structural framework for embracing these new technologies needs to embrace and address the disruption while engaging with processes that optimize desired outcomes. 

In 1987, Robert Solow, a Nobel Laureate and MIT professor, quibbled about the preponderance of computers and lack of productivity. So this is not a new issue. The economics of business process and the Productivity J-Curve concept was framed by Erik Brynjolfsson, Daniel Rock, and Chad Syverson – who examined the often slow and bumpy productivity gains arising from the adoption of new technologies. Their collective studies from the National Bureau of Economic Analysis offer a compelling rationale for developing business processes that enhance the adoption of innovative technologies. In essence, because training, experience curves, changes to business operations and services lag productivity gains. Their findings suggest “the more transformative the new technology, the more likely its productivity effects will initially be underestimated.” A recent article in The Economist, Reasons to be Cheerful, highlighted how education and training that speed the adoption of new innovation could raise productivity. 

Figure 1 Productivity J-Curve

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A data analytics framework is applicable to insight discovery; provides a roadmap towards innovation; and enables capabilities that can optimize approaches to new business models and opportunities. The following paper provides examples revealing how and why to apply visual analytics for discovery, innovation and evaluating new opportunities. 

Discover how waveforms and patterns are applied to science and finance, and how customer usage patterns can reveal new approaches to market micro-segmentation and persona classifications.  Lastly we’ll reveal how the deployment of IoT devices across the enterprise fuels data flow in the physical world regarding the performance and conditions of business assets.

Introduction

Our theme is applying visual data analytics as a tool for discovery, innovation and evaluating market opportunities. We show how two metrics, price and volume, are able to convey insight and establish price targets for technical analysis. Why energy consumption patterns and waveforms lend themselves to understanding science and classifying human behavior.  How proxy metrics can serve as measures for physical events. Why linking granular visibility into processes and the monitoring of conditions and operating performance help build an advantage in the digital economy.  

Green Econometrics relies on visual analytics as a core fabric in our data analytics frameworks because visual analytics are integral to discovery, innovation and new opportunity development. Visual insights are easy to understand – allowing business objective and performance metrics to seamlessly transfer across business units. So how do we do it?

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