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Data Is a New Currency | @CloudExpo #BigData #BI #AI #ML #Analytics
Data is the new Oil

This guest blog is provided by Brandon Kaier (@bkaier). Brandon has more than two decades of experience in the IT industry as a transformational leader.  Kaier is responsible for setting the strategy, defining the service line offerings and capabilities as the Field CTO for the North America. These responsibilities include bringing net new products and solutions to market with a focus on the deployment of solutions enabling analytics and agile application development. Most recently Kaier held positions on Technical Architecture team and Strategy and Direction team for Dell EMC’s Data Lake solutions. Kaier also brings experienced based, Dell EMC IT best practices, Big Data and Transformational strategies to Dell EMC customers.

“Data is the new Oil.”

Has anyone not heard this phrase yet? This analogy was first presented by Clive Humby at the Association of National Advertisers (ANA), Senior Marketers Summit at Kellogg School in 2006.

If you have been following and or are active in the analytics and data science community, I am sure that you have, and honestly I would hope so. This was not the first time we heard the phrase “data is the new oil,” and it certainly will not be the last. Interestingly enough this exclamation is actually at least ten years old. For example, marketing commentator Michael Palmer blogged back in 2006: “Data is just like crude. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc., to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.”

I really like Michael’s extended comment here because I think that it is much needed clarity. The difference between a barrel of oil and highway traffic is one of energy. A barrel of oil had a ton of Potential Energy and burning that oil as fuel creating the movement of the cars is its conversion to Kinetic Energy. (Definition: Kinetic Energy is the energy of motion; the movement of objects. Objects that are not in motion possess Potential Energy, which is converted to kinetic energy when some force acts upon the object to set it in motion.) This is the essential challenge of analytics. Data sitting on your data center floor is generally valueless except for the time at which it was created. Let me be clear, this is not to say that the data isn’t important. It is, you need it for all sorts of things; GRC, management and compliance reporting, order tracking, and what not, but it is no longer creating value. Data is like oil in that it retains a ton of Potential Value. However, it requires analytics to create motion in the business to find something actionable or decisions to optimize in order to realize that value.

Oil Data
Oil has Potential Energy Data has Potential Value
Gas or refined Oil has more Potential Energy than Oil by volume Data Science or refined Data increases the Potential Value of Data
Burning Gas to create motion converts Potential Energy to Kinetic Energy Analyzing Data to create Analytic Insights converts Potential Value to Kinetic Value
Oil is raw and of little direct use Data is raw and of little direct use
Oil is refined into fuel; powers the economy and drove the 3rd Industrial Revolution Data is refined by analytics to power the business and shall fuel 4th Ind. Rev.

So the really interesting question to ask now is “What is the economic value of data?”

There is this idea that currency, once spent does not lose value, it retains it. It retains its potential energy or potential value, in other words…..

Think of a $10 bill.

You may take it and give it to Starbucks and while, yes, you are out that $10 the $10 bill is still worth well, $10.

Starbucks is going to use that $10 to buy coffee from a distributor.

Who in turn buys coffee from a grower, who might then use it on food or maybe even  a Starbucks.

When you insert something, a new demand, into a circular flow like this then you create an economic concept called the Multiplier Effect. This is a concept that countries use to consider how they invest money and how that investment, by having it distribute though a supply chain, like the example above, will impact the economy of their country.

Multiplier Effect Definition: “An effect in economics in which an increase in spending produces an increase in national income and consumption greater than the initial amount spent.”

Multiplier Effect is a well-established economical principal first postulated in 1939 and there are standard formulas to apply this effect based on different kind of investments.

To overly simplify the process it basically equates to 4:1 (Note: the actual multiplier is dependent upon the marginal propensity to consumer (MPC). See here for more details. For every dollar put into the supply chain you are creating $4 of economic value. Whoever they invest in or award a contract to has to buy supplies from someone else and they buy other materials from a third company and so on down the line.

So with that in mind, how does Multiplier Effect impact Data in a modern organization? Bill Schmarzo is noted as saying that, “Data exhibits a Network Effect, where data can be used at the same time across multiple use cases thereby increasing its value to the organization.” I would contend that this network effect is in fact the same thing principally as the Multiplier Effect.

We have seen this Network Effect – the Multiplier Effect – play out within our customer base. Let’s assume that I have three use cases, A, B, and C. Let’s also assume that independently those three use cases have a value of 5, 3, and 2 (high to low) respectively. Now we have one of the data scientists apply the results of use case B to use case C and we find that the value of C goes from 2 to 4. This is what I mean by the Network Effect is similar if not identical to the Multiplier Effect; investment on one side of the value chain has cascading impacts to other parts of the value chain, just like my $10 bill.

I have found some companies who have recognized this inherent value in data sharing and are starting to form ecosystems where the only cost to get into the ecosystem is the contribution of your data. Take a home hospice care company. Their employees have very stressful jobs with a lot of time in their car going form patient to patient. Would it be valuable to work with other companies to share information that would make the lives of their nurses and home care employees better? We are seeing one such case starting to come together in the UK.

If seemingly disparate companies can find value to their business through the sharing of data what is to stop one company from asking to exchange hard tangible goods for data.

I think that companies are going to have to make a decision and I think that it is going to be one of three choices. Fundamentally, they can continue to treat data as a cost to be minimized and as a consequence will relinquish the value in the data to more aggressive partners and competitors at the risk of market share. They will buy into the notion that data is the new oil and as a result treat data like a tangible asset with all of the associated processes; supply chain management, asset capitalization or depreciation one would normal use with a physical asset. This approach will yield results and positive outcomes to the business but will fail to take real advantage of the asset.

The final option, consider data to be in fact a new currency, Leverage the Multiplier Effect internally, then expand it to include value chain partners, and then one day soon, I think, exchange or barter data and the associated analytics for goods and services. Conceptually, this isn’t a new idea. Back in the 80’s, when Point of Sale (POS) scanners were first making their way into retail stores, in many cases those scanners were being given to the retailers, “for free”, in exchange for all the data from those POS devices. If you were to consider what GE knows about jet engines how much longer will it be before GE uses or gives away its data to the supplier of fuel valves for the engine in exchange for those valves?

The post Data is a New Currency appeared first on InFocus Blog | Dell EMC Services.

Read the original blog entry...

About 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.

Presentation Slides
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CloudEXPO Stories
The challenges of aggregating data from consumer-oriented devices, such as wearable technologies and smart thermostats, are fairly well-understood. However, there are a new set of challenges for IoT devices that generate megabytes or gigabytes of data per second. Certainly, the infrastructure will have to change, as those volumes of data will likely overwhelm the available bandwidth for aggregating the data into a central repository. Ochandarena discusses a whole new way to think about your next-gen applications and how to address the challenges of building applications that harness all data types and sources.
Andi Mann, Chief Technology Advocate at Splunk, is an accomplished digital business executive with extensive global expertise as a strategist, technologist, innovator, marketer, and communicator. For over 30 years across five continents, he has built success with Fortune 500 corporations, vendors, governments, and as a leading research analyst and consultant.
Extreme Computing is the ability to leverage highly performant infrastructure and software to accelerate Big Data, machine learning, HPC, and Enterprise applications. High IOPS Storage, low-latency networks, in-memory databases, GPUs and other parallel accelerators are being used to achieve faster results and help businesses make better decisions. In his session at 18th Cloud Expo, Michael O'Neill, Strategic Business Development at NVIDIA, focused on some of the unique ways extreme computing is being used on IBM Cloud, Amazon, and Microsoft Azure and how to gain access to these resources in the cloud... for FREE!
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.
"We do one of the best file systems in the world. We learned how to deal with Big Data many years ago and we implemented this knowledge into our software," explained Jakub Ratajczak, Business Development Manager at MooseFS, in this SYS-CON.tv interview at 20th Cloud Expo, held June 6-8, 2017, at the Javits Center in New York City, NY.

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The challenges of aggregating data from consumer-oriented devices, such as wearable technologies and smart thermostats, are fairly well-understood. However, there are a new set of challenges for IoT devices that generate megabytes or gigabytes of data per second. Certainly, the infrastructure will have to change, as those volumes of data will likely overwhelm the available bandwidth for aggregating the data into a central repository. Ochandarena discusses a whole new way to think about your next-gen applications and how to address the challenges of building applications that harness all data t...