Welcome!

Adobe Flex Authors: Matthew Lobas, PR.com Newswire, Shelly Palmer, Kevin Benedict

Related Topics: @CloudExpo, Artificial Intelligence, @DXWorldExpo, @ThingsExpo

@CloudExpo: Blog Feed Post

“Unlearn” to Unleash Your #DataLake | @CloudExpo @Schmarzo #BigData #AI #DX

The Data Science Process is about exploring, experimenting, and testing new data sources and analytic tools quickly

It takes years – sometimes a lifetime – to perfect certain skills in life: hitting a jump shot off the dribble, nailing that double high C on the trumpet, parallel parking a Ford Expedition. Malcolm Gladwell wrote a book, “Outliers,” discussing the amount of work – 10,000 hours – required to perfect a skill (while the exactness of 10,000 hours has come under debate, it is still a useful point that people need to invest considerable time and effort to master a skill). But once we get comfortable with something that we feel that we have mastered, we become reluctant to change. We are reluctant to unlearn what we’ve taken so long to master.

Changing your point of release on a jump shot or your embouchure for playing lead trumpet is dang hard! Why? Because it is harder to unlearn that it is to learn. It is harder to un-wire all those synoptic nerve endings and deep memories than it was to wire them in the first place. It’s not just a case of thinking faster, smaller or cheaper; it necessitates thinking differently.

For example, why did it take professional basketball so long to understand the game changing potential of the 3-point shot? The 3-point shot was added to the NBA during the 1979-1980 season, but for decades the 3-point shot was more a novelty then a serious game strategy. Pat Riley, the legendary coach of the 3-pointer’s first decade in the league (won NBA Championships in 1982, 1985, 1987 and 1988), called it a “gimmick.” Larry Bird, one of that era’s top players said: “I really don’t like it.”

It’s only been within the past 3 years where the “economics of the 3-point shot” have changed the fundamentals of how to win an NBA Championship (see Figure 1).

Figure 1: NBA 3-point Baskets per Season

NBA Coaches and General Managers just didn’t comprehend the “economics of the 3-point shot” and how the 3-point shot could turn a good shooter into a dominant player; that a 40% 3-point shooting percentage is equivalent to a 60% 2-point shooting percentage from a points / productivity perspective. The economics of the 3-point shot (coupled with rapid ball movement to create uncontested 3-point shots) wasn’t full exploited until the 2015-2016 season by the Golden State Warriors. Their success over the past 3 seasons (3 trips to the NBA finals with 2 championships) shows how much the game of basketball has been changed.

Sometimes it’s necessary to unlearn long held beliefs (i.e. 2-point shooting in a predominately isolation offense game) in order to learn new, more powerful, game changing beliefs (i.e., 3-point shooting in a rapid ball movement offense).

Sticking with our NBA example, Phil Jackson is considered one of the greatest NBA coaches, with 11 NBA World Championships coaching the Chicago Bulls and the Los Angeles Lakers. Phil Jackson mastered the “Triangle Offense” that played to the strengths of the then dominant players Michael Jordan (Chicago Bulls) and Kobe Bryant (Los Angeles Lakers) to win those 11 titles.

However, the game passed Phil Jackson as the economics of the 3-point shot changed how to win. Jackson’s tried-and-true “Triangle Offense” failed with the New York Knicks leading to the team’s dramatic under-performance and ultimately his firing. It serves as a stark reminder of how important it is to be ready to unlearn old skills in order to move forward.

And what holds true for sports, holds even more so for technology and business.

The Challenge of Unlearning
For the first two decades of my career, I worked to perfect the art of data warehousing. I was fortunate to be at Metaphor Computers in the 1980’s where we refined the art of dimensional modeling and star schemas. I had many years working to perfect my star schema and dimensional modeling skills with data warehouse luminaries like Ralph Kimball, Margy Ross, Warren Thornthwaite, and Bob Becker. It became engrained in every customer conversation; I’d built a star schema and the conformed dimensions in my head as the client explained their data analysis requirements.

Then Yahoo happened to me and soon everything that I held as absolute truth was turned upside down. I was thrown into a brave new world of analytics based upon petabytes of semi-structured and unstructured data, hundreds of millions of customers with 70 to 80 dimensions and hundreds of metrics, and the need to make campaign decisions in fractions of a second. There was no way that my batch “slice and dice” business intelligence and highly structured data warehouse approach was going to work in this brave new world of real-time, predictive and prescriptive analytics.

I struggled to unlearn engrained data warehousing concepts in order to embrace this new real-time, predictive and prescriptive world. And this is one of the biggest challenge facing IT leaders today – how to unlearn what they’ve held as gospel and embrace what is new and different. And nowhere do I see that challenge more evident then when I’m discussing Data Science and the Data Lake.

Embracing The “Art of Failure” and The Data Science Process
Nowadays, Chief Information Officers (CIOs) are being asked to lead the digital transformation from a batch world that uses data and analytics to monitor the business to a real-time world that exploits internal and external, structured and unstructured data, to predict what is likely to happen and prescribe recommendations. To power this transition, CIO’s must embrace a new approach for deriving customer, product, and operational insights – the Data Science Process (see Figure 2).

Figure 2:  Data Science Engagement Process

The Data Science Process is about exploring, experimenting, and testing new data sources and analytic tools quickly, failing fast but learning faster. The Data Science process requires business leaders to get comfortable with “good enough” and failing enough times before one becomes comfortable with the analytic results. Predictions are not a perfect world with 100% accuracy. As Yogi Berra famously stated:

“It’s tough to make predictions, especially about the future.”

This highly iterative, fail-fast-but-learn-faster process is the heart of digital transformation – to uncover new customer, product, and operational insights that can optimize key business and operational processes, mitigate regulatory and compliance risks, uncover new revenue streams and create a more compelling, more prescriptive customer engagement. And the platform that is enabling digital transformation is the Data Lake.

The Power of the Data Lake
The data lake exploits the economics of big data; coupling commodity, low-cost servers and storage with open source tools and technologies, is 50x to 100x cheaper to store, manage and analyze data then using traditional, proprietary data warehousing technologies. However, it’s not just cost that makes the data lake a more compelling platform than the data warehouse. The data lake also provides a new way to power the business, based upon new data and analytics capabilities, agility, speed, and flexibility (see Table 1).

Data Warehouse Data Lake
Data structured in heavily-engineered structured dimensional schemas Data structured as-is (structured, semi-structured, and unstructured formats)
Heavily-engineered, pre-processed data ingestion Rapid as-is data ingestion
Generates retrospective reports from historical, operational data sources Generates predictions and prescriptions from a wide variety of internal and external data sources
100% accurate results of past events and performance “Good enough” predictions of future events and performance
Schema-on-load to support the historical reporting on what the business did Schema-on-query to support the rapid data exploration and hypothesis testing
Extremely difficult to ingest and explore new data sources (measured in weeks or months) Easy and fast to ingest and explore new data sources (measured in hours or days)
Monolithic design and implementation (water fall) Natively parallel scale out design and implementation (scrum)
Expensive and proprietary Cheap and open source
Widespread data proliferation (data warehouses and data marts) Single managed source of organizational data
Rigid; hard to change Agile; relatively ease to change

Table 1:  Data Warehouse versus Data Lake

The data lake supports the unique requirements of the data science team to:

  • Rapidly explore and vet new structured and unstructured data sources
  • Experiment with new analytics algorithms and techniques
  • Quantify cause and effect
  • Measure goodness of fit

The data science team needs to be able perform this cycle in hours or days, not weeks or months. The data warehouse cannot support these data science requirements. The data warehouse cannot rapidly exploration the internal and external structured and unstructured data sources. The data warehouse cannot leverage the growing field of deep learning/machine learning/artificial intelligence tools to quantify cause-and-effect. Thinking that the data lake is “cold storage for our data warehouse” – as one data warehouse expert told me – misses the bigger opportunity. That’s yesterday’s “triangle offense” thinking. The world has changed, and just like how the game of basketball is being changed by the “economics of the 3-point shot,” business models are being changed by the “economics of big data.”

But a data lake is more than just a technology stack. To truly exploit the economic potential of the organization’s data, the data lake must come with data management services covering data accuracy, quality, security, completeness and governance. See “Data Lake Plumbers: Operationalizing the Data Lake” for more details (see Figure 3).

Figure 3:  Components of a Data Lake

If the data lake is only going to be used another data repository, then go ahead and toss your data into your unmanageable gaggle of data warehouses and data marts.

BUT if you are looking to exploit the unique characteristics of data and analytics –assets that never deplete, never wear out and can be used across an infinite number of use cases at zero marginal cost – then the data lake is your “collaborative value creation” platform. The data lake becomes that platform that supports the capture, refinement, protection and re-use of your data and analytic assets across the organization.

But one must be ready to unlearn what they held as the gospel truth with respect to data and analytics; to be ready to throw away what they have mastered to embrace new concepts, technologies, and approaches. It’s challenging, but the economics of big data are too compelling to ignore. In the end, the transition will be enlightening and rewarding. I know, because I have made that journey.

The post “Unlearn” to Unleash Your Data Lake 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”, is responsible for setting the strategy and defining the Big Data service line offerings and capabilities for the EMC Global Services organization. As part of Bill’s CTO charter, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He’s written several white papers, avid blogger and is a frequent speaker on the use of Big Data and advanced analytics to power organization’s key business initiatives. He also teaches the “Big Data MBA” at the University of San Francisco School of Management.

Bill has nearly three decades of experience in data warehousing, BI and analytics. Bill authored EMC’s Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements, and co-authored with Ralph Kimball a series of articles on analytic applications. Bill has served on The Data Warehouse Institute’s faculty as the head of the analytic applications curriculum.

Previously, Bill was the Vice President of Advertiser Analytics at Yahoo and the Vice President of Analytic Applications at Business Objects.

@ThingsExpo Stories
"There's plenty of bandwidth out there but it's never in the right place. So what Cedexis does is uses data to work out the best pathways to get data from the origin to the person who wants to get it," explained Simon Jones, Evangelist and Head of Marketing at Cedexis, in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
"Cloud Academy is an enterprise training platform for the cloud, specifically public clouds. We offer guided learning experiences on AWS, Azure, Google Cloud and all the surrounding methodologies and technologies that you need to know and your teams need to know in order to leverage the full benefits of the cloud," explained Alex Brower, VP of Marketing at Cloud Academy, in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clar...
Large industrial manufacturing organizations are adopting the agile principles of cloud software companies. The industrial manufacturing development process has not scaled over time. Now that design CAD teams are geographically distributed, centralizing their work is key. With large multi-gigabyte projects, outdated tools have stifled industrial team agility, time-to-market milestones, and impacted P&L stakeholders.
Gemini is Yahoo’s native and search advertising platform. To ensure the quality of a complex distributed system that spans multiple products and components and across various desktop websites and mobile app and web experiences – both Yahoo owned and operated and third-party syndication (supply), with complex interaction with more than a billion users and numerous advertisers globally (demand) – it becomes imperative to automate a set of end-to-end tests 24x7 to detect bugs and regression. In th...
"Akvelon is a software development company and we also provide consultancy services to folks who are looking to scale or accelerate their engineering roadmaps," explained Jeremiah Mothersell, Marketing Manager at Akvelon, in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
"MobiDev is a software development company and we do complex, custom software development for everybody from entrepreneurs to large enterprises," explained Alan Winters, U.S. Head of Business Development at MobiDev, in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
SYS-CON Events announced today that CrowdReviews.com has been named “Media Sponsor” of SYS-CON's 22nd International Cloud Expo, which will take place on June 5–7, 2018, at the Javits Center in New York City, NY. CrowdReviews.com is a transparent online platform for determining which products and services are the best based on the opinion of the crowd. The crowd consists of Internet users that have experienced products and services first-hand and have an interest in letting other potential buye...
"IBM is really all in on blockchain. We take a look at sort of the history of blockchain ledger technologies. It started out with bitcoin, Ethereum, and IBM evaluated these particular blockchain technologies and found they were anonymous and permissionless and that many companies were looking for permissioned blockchain," stated René Bostic, Technical VP of the IBM Cloud Unit in North America, in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Conventi...
SYS-CON Events announced today that Telecom Reseller has been named “Media Sponsor” of SYS-CON's 22nd International Cloud Expo, which will take place on June 5-7, 2018, at the Javits Center in New York, NY. Telecom Reseller reports on Unified Communications, UCaaS, BPaaS for enterprise and SMBs. They report extensively on both customer premises based solutions such as IP-PBX as well as cloud based and hosted platforms.
"Space Monkey by Vivent Smart Home is a product that is a distributed cloud-based edge storage network. Vivent Smart Home, our parent company, is a smart home provider that places a lot of hard drives across homes in North America," explained JT Olds, Director of Engineering, and Brandon Crowfeather, Product Manager, at Vivint Smart Home, in this SYS-CON.tv interview at @ThingsExpo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
Coca-Cola’s Google powered digital signage system lays the groundwork for a more valuable connection between Coke and its customers. Digital signs pair software with high-resolution displays so that a message can be changed instantly based on what the operator wants to communicate or sell. In their Day 3 Keynote at 21st Cloud Expo, Greg Chambers, Global Group Director, Digital Innovation, Coca-Cola, and Vidya Nagarajan, a Senior Product Manager at Google, discussed how from store operations and ...
In his session at 21st Cloud Expo, Carl J. Levine, Senior Technical Evangelist for NS1, will objectively discuss how DNS is used to solve Digital Transformation challenges in large SaaS applications, CDNs, AdTech platforms, and other demanding use cases. Carl J. Levine is the Senior Technical Evangelist for NS1. A veteran of the Internet Infrastructure space, he has over a decade of experience with startups, networking protocols and Internet infrastructure, combined with the unique ability to it...
It is of utmost importance for the future success of WebRTC to ensure that interoperability is operational between web browsers and any WebRTC-compliant client. To be guaranteed as operational and effective, interoperability must be tested extensively by establishing WebRTC data and media connections between different web browsers running on different devices and operating systems. In his session at WebRTC Summit at @ThingsExpo, Dr. Alex Gouaillard, CEO and Founder of CoSMo Software, presented ...
WebRTC is great technology to build your own communication tools. It will be even more exciting experience it with advanced devices, such as a 360 Camera, 360 microphone, and a depth sensor camera. In his session at @ThingsExpo, Masashi Ganeko, a manager at INFOCOM Corporation, introduced two experimental projects from his team and what they learned from them. "Shotoku Tamago" uses the robot audition software HARK to track speakers in 360 video of a remote party. "Virtual Teleport" uses a multip...
A strange thing is happening along the way to the Internet of Things, namely far too many devices to work with and manage. It has become clear that we'll need much higher efficiency user experiences that can allow us to more easily and scalably work with the thousands of devices that will soon be in each of our lives. Enter the conversational interface revolution, combining bots we can literally talk with, gesture to, and even direct with our thoughts, with embedded artificial intelligence, whic...
SYS-CON Events announced today that Evatronix will exhibit at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Evatronix SA offers comprehensive solutions in the design and implementation of electronic systems, in CAD / CAM deployment, and also is a designer and manufacturer of advanced 3D scanners for professional applications.
Leading companies, from the Global Fortune 500 to the smallest companies, are adopting hybrid cloud as the path to business advantage. Hybrid cloud depends on cloud services and on-premises infrastructure working in unison. Successful implementations require new levels of data mobility, enabled by an automated and seamless flow across on-premises and cloud resources. In his general session at 21st Cloud Expo, Greg Tevis, an IBM Storage Software Technical Strategist and Customer Solution Architec...
To get the most out of their data, successful companies are not focusing on queries and data lakes, they are actively integrating analytics into their operations with a data-first application development approach. Real-time adjustments to improve revenues, reduce costs, or mitigate risk rely on applications that minimize latency on a variety of data sources. In his session at @BigDataExpo, Jack Norris, Senior Vice President, Data and Applications at MapR Technologies, reviewed best practices to ...
An increasing number of companies are creating products that combine data with analytical capabilities. Running interactive queries on Big Data requires complex architectures to store and query data effectively, typically involving data streams, an choosing efficient file format/database and multiple independent systems that are tied together through custom-engineered pipelines. In his session at @BigDataExpo at @ThingsExpo, Tomer Levi, a senior software engineer at Intel’s Advanced Analytics gr...
When talking IoT we often focus on the devices, the sensors, the hardware itself. The new smart appliances, the new smart or self-driving cars (which are amalgamations of many ‘things’). When we are looking at the world of IoT, we should take a step back, look at the big picture. What value are these devices providing? IoT is not about the devices, it’s about the data consumed and generated. The devices are tools, mechanisms, conduits. In his session at Internet of Things at Cloud Expo | DXWor...