Welcome!

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” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting strategy and defining the Big Data service offerings for Dell EMC’s Big Data Practice. 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
DX World EXPO, LLC, a Lighthouse Point, Florida-based startup trade show producer and the creator of "DXWorldEXPO® - Digital Transformation Conference & Expo" has announced its executive management team. The team is headed by Levent Selamoglu, who has been named CEO. "Now is the time for a truly global DX event, to bring together the leading minds from the technology world in a conversation about Digital Transformation," he said in making the announcement.
"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.
SYS-CON Events announced today that Conference Guru has been named “Media Sponsor” of the 22nd International Cloud Expo, which will take place on June 5-7, 2018, at the Javits Center in New York, NY. A valuable conference experience generates new contacts, sales leads, potential strategic partners and potential investors; helps gather competitive intelligence and even provides inspiration for new products and services. Conference Guru works with conference organizers to pass great deals to gre...
DevOps is under attack because developers don’t want to mess with infrastructure. They will happily own their code into production, but want to use platforms instead of raw automation. That’s changing the landscape that we understand as DevOps with both architecture concepts (CloudNative) and process redefinition (SRE). Rob Hirschfeld’s recent work in Kubernetes operations has led to the conclusion that containers and related platforms have changed the way we should be thinking about DevOps and...
The Internet of Things will challenge the status quo of how IT and development organizations operate. Or will it? Certainly the fog layer of IoT requires special insights about data ontology, security and transactional integrity. But the developmental challenges are the same: People, Process and Platform. In his session at @ThingsExpo, Craig Sproule, CEO of Metavine, demonstrated how to move beyond today's coding paradigm and shared the must-have mindsets for removing complexity from the develop...
In his Opening Keynote at 21st Cloud Expo, John Considine, General Manager of IBM Cloud Infrastructure, led attendees through the exciting evolution of the cloud. He looked at this major disruption from the perspective of technology, business models, and what this means for enterprises of all sizes. John Considine is General Manager of Cloud Infrastructure Services at IBM. In that role he is responsible for leading IBM’s public cloud infrastructure including strategy, development, and offering m...
The next XaaS is CICDaaS. Why? Because CICD saves developers a huge amount of time. CD is an especially great option for projects that require multiple and frequent contributions to be integrated. But… securing CICD best practices is an emerging, essential, yet little understood practice for DevOps teams and their Cloud Service Providers. The only way to get CICD to work in a highly secure environment takes collaboration, patience and persistence. Building CICD in the cloud requires rigorous ar...
Companies are harnessing data in ways we once associated with science fiction. Analysts have access to a plethora of visualization and reporting tools, but considering the vast amount of data businesses collect and limitations of CPUs, end users are forced to design their structures and systems with limitations. Until now. As the cloud toolkit to analyze data has evolved, GPUs have stepped in to massively parallel SQL, visualization and machine learning.
"Evatronix provides design services to companies that need to integrate the IoT technology in their products but they don't necessarily have the expertise, knowledge and design team to do so," explained Adam Morawiec, VP of Business Development at Evatronix, in this SYS-CON.tv interview at @ThingsExpo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
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 ...
Widespread fragmentation is stalling the growth of the IIoT and making it difficult for partners to work together. The number of software platforms, apps, hardware and connectivity standards is creating paralysis among businesses that are afraid of being locked into a solution. EdgeX Foundry is unifying the community around a common IoT edge framework and an ecosystem of interoperable components.
"ZeroStack is a startup in Silicon Valley. We're solving a very interesting problem around bringing public cloud convenience with private cloud control for enterprises and mid-size companies," explained Kamesh Pemmaraju, VP of Product Management at ZeroStack, 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.
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.
"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.
Enterprises are adopting Kubernetes to accelerate the development and the delivery of cloud-native applications. However, sharing a Kubernetes cluster between members of the same team can be challenging. And, sharing clusters across multiple teams is even harder. Kubernetes offers several constructs to help implement segmentation and isolation. However, these primitives can be complex to understand and apply. As a result, it’s becoming common for enterprises to end up with several clusters. Thi...