Welcome!

Microservices Expo Authors: Elizabeth White, Aruna Ravichandran, Pat Romanski, Liz McMillan, Cameron Van Orman

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

@BigDataExpo: Blog Feed Post

Demystifying #DataScience | @CloudExpo #BigData #AI #ArtificialIntelligence

Data science is about identifying those variables and metrics that might be better predictors of performance

[Opening Scene]: Billy Dean is pacing the office. He’s struggling to keep his delivery trucks at full capacity and on the road. Random breakdowns, unexpected employee absences, and unscheduled truck maintenance are impacting bookings, revenues and ultimately customer satisfaction. He keeps hearing from his business customers how they are leveraging data science to improve their business operations. Billy Dean starts to wonder if data science can help him. As he contemplates what data science can do for him, he slowly drifts off to sleep, and visions of Data Science starts dancing in his head…

[Poof! Suddenly Wizard Wei appears]: Hi, I’m your data science wizard to help alleviate your data science concerns. I don’t understand why folks try to make the data science discussion complicated. Let’s start simple with a simple definition of data science:

Data science is about identifying those variables and metrics that might be better predictors of performance

The key to a successful analytical model is having a robust set of variables against which to test for their predictive capabilities. And the key to having a robust set of variables from which to test is to get the business users engaged early in the process.

[A confused Billy Dean]: Okay, but I’m still confused. I mean, how does this really apply to my business?

[A patient Wizard Wei]: Well, let’s say that you are trying to predict which of your routes are likely to have under-capacity loads so that you can combine loads. In order to identify those variables that might be better predictors of under-capacity routes, you might ask your business users:

What data might you want to have in order to predict under-capacity routes?

The business users are likely to come up with a wide variety of variables, including:

Customer name Ship to location Customer industry
Building permits Customer tenure Change in customer size
Customer stock price Customer D&B rating Types of products hauled
Time of year Seasonality/Holidays Day of week
Traffic Weather Local Events
Distance from distribution center Open headcount on Indeed.com Tenure of logistics manager

The Data Science team will then gather these variables, perform some data transformations and enrichment, and then look for variables and combinations of variables that yield the best predictive results regarding under-capacity routes (see Figure 1).

Figure 1: Data Science Process

Role of Artificial Intelligence
[A less confuse Billy Dean]:
Ah, I think I understand, but what about all this talk about artificial intelligence? From some of these commercials on TV, it appears that robots with artificial intelligence will be ruling the world. Can you say Skynet?

[A still patient Wizard Wei]: Ah, that’s just marketing. Artificial intelligence is just one of many different tools in the predictive analytics kit bag of a data scientist. But artificial intelligence – while embracing some very sophisticated mathematical, data enrichment and computing techniques – is really pretty straightforward. All artificial intelligence is trying to do is to find and quantify relationships between variables buried in large data sets (see Figure 2).

Figure 2: Understanding Artificial Intelligence

[An inquisitive Billy Dean]: Okay, I’m starting to get it, but there seems to be some many
different analytic and predictive algorithms from which to choose. How does the business user know where to start?

[A growing frustrated Wizard Wei]: Ah, that’s the secret to the process. Business users don’t need to know which algorithms to use; they need to be able to identify those variables that might be better predictors of performance. It is up to the data science team to determine which variables are the most appropriate by testing the different algorithms.

Data Mining, Machine Learning and Artificial Intelligence (including areas such as cognitive computing, statistics, neural networks, text analytics, video analytics, etc.) are all members of the broader category of data science tools. Our data scientist team has experts in each of these areas, though no one data scientist is an expert at all of them (in spite of what they tell me). The different data science tools are used in different scenarios for different needs. Think of one of your mechanics. They have a large toolbox full of different tools. They determine what tools to use to fix a truck based upon the problem they are trying to solve. That’s exactly what a data scientist is doing, just with a different toolbox of algorithms.

No single algorithm is best over whole domain; so different algorithms are needed to cover different domains. Often combinations of algorithms are used in order to achieve the best results. To be honest, it’s like a giant jigsaw puzzle with the data science team constantly testing different combinations of metrics, data enrichment and algorithms until they find the combination that yields the best results.

[An enlightened Billy Dean]: I think I’ve finally got it. All of these different algorithms and techniques are just trying to help predict what is likely to happen so that I can make better operational and customer issues. But what’s the realm of what’s possible with data and analytics; I mean, how effective can my organization become at leveraging data and analytics to power my business?

[A proud Wizard Wei]: Great question, and the heart of the big data and data science conversation. Figure 3 shows how you could use these different data science tools to progress up the Big Data Business Model Maturity Index; to transition from running your business on Descriptive analytics that tell you what happened (Monitoring stage) to Predictive analytics that tell you what is likely to happen (Insights stage) to Prescriptive analytics that tell you what they should do (Optimization stage).

Figure 3: Leveraging Artificial Intelligence to drive Business Value

In the end, the data and the analytics are only useful if they help you optimize key operational processes, reduce compliance and security risks, uncover new revenue opportunities and create a more compelling, more prescriptive customer engagement. In the end, data and analytics are all about your business.

[A satisfied Billy Dean]: That’s great Wizard Wei! Thanks for your help!

Now, what can you do about my taxes…

To learn more about “Demystifying Data Science”, come to my Dell EMC World session: “Demystifying Data Science: A Pragmatic Guide To Building Big Data Use Cases” See you there!!

The post Demystifying Data Science appeared first on InFocus Blog | Dell EMC Services.

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.

@MicroservicesExpo Stories
We all know that end users experience the Internet primarily with mobile devices. From an app development perspective, we know that successfully responding to the needs of mobile customers depends on rapid DevOps – failing fast, in short, until the right solution evolves in your customers' relationship to your business. Whether you’re decomposing an SOA monolith, or developing a new application cloud natively, it’s not a question of using microservices – not doing so will be a path to eventual b...
Transforming cloud-based data into a reportable format can be a very expensive, time-intensive and complex operation. As a SaaS platform with more than 30 million global users, Cornerstone OnDemand’s challenge was to create a scalable solution that would improve the time it took customers to access their user data. Our Real-Time Data Warehouse (RTDW) process vastly reduced data time-to-availability from 24 hours to just 10 minutes. In his session at 21st Cloud Expo, Mark Goldin, Chief Technolo...
Digital transformation leaders have poured tons of money and effort into coding in recent years. And with good reason. To succeed at digital, you must be able to write great code. You also have to build a strong Agile culture so your coding efforts tightly align with market signals and business outcomes. But if your investments in testing haven’t kept pace with your investments in coding, you’ll lose. But if your investments in testing haven’t kept pace with your investments in coding, you’ll...
In his session at 21st Cloud Expo, Michael Burley, a Senior Business Development Executive in IT Services at NetApp, will describe how NetApp designed a three-year program of work to migrate 25PB of a major telco's enterprise data to a new STaaS platform, and then secured a long-term contract to manage and operate the platform. This significant program blended the best of NetApp’s solutions and services capabilities to enable this telco’s successful adoption of private cloud storage and launchi...
DevOps is often described as a combination of technology and culture. Without both, DevOps isn't complete. However, applying the culture to outdated technology is a recipe for disaster; as response times grow and connections between teams are delayed by technology, the culture will die. A Nutanix Enterprise Cloud has many benefits that provide the needed base for a true DevOps paradigm. In their Day 3 Keynote at 20th Cloud Expo, Chris Brown, a Solutions Marketing Manager at Nutanix, and Mark Lav...
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...
Containers are rapidly finding their way into enterprise data centers, but change is difficult. How do enterprises transform their architecture with technologies like containers without losing the reliable components of their current solutions? In his session at @DevOpsSummit at 21st Cloud Expo, Tony Campbell, Director, Educational Services at CoreOS, will explore the challenges organizations are facing today as they move to containers and go over how Kubernetes applications can deploy with lega...
Today most companies are adopting or evaluating container technology - Docker in particular - to speed up application deployment, drive down cost, ease management and make application delivery more flexible overall. As with most new architectures, this dream takes significant work to become a reality. Even when you do get your application componentized enough and packaged properly, there are still challenges for DevOps teams to making the shift to continuous delivery and achieving that reducti...
DevOps at Cloud Expo, taking place October 31 - November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA, is co-located with 21st Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world. The widespread success of cloud computing is driving the DevOps revolution in enterprise IT. Now as never before, development teams must communicate and collaborate in a dynamic, 24/7/365 environment. There is no time to w...
Is advanced scheduling in Kubernetes achievable? Yes, however, how do you properly accommodate every real-life scenario that a Kubernetes user might encounter? How do you leverage advanced scheduling techniques to shape and describe each scenario in easy-to-use rules and configurations? In his session at @DevOpsSummit at 21st Cloud Expo, Oleg Chunikhin, CTO at Kublr, will answer these questions and demonstrate techniques for implementing advanced scheduling. For example, using spot instances ...
SYS-CON Events announced today that Cloud Academy has been named “Bronze Sponsor” of 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. Cloud Academy is the leading technology training platform for enterprise multi-cloud infrastructure. Cloud Academy is trusted by leading companies to deliver continuous learning solutions across Amazon Web Services, Microsoft Azure, Google Cloud Platform, and the most...
The last two years has seen discussions about cloud computing evolve from the public / private / hybrid split to the reality that most enterprises will be creating a complex, multi-cloud strategy. Companies are wary of committing all of their resources to a single cloud, and instead are choosing to spread the risk – and the benefits – of cloud computing across multiple providers and internal infrastructures, as they follow their business needs. Will this approach be successful? How large is the ...
Many organizations adopt DevOps to reduce cycle times and deliver software faster; some take on DevOps to drive higher quality and better end-user experience; others look to DevOps for a clearer line-of-sight to customers to drive better business impacts. In truth, these three foundations go together. In this power panel at @DevOpsSummit 21st Cloud Expo, moderated by DevOps Conference Co-Chair Andi Mann, industry experts will discuss how leading organizations build application success from all...
DevSecOps – a trend around transformation in process, people and technology – is about breaking down silos and waste along the software development lifecycle and using agile methodologies, automation and insights to help get apps to market faster. This leads to higher quality apps, greater trust in organizations, less organizational friction, and ultimately a five-star customer experience. These apps are the new competitive currency in this digital economy and they’re powered by data. Without ...
A common misconception about the cloud is that one size fits all. Companies expecting to run all of their operations using one cloud solution or service must realize that doing so is akin to forcing the totality of their business functionality into a straightjacket. Unlocking the full potential of the cloud means embracing the multi-cloud future where businesses use their own cloud, and/or clouds from different vendors, to support separate functions or product groups. There is no single cloud so...
For most organizations, the move to hybrid cloud is now a question of when, not if. Fully 82% of enterprises plan to have a hybrid cloud strategy this year, according to Infoholic Research. The worldwide hybrid cloud computing market is expected to grow about 34% annually over the next five years, reaching $241.13 billion by 2022. Companies are embracing hybrid cloud because of the many advantages it offers compared to relying on a single provider for all of their cloud needs. Hybrid offers bala...
With the modern notion of digital transformation, enterprises are chipping away at the fundamental organizational and operational structures that have been with us since the nineteenth century or earlier. One remarkable casualty: the business process. Business processes have become so ingrained in how we envision large organizations operating and the roles people play within them that relegating them to the scrap heap is almost unimaginable, and unquestionably transformative. In the Digital ...
These days, APIs have become an integral part of the digital transformation journey for all enterprises. Every digital innovation story is connected to APIs . But have you ever pondered over to know what are the source of these APIs? Let me explain - APIs sources can be varied, internal or external, solving different purposes, but mostly categorized into the following two categories. Data lakes is a term used to represent disconnected but relevant data that are used by various business units wit...
The nature of the technology business is forward-thinking. It focuses on the future and what’s coming next. Innovations and creativity in our world of software development strive to improve the status quo and increase customer satisfaction through speed and increased connectivity. Yet, while it's exciting to see enterprises embrace new ways of thinking and advance their processes with cutting edge technology, it rarely happens rapidly or even simultaneously across all industries.
It has never been a better time to be a developer! Thanks to cloud computing, deploying our applications is much easier than it used to be. How we deploy our apps continues to evolve thanks to cloud hosting, Platform-as-a-Service (PaaS), and now Function-as-a-Service. FaaS is the concept of serverless computing via serverless architectures. Software developers can leverage this to deploy an individual "function", action, or piece of business logic. They are expected to start within milliseconds...