Matt Maccaux – InFocus Blog | Dell EMC Services https://infocus.dellemc.com DELL EMC Global Services Blog Thu, 13 Dec 2018 11:38:05 +0000 en-US hourly 1 https://wordpress.org/?v=4.9.7 Accelerating Exploratory Analytics with Big Data as a Service https://infocus.dellemc.com/matt_maccaux/accelerating-exploratory-analytics-with-big-data-as-a-service-bdaas/ https://infocus.dellemc.com/matt_maccaux/accelerating-exploratory-analytics-with-big-data-as-a-service-bdaas/#respond Tue, 11 Sep 2018 12:55:21 +0000 https://infocus.dellemc.com/?p=36114 In today’s digital age, the Big Data landscape is rapidly evolving for both data science and IT teams—with a steady stream of new products, tools and frameworks being released and incorporated into an already complex ecosystem. Data scientists and developers want flexibility and choice, with on-demand access to new Big Data technologies such as machine […]

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In today’s digital age, the Big Data landscape is rapidly evolving for both data science and IT teams—with a steady stream of new products, tools and frameworks being released and incorporated into an already complex ecosystem. Data scientists and developers want flexibility and choice, with on-demand access to new Big Data technologies such as machine learning and artificial intelligence. IT managers are under pressure to support these new innovations and the ever-changing menagerie of tools, while also providing enterprise grade IT security and control. Meanwhile, the demands from the business for new analytical capabilities is growing faster than the organization can support.

Under these conditions, it has become increasingly difficult for enterprises to keep up with the pace of change in Big Data. Traditional deployment methodologies and architectures using bare metal servers with direct-attached storage for data lakes can quickly become disk/storage-constrained as an organization’s use of the data expands. As nodes/servers are added, the management overhead becomes costly and inefficient, not to mention the costs of the servers themselves.

A common approach to address this problem is to spread the data around across multiple Hadoop® clusters. However, with the rapid growth of data, this also becomes inefficient to maintain, and even more so if copies of data reside on multiple clusters. As clusters proliferate and the number of analytics and data science applications and tools increases, enforcing access restrictions and policies can also be challenging as the environment scales.

Additionally, the time-consuming nature of manually building a new environment for each user—to acquire a compute node with storage, install the operating system, install the Hadoop version and applications, patch, test and deploy, and then secure all of those components—can compound the chances of errors and cause costly delays to the business.

Our Approach to Exploratory Analytics

In previous blogs, we’ve talked a lot about our Elastic Data Platform.  The Elastic Data Platform is a proven and cost-effective solution that enables organizations to address these challenges at speed and scale for exploratory analytics and flexible workloads. The integrated solution is designed to extend and augment an organization’s existing Big Data investments with workload-specific infrastructure, intelligent software, and end-to-end automation and is delivered by Dell EMC Consulting to accelerate the time to value.  Check out the short video below to learn more.

New Ready Solutions for Big Data

If you are looking to upgrade your Big Data infrastructure, Dell EMC has you covered with newly announced Ready Solutions for Big Data and a Big Data as a Service (BDaaS) design. The pre-engineered, integrated solutions include the Dell EMC best-of-breed servers and networking, BlueData EPIC software, and services to fast-track and simplify your analytics journey with a secure, on-premise Big Data as a Service capability.  The solution leverages core components of the Elastic Data Platform, including cluster deployment and multi-tenancy, enabling self-service analytics, provisioning in minutes, and tooling flexibility.

Experts Every Step of the Way

The Ready Solutions for Big Data include consulting, deployment and support services from Dell EMC Services to help customers drive the rapid adoption and optimization of solution in their Big Data environments from initial set up and integration through to ongoing support and roadmap planning.

During a 6-week engagement, Dell EMC consultants work with you to identify the analytics use case that will have the most business impact, gather requirements and design the solution architecture. Our teams then install, configure and integrate the Ready Solution into the customer’s environment for the prioritized use case. This includes tying into the existing security framework (e.g., Active Directory), connecting to existing Hadoop systems, and developing custom application package templates for data scientists, analysts, and engineers to get a fast start with the solution.

Dell EMC Consulting will also run workshops and develop a roadmap for how the BDaaS solution can be extended to the rest of the enterprise to include consolidating systems and infrastructure, centralizing the data lake, and offering end-to-end automated provisioning leveraging the existing IT ticketing systems (e.g., ServiceNow).

Once the Ready Solution is implemented, Dell EMC ProSupport provides comprehensive hardware and collaborative software support to help ensure optimal system performance and minimize downtime. Customers can also opt for ProSupport Plus to get a Technology Service Manager who provides a single point-of-contact for support. And, Dell EMC Education Services offers a range of courses and certifications on Data Science and Advanced Analytics which is a great option for training teams to use the new technologies.

Getting Started

The new Dell EMC Ready Solutions for Big Data offer a compelling way to fast-track and simplify Big Data as a Service and exploratory analytics using Dell EMC’s world class technology and proven expertise and services. We also offer the accelerated 6-week Big Data as a Service implementation option standalone or can deliver a full enterprise-wide solution.

Are you ready to harness the power of big data and analytics to transform your organization? To learn more, reach out to your Dell EMC representative or checkout Dell EMC Consulting Services to learn how we can help you get started on your transformation.

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3 Imperatives for Artificial Intelligence Success: People, Process, Technology https://infocus.dellemc.com/matt_maccaux/3-imperatives-for-ai-artificial-intelligence-success-people-process-technology/ https://infocus.dellemc.com/matt_maccaux/3-imperatives-for-ai-artificial-intelligence-success-people-process-technology/#comments Tue, 07 Aug 2018 15:45:15 +0000 https://infocus.dellemc.com/?p=35920 As I speak to customers about Big Data and Analytics, the topic of Artificial Intelligence (AI) inevitably comes up. I am often asked how organizations can use AI to monetize their data and transform their business processes. While Artificial Intelligence can absolutely help drive those kinds of results, there isn’t a simple answer to these […]

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As I speak to customers about Big Data and Analytics, the topic of Artificial Intelligence (AI) inevitably comes up. I am often asked how organizations can use AI to monetize their data and transform their business processes.

While Artificial Intelligence can absolutely help drive those kinds of results, there isn’t a simple answer to these types of question because there are many challenges that must be overcome first, particularly around:

  • Having a strategy and the right processes in place
  • Skills and expertise of analytical users
  • Big Data and analytical platforms and technologies

The best way to get started is to focus on the business outcomes and analytical models and not on the underlying technologies.

Our Approach to Artificial Intelligence

Dell EMC Consulting has been helping organizations of all sizes, industries, and maturity levels adopt and accelerate their analytical journey by making data-driven decisions to select the right use cases, implement scalable platforms, and operationalize algorithms to drive business outcomes. We do this by partnering with business stakeholders to select the killer use cases, apply the right technologies to implement them, and provide expert data science and engineering resources to get quick wins to establish meaningful change throughout the organization.

We work with organizations to validate the use cases for analytical feasibility and then select the right tools and techniques, leveraging Artificial Intelligence, Machine Learning, and Deep Learning techniques. Our methodology is to not only create the analytical models leveraging these tools and techniques, but also to teach organizations how to build and maintain them over time. Our goal is to help our customers become self-sufficient by providing scalable solutions and best practices around the analytical toolsets.

For any Artificial Intelligence implementation to be successful, it’s imperative that you bridge between the people, process and technology needed to achieve the business outcomes you want. Our Consulting practice works with customers to do just that, and helps ensure you get the most from your Big Data investments.

New Ready Solutions for AI (Artificial Intelligence)

When it comes to the technologies, Dell EMC has you covered with verified architectures and deployments in the form of Ready Solutions. Dell EMC just announced new Ready Solutions for AI with a design for Machine Learning with Hadoop and one for Deep Learning with NVIDIA. These end-to-end solutions include the Dell EMC best-of-breed hardware, software, and services needed to fast-track and simplify your AI journey.

Dell EMC Services are an integral part of the solutions, helping customers drive the rapid adoption and optimization of their AI environments from initial set up and upskilling of resources through to ongoing support. A core set of services are included with the solutions, and other value-added services are available for customers to choose from based on their business requirements and priorities.

Some of the key areas our services teams deliver value for Ready Solution for AI customers include:

  • Implement and operationalize the Ready Solution technologies and AI libraries, and scale customer Data Engineering and Data Science capabilities
  • Provide technical architecture recommendations and data science best practices
  • Offer courses and certifications on Data Science and Advanced Analytics and workshops on Machine Learning in collaboration with NVIDIA to develop the solution and technology skills needed to fully leverage the AI capabilities
  • Provide comprehensive hardware and collaborative software support to help ensure optimal system performance and minimize downtime

Key areas our services teams deliver value for Ready Solution for AI

 

For the remainder of this blog, let’s delve into how Dell EMC Consulting helps customers operationalize and accelerate the time to value of the Ready Solutions for AI so they can start getting actionable insight from their data.

New AI Consulting Services for the Ready Solutions

To support the rapid adoption and integration of the Dell EMC Ready Solutions for AI, we offer new AI advisory and implementation services that span the initial environment set up, operationalizing the solution and building the necessary data science and data engineering capabilities.

Strategic Guidance and Expert Integration

Our consultants can engage from the start to plan and manage the Ready Solution implementation and help operationalize it in the customer’s environment.  This includes setting up a Cloudera Data Science workbench for the Hadoop design and for NVIDIA, we can help test and configure the AI libraries and tools.

Additionally, we can make architectural recommendations for your data science platform, along with providing best practices for data science, methods, tools and processes.  Defining the right, collaborative processes across teams – such as from the lines of business to data scientists and IT – is particularly important, as is industrializing the process to get as much value from your data as you can, as quickly as you can.

For more on process and tools, see these related blogs:

Achieving Business Results in the New Environment

To realize the value from the new Dell EMC AI environment, customers need to have data science and data engineering capabilities.  Whether your organization is new to data science or you’re already far down the path, we can help get you where you need to be using a tailored approach driven by business objectives and priorities.

Data Science:

Dell EMC Consulting’s global team of data scientists will work with your business users to identify the right use cases and scope out the effort to implement them. Then, we will help your own teams, through an iterative process, develop and refine the data models and analytical algorithms that will be operationalized. We will also work with existing reporting teams to ensure that the models are measured and populating business KPIs. Finally, we will train your analytical teams on the new tools, methods, and techniques that leverage the ML/DL/AL technologies.

Data Engineering:

In addition to the Data Science expertise, Dell EMC Consulting has global expertise in data engineering to take those refined models and algorithms to an operational state. This is sometimes referred to as “monetization”, but really it is about the creation of value for your organization. This involves some of the “data plumbing” which connects the models to data sources, transforms and enriches the data, and creates output APIs to feed other applications. This is all done with an eye to security and privacy to ensure that your organization remains in compliance with regulatory and industry laws and policies. We will also work with your data architecture and engineering teams to ensure that these new capabilities integrate and align with your existing Big Data environment.

Bringing It All Together

Dell EMC has the capabilities, experience, and technologies to help organizations accelerate their adoption of advanced data science technologies and techniques for Artificial Intelligence, Machine Learning, and Deep Learning.  The new Dell EMC Ready Solutions for AI offer a compelling way to fast-track and simplify AI using Dell EMC’s world class technology and proven AI expertise and services.  To learn more, contact your account executive or comment below.

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Driving Competitive Advantage through Data Monetization https://infocus.dellemc.com/matt_maccaux/driving-competitive-advantage-data-monetization/ https://infocus.dellemc.com/matt_maccaux/driving-competitive-advantage-data-monetization/#respond Tue, 24 Apr 2018 09:00:56 +0000 https://infocus.dellemc.com/?p=35051 Data monetization is a much-discussed topic in the business world these days – the holy grail of enterprise analytics initiatives and an enabler for digital transformation and market differentiation. The value is undeniable yet the journey to get there can be tricky and complex to navigate. So, how do organizations successfully monetize their data to […]

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Data monetization is a much-discussed topic in the business world these days – the holy grail of enterprise analytics initiatives and an enabler for digital transformation and market differentiation. The value is undeniable yet the journey to get there can be tricky and complex to navigate.

So, how do organizations successfully monetize their data to drive competitive advantage? First and foremost, understand that it is not all about the technology, nor should it be a technology-led discussion.

In this blog, I’ll focus on 3 essential and interlocking elements:

  • People – the right skilled resources working on cross-functional, integrated teams
  • Process – collaborative and agile ‘DataOps’ processes
  • Technology – modern infrastructure and tools

Building a High Performing, Cross-Functional Team

The monetization of data relies on the people performing the analytics and building the systems that operationalize them. There are several different groups that work to make that happen:

  • Data Scientists – these are the people get all the press. They experiment by starting with open ended questions such as, how can the organization increase share of wallet? Or in the case of a public school system, how can we help underserved students learn and perform better?  These folks want it all – all the data, all the resources, bring their own data, bring their own tools, and more. And you need to keep them happy because they are in high demand.
  • Data Analysts – these are the people that do more traditional analytics using traditional tools such as SAS, Tableau, etc. These folks make up the biggest part of the analytical community and provide the operational reporting that keeps the organization on track. As the tools get more sophisticated however (e.g., SAS Viya), these users are going to start looking a lot more like data scientists.
  • Engineers and Operations – these folks take the outputs from the data scientists and analysts and put the systems, infrastructure, and applications in place to operationalize the models and reports. These users are software developers and system operators and don’t need access to sensitive data, but will need to work in production-like environments to ensure the models and reports function as expected. For example, they would orchestrate taking a completed model with streaming data and feeding the output via APIs to be consumed by applications.
  • Data Stewards – these are the people in the business that understand the context for the data – where it came from, how it changes, the security and access requirements/restrictions, and most importantly – what the data means.

Successful organizations have these groups working together on projects from start to finish so that everyone is working towards the same end goal in an integrated, streamlined fashion. Most data science projects fail because organizations follow the traditional process of ‘check the code in and move on’ and the process breaks down when the engineer or operator can’t apply the model to an application. For example, if a data scientist builds a model against static or batch data however the applications that process the information use streaming data, it can change the way the model works and cause costly delays.

Optimizing Processes for Data Monetization at Speed and Scale

How much time does your organization spend deploying infrastructure? How about tearing it down? Are you cloud-like in the way you do things?

Since we are talking about data monetization, digital transformation is at the heart of the solution and it isn’t just data. The process to provision data, build models, publish them, build applications, and provide new experiences for your users/customers requires a new approach to application development and at the heart of it is DevOps, or in this case DataOps. How repeatable is your process? Organizations should be thinking about this in the same terms as you do modern application development.

You also need to make sure you’re targeting the right high impact use cases.  As organizations gain maturity in data science and analytics, there will be no shortage of use cases and ideas. It’s imperative that organizations follow a process upfront to identify and prioritize business use cases that will have the most benefit with the lowest barrier to implement.  The worst case scenario is that you spend the effort to operationalize the analytics and it doesn’t have a measurable effect on the business.

Another critical area for process excellence is enabling your data scientists to be productive day in and day out.  Success hangs on their ability to find and apply patterns in your data – so get them what they need fast and get out of their way!  This means provisioning analytics environments in minutes, not months, and having a safe space for them to test out ideas without blowing anything up.  To achieve this from a process standpoint, organizations need to automate the end-to-end process and proactively identify and eliminate any gaps or delays in the discovery and monetization lifecycle.

And Then There’s the Technology…

Rather than dive into specific technologies, let’s talk about some of the guiding principles that the technology should be architected for:

  1. Data that is made available easily and freely, but in a controlled and secured manner – that means not creating physical copies of the data for each and every user
  2. Isolated “sandbox” environments so users can freely experiment with the data using tools of their choice, without the risk of corrupting the “master” or production data
  3. Environment elasticity that scales up and down based on the user workload and analytical requirements
  4. Compliance with all governance, security, and regulatory rules and processes
  5. Cloud-ready, be it private, public or hybrid cloud deployments

IT teams simply cannot keep up with the demand from analytical users following the old way of provisioning static environments on bare metal servers with a limited set of tools. It is equally challenging for IT to keep up with the sheer volume of new and updated tools and libraries, especially in the machine learning, deep learning, and AI space. We recommend that IT focus on providing the core infrastructure as-a-Service and giving users the freedom to bring their own tools so that they can innovate at the pace the business requires.

At Dell EMC, we offer a comprehensive portfolio of Big Data & IoT Consulting services from strategy through to implementation and beyond and help bridge the people, process, and technology for organizations to accelerate the time to value of their data monetization initiatives.

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Avoid the Top 5 Big Data Mistakes with an Elastic Data Platform https://infocus.dellemc.com/matt_maccaux/avoid-top-5-big-data-mistakes-elastic-data-platform/ https://infocus.dellemc.com/matt_maccaux/avoid-top-5-big-data-mistakes-elastic-data-platform/#comments Mon, 13 Nov 2017 10:00:36 +0000 https://infocus.dellemc.com/?p=33024 Many organizations are striving to extract more value from their Big Data investments. They must determine what capabilities are required. Many organizations are failing to examine the problems that plagued the early adopters that came before them. These early adopters encountered scalability, organizational, and business value related challenges stemming from a few common mistakes. We […]

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Many organizations are striving to extract more value from their Big Data investments. They must determine what capabilities are required. Many organizations are failing to examine the problems that plagued the early adopters that came before them. These early adopters encountered scalability, organizational, and business value related challenges stemming from a few common mistakes. We call these mistakes Anti-Patterns or the opposite of best practices.

In this video from the Strata Data Conference, I explain these concepts:

  • Silos of Information: if information is contained in silos throughout the organization, it becomes costly to maintain and difficult to manage, not to mention how hard it is to meaningfully query across it.
  • Too Much Governance: what good is having a data lake if an organization has so many restrictions on it that no one has meaningful access to it?
  • Not Enough Governance: if you can’t trust the quality or veracity of the data, then how can the business ever take advantage of it?
  • Inelastic Architecture: when organizations hit scalability limits on existing deployments, simply adding more infrastructure won’t help solve the problem. Bare-metal is easy to deploy, but brittle to manage.
  • Pet Projects: if you build it, they will come is great for baseball, but not Analytics!

So what’s the answer?

Dell EMC’s Elastic Data Platform provides a best-in-class approach to avoid these Anti-Patterns and enables analytical self-service to drive speed to insights.

Please let me know what you think!

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