the journey to big data analytics

The Journey to Big Data Analytics

Dr. Stefan RadtkeCTO Isilon Storage Division, EMEADell EMC

IDC Conference, Madrid, January 31st 2017

2© Copyright 2016 Dell EMC All rights reserved.

Welcome!Dr. Stefan RadtkeCTO Isilon, EMEADell EMC | Storage Division

- 1995-2011 : 17 Years for IBM in various technical roles- 2012-2013 : Global Architect, EMC Global Alliances- 2013-2016 : CTO, EMEA, Isilon Storage Division, EMC- 2016-today : CTO, EMEA, Isilon Storage Division, Dell EMC

Phone: +49-176-34434460E-Mail: [email protected]: http://de.linkedin.com/in/drstefanradtkeBlog: http://stefanradtke.blogspot.com

mailto:[email protected]

http://de.linkedin.com/in/drstefanradtke

http://stefanradtke.blogspot.com/


Analytics affects all Industries

Smart factories, process control, supply/labour efficiency, and automation control

Smart meters and grids, preventative maintenance, and environmental monitoring

Smart infrastructure, traffic optimization, maintenance, and fleet tracking

Hospital patient monitoring, home healthcare, and remote diagnosis

Wearables, automotive, smart home, and entertainment disruptions

“just-in-time” management, promotions, and Location-based advertising

Manufacturing

Retail

Energy

Transportation/Infrastructure

Consumer

Healthcare


Step 2: Define question to be answered

Step 3: Use Business Intelligence (BI) tool’s graphical user interface (GUI) to construct query

Step 4: BI tool creates SQL

Step 5: SQL is run against data warehouse to create report

DW

Traditional BI Engagement ProcessStep 1: Pre-build data schema (schema-on-load)


Evolution Of The Analytic Questions

• How many widgets did I sell last month?

• What were sales by zip code for Christmas last year?

• How many of Product X were returned last month?

• What were company revenues and profits for the past quarter?

• How many employees did I hire last year?

What Happened?(Descriptive/BI)

What Will Happen?(Predictive)• How many widgets will I

sell next month?• What will be sales by zip

code over this Christmas season?

• How many of Product X will be returned next month?

• What are projected company revenues and profits for next quarter?

• How many employees will I need to hire next year?

What Should I do?(Prescriptive)• Order [5,0000] component Z to

support widget sales for next month• Hire [Y] new sales reps by these zip

code to handle projected Christmas sales

• Set aside [$125K] in financial reserve to cover Product X returns

• Sell the following product mix to achieve quarterly revenue and margin goals

• Increase hiring pipeline by 35% to achieve hiring goals


Step 1: Define Hypothesis to test or Prediction to be made

Step 3: Build schema (schema-on-query)

Step 4: Visualize the data (Tableau, Spotfire, ggplot2,…)

Step 6: Evaluate model results (probabilities, confidence levels)

Data Science Engagement ProcessR

epea

t

Step 5: Build analytic models (SAS, R, MADlib, Mahout,…)

Kronos

HistoricalGoogle Trends

PhysicianNotes

Local Events

Weather Forecast

EpicLawson

CDC

Step 2: Gather data…and more data (Data Lake: SQL + Hadoop)


Why we need to collect ALL data !


Holistic Data Collection

Data Lake


Companies understand the Value of Information but

many of them don’t know how to start the

journey.


Brainstorm the right questions to ask


Do not filter any Data SourcesBrainstorming predictive and prescriptive questions typically uncovers numerous new data sources that are worthy of consideration. And this is a key point: ALL data sources are worthy of consideration!

Do NOT filter the data sources at this point in the process.


But how do we get “Smart”First consideration: What is the business initiative or “what” we want to accomplish? For example, Reduce traffic congestions.

Some key questions/parameters to consider:• Traffic flow decisions: New roads? New lanes? New turn lanes? New bike lanes? Pedestrian crossings?

Railroad crossings? Bus stops?• Road repair and maintenance decisions: Fixing potholes? Repaving surfaces? Materials and

equipment needed? When to fix potholes and repave streets?• Construction permits decisions: Types of permits needed? Impact on traffic flow? Length of time to

complete the work? Number of employees to consider?• Events management decisions: Traffic (cars and pedestrians) attending proposed event? Impact on

normal traffic flow? Date, time, location and duration of events?• Parks decisions: Location of parks? Size of parks? Hours of operation? Park equipment maintenance?• Schools decisions: Location and size of new schools? Hours of operations? Location of stoplights and

stop signs?


Data Assessment: Value vs. FeasibilityBusiness Initiative: Improve Traffic Flow Im

pact

Feasibility


Prioritization Matrix

Hi

Hi

Lo Implementation Feasibility

Bus

ines

s Va

lue

C

AF

B

D

E

Use Cases (Decisions)A. Optimize Traffic FlowB. Improve Road Repair and

MaintenanceC. Optimize Construction

PermitsD. Improve Events

ManagementE. Optimize Park Hours and

ActivitiesF. Optimize School Hours and

Activities

Business Initiative: “Smart” City Initiative


KEEPING UP WITH NEW TECHNOLOGIES AND TOOLS

Product Performance & Reliability

CustomerSentimentAnalysis

SupplyChain

Optimization

CompetitiveWar Games

IDENTIFYINGTHE RIGHT USE CASE

FINDING, CURATING, AND GOVERNING THE DATA

Why do most efforts to stand up Big Data Initiatives stall or fail ?


Demonstrate the potential value using

data science techniques

Strategic Approach

Align business and IT goals

around big data

Identify strategic opportunities for

big data analytics

Prioritize key use cases by

assessing feasibility and

ROI

Recommend the appropriate

analytics engagement and

deployment roadmap

1 2 3 4 5

Workshop Objectives


Get help for the Journey

Platform for Big Data and

Analytics Solutions

BUSINESS

TECHNOLOGY

DEPLOYASSESS PROVE

Big Data Proof of

Value

Big DataProof of

Technology

Big Data Applied Analytics Implementation

Big DataTechnology

Implementation

Big Data Vision

Workshop

Big Data Technology

Advisory

Dell EMC Service Offerings


My prediction: market is looking for well integrated systems. Easy to use and with flexible deployment options.


Advance Analytic (Data Science) ModelsGraph AnalyticsForecasting

Time Decomposition

Association Analytics

Behavioral Analytics


Complex and growing Ecosystem


DATA LAKE

COMPUTE:EMC Converged Platform

Integrated and Converged Infrastructure

PLATFORM MANAGER

ADMINISTRATION ANALYTICS CATALOG DATA CATALOG

Rich UX

INFRASTRUCTURE SOFTWARE OPTIONS

YOUR WORKSPACE

COMPUTE, NETWORK,STORAGE

CONVERGED INFRASTRUCTUREDATA SETS

DATA CURATOR

ENRICH

INGEST

INDEX

FIND AND INGEST DATA

AT LEAST ONE HADOOP DISTRIBUTION

DATA GOVERNOR

LINEAGE

QUALITY

SECURITY

GOVERN/PUBLISH

DATA, TOOLS, APPS

Integrated Analytics 3 rd Party Openness

DATA SCIENCEPivotal Big Data Suite

EXTENSION PACKSR, RStudio, Anaconda

python, jupyter

PUBLISHED TOOLS AND DATASETS


Thanks for your Attention!Dr. Stefan RadtkeCTO Isilon, EMEADell EMC | Storage Division

- 1995-2011 : 17 Years for IBM in various technical roles- 2012-2013 : Global Architect, EMC Global Alliances- 2013-2016 : CTO, EMEA, Isilon Storage Division, EMC- 2016-today : CTO, EMEA, Isilon Storage Division, Dell EMC

Phone: +49-176-34434460E-Mail: [email protected]: http://de.linkedin.com/in/drstefanradtkeBlog: http://stefanradtke.blogspot.com

mailto:[email protected]

http://de.linkedin.com/in/drstefanradtke

http://stefanradtke.blogspot.com/

the journey to big data analytics

Data & Analytics