The New Normal: Predictive Power on the Front Lines

The Briefing Room

Twitter Tag: #briefr

The Briefing Room

Welcome

Host: Eric Kavanagh

[email protected]


The Briefing Room

!   Reveal the essential characteristics of enterprise software, good and bad

!   Provide a forum for detailed analysis of today’s innovative technologies

!   Give vendors a chance to explain their product to savvy analysts

!   Allow audience members to pose serious questions... and get answers!

Mission


The Briefing Room

FEBRUARY: Analytics

March: OPERATIONAL INTELLIGENCE

April: INTELLIGENCE

May: INTEGRATION


The Briefing Room

Analytics

Hindsight and Insight are fairly common

© C

rist

ian

Farc

as |

Dre

amst

ime.

com

PREDICTIVE CAN BE ELUSIVE


The Briefing Room

Analyst: Mike Ferguson

Mike Ferguson is Managing Director of Intelligent Business Strategies Limited. As an independent analyst and consultant, he specializes in business intelligence, data management and enterprise business integration. With more than 30 years of IT experience, Mike has consulted for dozens of companies, spoken at events all over the world and written numerous articles. Formerly he was a principal and co-founder of Codd and Date Europe Limited – the inventors of the Relational Model, a Chief Architect at Teradata on the Teradata DBMS and European Managing Director of DataBase Associates where he was a partner with Colin White.


The Briefing Room

! Alteryx provides an enterprise-class analytics platform which enables users to combine Big Data with information assets across the organization

!   Analysts can perform predictive and spatial analytics, as well as produce sharable apps

! Alteryx’s Strategic Analytics Software is a desktop-to-cloud solution that combines business data, industry content and spatial processing

Alteryx


The Briefing Room

Matt Madden

Matt Madden is Senior Product Marketing Manager at Alteryx. He has over 13 years of experience helping organizations realize the power and benefits of analytics in the roles of Sales and Marketing.

9

Alteryx blue and tints for headings and main graphics

Secondary palette: For support in charts, graphics, and callouts

Accent colors (use sparingly)

© 2012 Alteryx, Inc.

The New Normal: Predictive Power on the Front Lines

Matt Madden- Sr. Product Marketing Manager

10





•  New sources creating huge volumes of “Big Data” •  Social Media •  Sensors •  Radio Frequency ID (RFID) •  Log files

•  More Data= More Questions= More Decisions

•  Predictive analytics provides tremendous potential for high-value analysis & decisions

•  Customer Analytics •  Marketing Optimization •  Market Basket Analysis •  Inventory Analysis •  Reducing Churn

Decisions start with data

Predictive

Value

Big Data

11





•  Traditional Business Intelligence (BI) platforms are backward-looking •  Predictive Analytics represents the highest value •  Historically, Predictive Analytics have also been the most complex to

implement

Predictive Analytics: A Competitive Imperative

Reporting

Analysis

Monitoring

Prediction

Reporting- What happened? •  Query, reporting tools

Analysis- Why did it happen? •  OLAP & visualization tools

Monitoring- What’s happening now? •  Dashboard, scorecards

Prediction- What might happen? •  Predictive analytics

*Source: The Data Warehousing Institute (TDWI) www.tdwi.org

12





•  Time-consuming •  Requires specialized expertise

The “Old Way” Doesn’t Work Any More

•  Expensive •  Hard to rapidly iterate

13





•  Faster time from question to insight •  No specialized skills required

Alteryx Strategic Analytics: A New Approach

•  Lower cost •  Iteration-friendly

14





Structured | Semi-structured | Unstructured

Integration

Local & Productivity

Alteryx Big Data Architecture – App Design

Inter Source Data Integration

Personal ETL access &

integrate any data

Agile Database large scale, rapid data processing

Data Quality keep your data

clean and credible

Data Management define and map

relationships between data

Analytics Predictive

Drive foresight with R based tools

Statistical Understand and

build models

Spatial Deep understanding of location intelligence

In DB

Analytics

Ingest

Output &

U

pload

Data Warehouse NoSQL Hadoop Big Data

Discovery

Analytics Consumption

Analytic Apps 3rd Party Multiple Format Output

IT / DW Team

(Coming soon)

Publish

Output

15





Alteryx Analytic Workflow – Step 1

Un-Structured Content

App &

Data

All Relevant Data

16






Integrate any data source

Integrate


App &

Data

All Relevant Data

17







Integrate


App &

Data

All Relevant Data

Enrich

Packaged Market & Customer Data

18







Integrate

Un-Structured Content Rapid design of

predictive analytics

Analyze

App &

Data

All Relevant Data

Enrich


19







Integrate

Un-Structured Content Rapid design of

predictive analytics

Analyze

App &

Data

All Relevant Data

Enrich


20





Create & Share Analytic Apps in Cloud – Step 2

Assemble App

21






Assemble App

Private or Public Cloud

Publish

22






Assemble App

Private or Public Cloud

Publish

Run

23





•  Emerging data sources •  High volume •  High velocity •  High variability

•  New data platforms •  Hadoop •  NoSQL

•  Alteryx advantage •  Easily integrate non-traditional

data •  Leverage technology and cost

advantages of next-gen platforms

Integrate “Three-V” Data


24





Harness third-party data sources to provide data on: •  Consumers

•  Age, income, education, etc.

•  Locations •  i.e. Local business and

residential spending projections

•  Competitors •  Employees, revenue,

locations, etc.

•  Drive times •  Traffic patterns, typical

weather, road types, etc.

Include Third-Party Data for a Complete Picture

25





•  Take the value of Predictive Analytics beyond the “Ivory Tower”

•  Empower front-line employees •  Clerks, customer service

agents, field service personnel

•  Harness the power of the R Analytical language

•  Over 20 Prepackaged analytic techniques

•  No coding required •  Drag-and-drop •  Tightly integrated

Move Predictive Analytics to the Front Lines

26





Key Requirements: •  Unify customer data

across multiple sources •  Improve direct mail

campaign execution and results

•  Maximize revenue generation from catalogue business

•  Enhance ROI from mailings

Southern States Cooperative Continues Success With Increased Campaign Response and Revenue

“My number one responsibility is to make sure we understand our customers’ needs and wants, I use Alteryx every single day to do just that ” Greg Bucko, Manager of Customer Insights.

•  Customer focused analytics improve response rates by 63% •  More targeted mailings improving gross margin for each campaign •  Extending insights to full range of customer channels including retail

27





Demonstration Richard Snow

28





•  Organizations must adapt: •  From backward-looking to

forward-looking •  Beyond traditional data sources •  To deliver the value of

Predictive Analytics to the front line

Conclusion

“My number one responsibility is to make sure we understand our customers’ needs and wants, I use Alteryx every single day to do just that” Greg Bucko, Manager of Customer Insights.

29





•  Organizations must adapt: •  From backward-looking to

forward-looking •  Beyond traditional data sources •  To deliver the value of

Predictive Analytics to the front line

•  Alteryx’s unique approach delivers:

•  Far broader accessibility for Predictive Analytics

•  Much lower cost and complexity

•  Deeper insight into data (Social Media & Big Data, Third-party Data, Traditional sources)

Conclusion

“My number one responsibility is to make sure we understand our customers’ needs and wants, I use Alteryx every single day to do just that” Greg Bucko, Manager of Customer Insights.

30





Contact Info

www.alteryx.com

Learn More or Get the 30-Day Trial:

gallery.alteryx.com

Visit the Analytics Gallery:

@alteryx

Contact us: 1-888-836-4274 www.alteryx.com/contact-alteryx

31





“Overall the best single company sponsored conference I've ever attended.” ─Antoinette Bowen, Sr. Marketing Manager, AT&T Mobility

Inspire 2013

Sheraton Phoenix Downtown Hotel March 5-7, 2013

Learn More! www.alteryx.com/inspire

Follow Us on Twitter! @alteryx #Inspire13

Seize the Power of Strategic Analytics


The Briefing Room

Analyst: Mike Ferguson

Perceptions & Questions

Alteryx In The Briefing Room

Mike Ferguson Managing Director Intelligent Business Strategies February 2013 www.intelligentbusiness.biz Twitter: @mikeferguson1

34

Operational data

web

P o r t a l

BI Tools

Platform Dat

a In

tegr

atio

n / D

Q

Traditional Data Warehousing and Business Intelligence

Reports & analytics

Data warehouse & data marts

DW

Data Warehousing

What is Data Warehousing? Data warehousing is the process of building an analytical system by cleaning and integrating data from multiple data sources The analytical system can consist of 1 or more databases

Business Intelligence

What is Business Intelligence? Business Intelligence is actionable business insight that is produced by querying and analysing data in a data warehouse or a data mart using BI tools A typical organisation has information producers and information consumers.

35

What Is Self Service BI?

“ The creation of a BI environment whereby business users can create and access BI reports, queries, and analytics without the need for IT involvement”

§  Business users need to be able to: •  Be more self-sufficient •  Collaborate with others to share insights and make decisions •  Access personalised business insight

§  Self-service BI options •  Data discovery and visualisation tools •  Analytical workflow and visualisation tools

§  Self-service BI is NOT about self-service data warehousing •  Data governance and common data definitions are critical to

maximising the use of trusted data and facilitating common understanding

36

Self-Service BI Data Discovery and Visualisation Tools Allow Users to Quickly Produce Insight – e.g. Insurance

In-memory data Data

visualisation server with in-memory columnar storage

Data discovery and

visualisation tool

DW Underwriting system

Re-insurance data

Ultimates data

Predictive model

e.g. Calculate Net Premiums and Claims even when re-insurance data is not in the DW

community

Consume / Enhance / Re-publish / Act

Publish / Share

insights

37

Self-Service Analytical Workflow Development & Visualisation Tools Allow Users to Quickly Produce Insight – e.g. Insurance

Workflow execution Analytical Workflow Execution

Server

Analytical workflow development and visualisation tool


Re-insurance data

Ultimates data

Predictive model

e.g. Calculate Net Premiums and Claims even when re-insurance data is not in the DW

community

Publish / Share

insights Consume / Enhance / Re-publish / Act

38

Predictive Analytics Are Now Becoming Available In Self-Service BI Tools – But Do Users Know How to Use Them

Data Discovery & Visualisation OR

Analytical workflow server

Business Analyst


Re-insurance data

Ultimates data

Predictive model

Predictive models

community

Publish / Share

insights Consume / Enhance / Re-publish / Act

The challenge is making it easy for non-statistically

trained business analysts to select the right algorithms for the business questions they are trying to answer

39

Impact of Self-Service BI/Analytical Tools on Data Management

§  Business users needing data from multiple sources are using front end tools for data integration rather than for data analysis and visualisation

§  Potentially inconsistent data definitions and calculations for the same data created by every user doing their own data integration

§  Potentially a major increase in the proliferation of overlapping data sets created by self-service BI business users not connecting to data via a BI platform semantic layer

§  Potential for multiple versions of unmanaged data scattered throughout the enterprise •  Potential for multiple versions of reference data

§  Potential for inconsistent data everywhere and not just created by Excel users

40

Self-Service BI

Data Discovery & Visualisation OR

Analytical workflow server

Business Analyst

Data Virtualization

personal & office

data Predictive models

community

Publish / Share Consume / Enhance / Re-publish

Transaction systems

Data Management

DW

Simplifying And Governing Data Access to Improve Self-Service BI – One Approach is Via Data Virtualisation

41

Governing Information Distribution Is Also Important - Information Producers and Information Consumers

Information Producers Information Consumers

Executives, Managers, Frontline workers, Customers, Partners, Suppliers

Business & Financial Analysts, IT Developers, Some Managers

Information Distribution

Govern distribution

Govern who can produce, what data they can access and how they name data

Govern what they can access and what devices they can use Business

glossary

Business glossary

42

Sales

Product line n

Product line 4

Product line 3

Product line 2

Product/service line 1

Marketing

Service

Credit Verification

HR

Finance

Planning

Procurement

Sup

ply

Cha

in

Sup

plie

rs

Front Office BackOffice

Operations

Cus

tom

ers

New Data Sources Have Emerged Inside And Outside The Enterprise That Business Now Wants To Analyse

E.g. RFID tag

sensor networks

weather data Data volume Data variety Number of sources

Data volume Data variety

43

Big Data Has Taken Us Beyond The Traditional Data Warehouse – New Big Data Analytical Workloads

1.  Complex analysis of structured data

2.  Analysis of data in motion

3.  Exploratory analysis of un-modeled multi-structured data

4.  Graph analytics

5.  Accelerating ETL and analytical processing of un-modeled data to enrich data in a data warehouse or analytical appliance

6.  The storage and re-processing of archived data

44

The Changing Landscape – We Now Have Different Platforms Optimised For Different Analytical Workloads

Streaming data

Hadoop data store

Data Warehouse RDBMS

NoSQL DBMS

EDW

DW & marts

NoSQL DB e.g. graph DB

Advanced Analytic (multi-structured data)

mart DW

Appliance

Advanced Analytics (structured data)

Analytical RDBMS

Big Data workloads result in multiple platforms now being needed for analytical processing

45

Hadoop ‘Sandboxes’ Are Common for Data Scientist Led Investigative Analysis of Multi-structured Data

Web logs

ETL MapReduce Applications (batch analysis)

new insights

sandbox sandbox Un-modelled data

Seismic data

sensor data

46

ETL Acceleration Is Also A Popular Big Data Use Case For Bringing Additional Insights Into Data Warehouses

Cloud Data

HDFS

Extract

DW D I Map/ Reduce

analytical applications

Transform

e.g. PIG, JAQL

Cloud Data e.g. Deriving insight from huge volumes of social web content on sites like Twitter, Facebook. Digg, MySpace, TripAdvisor, Linkedin….for sentiment analytics

Hundreds of terabytes up to petabytes

relevant insight

Operational systems

47

This Requires Parsing & Extraction From Multi-Structured Data While Integrating Data In A Big Data Environment

E-mail (semi-structured)

Text (unstructured)

Extract Parse Transform Load …

48

Data Deluge – Need To Accelerate And Automate Data Filtering To Consume Data That Is Arriving Faster Than We Can Consume It

F D I A L T T A E R

Enterprise

Enterprise systems

49

Data Management Tools Are Being Extended To Embrace And Exploit MPP Hadoop Clusters AND Embed Analytics

Parse & Prepare Data in Hadoop (MapReduce)

Load Data into Hadoop

Transform & Cleanse Data in Hadoop (MapReduce)

Extract Data from Hadoop

Discover data in Hadoop

Invoke Custom Analytics on Hadoop

Data management

tools

Approaches: •  Custom code •  Data Management tools suites •  Self-service analytical workflow development tools???

Trends: Expect MUCH more from data management tool vendors including generation of MapReduce code to clean and transform data

50

New Analytical Platforms Breed New Requirements – Cross Silo Analytics for Harder Business Questions

EDW

DW & marts

NoSQL DB e.g. graph DB

mart DW

Appliance

Advanced Analytics (structured data)

Analyse?

Advanced Analytics (multi-structured data)

Streaming data

RT Analytics

51

Cross Silo Analytics Option - Multi-Platform Analytical Workflows Need Analytics Embedded in ETL Processing

•  Support parsing and extract of data from multi-structured data sources •  Help automate analysis and consumption of data •  Move the data to the best platform to do the analytics •  Support analytical processing across multiple analytical platforms

Extract Parse Clean Transform Analyse Load Insights

Step 1

NoSQL DB e.g. graph DB EDW

Step 3 Step 2

52

Discussion Points §  Competitive positioning

•  Where does Alteryx fit in the analytical competitive landscape?

§  Product positioning •  Is Alteryx for Data Warehousing, Self-service BI or both?

§  Data Governance •  How does Alteryx facilitate support for data consistency and reuse

§  Analytical workloads •  What kinds of analytical workload is Alteryx providing solutions for? •  Big Data – How does Alteryx work with Big Data and NoSQL Platforms?

§  Performance •  How does Alteryx scale to handle concurrent users analysing and

consuming business insights •  How does Alteryx exploit underlying analytical platforms to get

performance with high volume multi-structured data?


The Briefing Room


The Briefing Room

Upcoming Topics

This month: Analytics March: Operational Intelligence

April: Intelligence

May: Integration www.insideanalysis.com


The Briefing Room

Thank You for Your

Attention

Certain images and/or photos on this page are the copyrighted property of 123RF Limited, their Contributors or Licensed Partners and are being used with permission under license. These images and/or photos may not be copied or downloaded without permission from 123RF Limited.

The New Normal: Predictive Power on the Front Lines

Technology

Transcript of The New Normal: Predictive Power on the Front Lines