Self Service Outline Updated 8 js

Self Service Business
Analytics
Andrew Doan
Christopher Ferris
Louay Rifai
Julia Smith

2
Agenda
1. Scope / Definition of Self Service Analytics
2. Rationale / Value
3. Market Overview
4. Demos
5. Risk Management
6. Purchase Considerations
7. Overall Evaluation of Self Service Analytics
SELF SERVICE BUSINESS ANALYTICS

USER DEFINITION
SELF SERVICE BUSINESS ANALYTICS 3

Investors
• Enterprise-Wide
Advocates
Consumers Practitioners Directors
• Mid-Level
Advocates
• Ordinary Decision
Makers
• Analytics-Based
Decision Makers
• Business Analysts
• Knowledge
Workers
• Analytics Power
Users
• Business Quants
• Ordinary Managers
of Analytics
• On-Topic Business
Analytics Leaders
• Expert Leaders
SCOPE – Who is the target user?
The “Self” in Self-Serve Tools can refer to a number of different analytics stakeholders.
Analytics Resources. (Source: A Practitioner’s Guide to Business Analytics – Bartlett)

Self service tools are those that enable people to gather information from multiple sources, analyze it
and share it with others, without having to know the technical protocols required to access the data.
Data Modellers
Data Architects
Coders

Which of my contracts put
me at most risk?
How will my facilities be
impacted by a natural
disaster?
How can I predict and
prepare for crowd surges?
Where are my trucks right
now?
If I divest a business, how will
my IT infrastructure be
impacted?
Where are my system
intrusion attempts most
likely to come from?
Which of my people are at
greatest risk of leaving the
company?
What’s the best asset
amortization strategy?
What are the best
opportunities to select
amongst capital funding
requests?
How might we be
impacted by the
bankruptcy of our largest
customer?
What are the most natural
customer segments to
consider?
What are the key drivers of
product consumption?
How does market
perception differ by
regions?
What do my customers
think about us right now?
Analytics consumers can come from any part of the business to answer their specific questions.
Sample User Questions
Marketing Finance IT Operations

A critical starting point is to be clear on the scope of activities to be delivered through self service.
SCOPE – Activities
Levels of Intelligence (Source: Getting Started with Business Analytics – Hardoon, Shmueli)

Users need to be able to execute many different types of analysis, depending on the requirements.
Descriptive Diagnostic Predictive
Report-based
presentation
Graphical presentation
of data
Simple statistical analysis
Dimensional
presentation of data
Dimensional comparison
of data
Mathematically based
visualization
Data Mining
Forecast
Predictive Analytic
Model
Adaptive or Learning
Analytic Model
Simulation /
Optimization
SCOPE – Activities

RATIONALE/VALUE

• Increased awareness of the power of
analytics amongst business users;
• New technology available that makes
this possible:
o In-Memory processing
o Cloud that takes data out of the
hands of IT
o Software as a service
o Volumes – more than can be dealt
with through excel
• Understanding of the talent shortage
• New Tools that make it seem easy
WHY? WHY, NOW?
Rationale for Self-Service Tools
WHY?
 Better Business Decisions
 Address the skills shortage
 Reduce demands on IT
WHY NOW?
By 2017, Most Business Users and Analysts in Organizations Will Have Access to Self-Service Tools
to Prepare Data for Analysis (Gartner).

*The Data Warehousing Initiative (TDWI) 2014
1
.
While the Big Data world grows quickly, the skills required to design, manage and utilize the new tools are
not growing as fast*
Rationale – Why is this needed?
According to McKinsey & Company, the United
States alone is likely to “face a shortage of 140,000
to 190,000 people with deep analytical skills as
well as 1.5 million managers and analysts with the
know-how to use the analysis of big data to make
effective decisions” by 2018.)

.
0
5
10
15
20
25
30
35
40
0% 1-10% 11-20% 21-30% 31-40% 41 - 50% 51-60% 61-70% 71-80% 81-90% 91-100%
Companies who have implemented self-service tools report an average reduction of
IT report requests of 37%
Logi Analytics Survey of IT executives who report a reduction in effort
Rationale – Why is this needed?

MARKETPLACE

 Can be desktop/enterprise, cloud/mobile
 Can include data preparation as well as data analytics
 Can include Machine learning, predictive modeling, data mining, text mining, data visualization, R/Python
integration
 Sometimes offer wizard-driven, guided, visual tools for non-technical users
 Context-sensitive querying (google for BI/BA)
 Increasingly offer pre-built models for functional areas (customer churn, financial risk, social network/media),
tasks (PA, optimization, scoring), or industries (healthcare, financial, insurance, retail)
 Allow users to develop, publish, share models, collaborate
 Can (and should) include Governance, security
The range of these tools can vary significantly..
SCOPE – Functionality

The “bar” where you draw the line is often somewhat fuzzy.
MARKET OVERVIEW – Key Players
Analytics Self Service Tools
Not Analytical:
Not Self-Serve:
• Powerpoint
• Excel
• Python
• R
• SPSS
• Tableau
Not: Analytics Self Service Tools
• Qlikview

Gartner Hurwitz and Associates IDC
Predictive Analytics
Today
• Alteryx
• Angoss
• IBM
• Megaputer
• Pegasystems
• Pitney Bowes
• Predixion
• Rapid Miner
• Revolution Analytics
• SAP
• SAS
• Tibco
• Actuate
• Alpine Data
Labs
• Alteryx
• Angoss
• FICO
• IBM
• InfoCentricity
• Knime
• Megaputer
• Microsoft
• Oracle
• RapidMiner
• Revolution Analytics
We began by looking at who the market identified as key players in the Advanced Analytics tool space.
• SAS
• StatSoft
• SAP
• Angoss
• IBM
• FICO
• H20
• Knime
• KXEN
• Oracle
• Pervasive
• Portrait
• Predixion
• RapidMiner
• Data Science
Studio
• Tibco
• Statistica
• TiMi
Suite
• Salford
• SAP
• SAS
• Actuate
• Alpine Data
• Alteryx
• Prognoz
• Blue Yonder
• Dell StatSoft
• Fuzzy Logic
• Wolfram
• Zementis
• Mathworks
• Microsoft
• Minitab
• Wolfram Zementis
• Opera
• Oracle
• Angoss
• Rapid
• Mu Sigma
• SAP
• SAS
• Strata
• Tibco
• FICO
• IBM

Against the top analytics providers, we then assessed to what degree the product enabled end-users
to run analytics without IT or data science support.
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
LM HM
Degree of SELF SERVICEHL L
L M L
L L L
L = Low
M = Medium
H = High
H M
15SELF SERVICE BUSINESS ANALYTICS
Degree of Self Service
15

Self Service vs. Traditional
Technology
Governance
Self Service
IT Agility
Better Business
Decisions
“By 2017, Most Business Users and Analysts in Organizations Will
Have Access to Self-Service Tools to Prepare Data for Analysis”
The trusted data sources (finance, etc.) available
through the enterprise data warehouse are a key
part of the data analysis done in the “self-service”
tools.

Predixion
• Serving the “last mile of analytics”
• Received funding from Accenture,
others as part of their venture
funding.
• Can access custom models
developed by a company’s data
scientists or leverage some of the
pre-defined wizards (ex. Patient Re-
Admission)
• Delivers through an Excel platform
for ease of use.
• Has portable models (ex. If a model
is designed in one setting, it can be
re-used by others. )
IBM Watson
• Cloud based service
• Enables end-users to:
• Find and improve datasets
• Understand business data
• Tell a story through
visualization
• Does the math for predictive
analytics
• Organizes queries by role:
marketing manager, HR professional,
Finance
• Offers a “Freemium” model to
enable people to experiment with
the software.
SAS
•• Visual Analytics Explorer tool enables a
user to:
• Explore data and discover new
information
• Visualize data to find
relationships, trends etc.
• Run correlations, regressions
(linear, quadratic, cubic, Pspline,
best fit), forecasting and
summary statistics
• Targeted towards end-users asking
questions on the fly:
• Example: How many customers,
from what segments which
geographical regions respond?
What do the outliers have in
common?
Offerings and market approach vary significantly between major players.

DataRPM
• Allows business users to ask
questions in natural language.
• Tool analyzes data and suggests
interesting correlations.
• Delivers statistical analysis on high
dimensional data, runs supervised
and unsupervised machine learning
algorithms and conducts hypothesis
test to evaluate significance of
identified patterns.
• Offered as on-premise or SaaS.
BeyondCore
• Automated analytics
• Highlights connections across
complex data sets without any
human interaction.
• Once the software identifies
something significant, an analyst can
dig in and clean the underlying data,
or manual explore it on their own.
• Addresses the issue of not knowing
what the question is to ask.
• Provides detailed answers and
education on analytics.
DataMeer
•• Delivers through an excel like front-end
on top of Hadoop
• Provides 255 point and click analytic
functions
• Provides automated delivery of four
advanced machine learning techniques
• Clustering
• Decision Trees
• Column Dependencies
• Recommendations
• Key selling point is time to insights and
reduced need for infrastructure
upgrades
There is also a growing number of niche players.

Demos: Predixion and IBM Watson

KEY RISK AREAS

Remember the target user?
Self service tools are those that enable people to gather information from multiple sources, analyze it
and share it with others, without having to know the technical protocols required to access the data.
Data Modellers
Data Architects
Coders

RISKS – Decentralization
Siloed analytics structures are common in every organization. This type of structure functions better for
tracking/reporting than advanced analytics [Bartlett]
Risks of Decentralization:
• Information is filtered as it travels up through the organization (only good news reported)
• No insulation from local political interests/conflicts of interest
• Information is held within business units and is not shared
• Weak connectivity and collaboration between analysts
• No separate clearinghouse for solving more advanced analytics
• No centralized place for reviewing important analyses
• Potential that resources (people) are not invested wisely
• Potential for overworked or struggling decision makers (lacking skills)

0%
10%
20%
30%
40%
50%
60%
70%
Business User
Skills
Lack of data
quality, control
and governance
Lack of training for
business users
Lack of budget to
implement
Lack of
management
backing
Lack of
appropriate tools
Lack of belief in
self service
Lack of IT skills to
support
Operational and
security risks
There are a number of risks to be managed when considering a self-service analytics program.
25
Reasons Management Has Avoided Self Service
Potential inhibitors to Self-Service*
*Source: TDWI

RISKS – The Analytics Black Box [CRISP-DM]

RISKS – Qualifications and Skills
“We have observed that the cost of overtraining is usually less than the cost of under-training.” [Bartlett]
Users/Managers may not be well versed in analytical tools, statistical techniques or decision-making
(i.e. thinking analytically):
• How to use the tool
• Underlying theories of statistical techniques – uses/limitations/risks

Qualifications and Skills – Statistical Diagnosis
Statistical diagnostics is an important area of analytics that really requires an understanding of statistical theory.
• Flipping a coin – your pattern is H H T H H T H H T => What will the software tell you?
• Overfitting is another problem
• Tests – Data Splitting, Re-sampling, Stress Testing
• Tests of Assumptions – Collinearity, Residual Plots, Goodness of Fit
5 Benefits of Statistical Diagnostics:
1) Detecting mistakes or weaknesses
2) Measuring the accuracy of the analysis
3) Measuring the reliability of the analysis
4) Providing insight into interpreting the results
5) Providing insights into potential improved solutions

RISKS – Qualifications and Skills
“We have observed that the cost of overtraining is usually less than the cost of under-training.” [Bartlett]
Users/Managers may not be well versed in analytical tools, statistical techniques or decision-making
(i.e. thinking analytically):
• How to use the tool
• Underlying theories of statistical techniques – uses/limitations/risks
• Limitations of the tool or supporting data
• Be able to ask the right question to get the right answer
• Interpreting results (introducing bias)
• Proper handling of data

Data integrity is a big issue – and one that particularly concerns IT personnel.
27%
28%
31%
37%
37%
36%
34%
29%
29%
20%
24%
20%
16%
16%
12%
14%
0% 20% 40% 60% 80% 100% 120%
Data can be sent to anyone who is authorized
or not.
No controlled way of sharing analysis, other
than email
Users can purposefully or mistakenly change
the data
Multiple users have their own copy of data, no
"single version of the truth"
Risks Associated with Self Service
Major Problem Minor Problem Slight Probem Not a Problem
RISKS – User Uploaded Data
Source: Logi Analytics Survey 2014)

Strong data governance becomes more important than ever.
Sample Reference Architecture (Source: Sunil Soares, IBM)
At a minimum, the following items
would have to be updated to
accommodate a range of new (and
likely inexperienced) users:
• Information Policy Management
• Master Data Management
• Data Security and Privacy (Cloud)
RISKS – Data Governance

SAMPLE PRIVACY RISKS:
1. Risks of re-identifying data that was initially de-
identified
2. Possible deduction of private information from
publicly available information
3. Risk of data breach
4. Insider Threats
5. Personal data being sold and shared
6. Risk of personal data and analytical insights being
used for unintended purposes
7. Location tracking
8. Losing customer trust unintentionally
9. Violating “Chinese walls”
10. Breaking laws or compliance issues unintentionally
Managing privacy risks is even more important when a broader group of stakeholders are accessing the data.
Most Restricted
Restricted
Some Restrictions
Minimal Restrictions
Effectively No Restrictions
No Legislation or Information
Privacy and Data Security By Country (Forrester 2013)
RISKS – Privacy Issues
EXISTING/EMERGING/CHANGING PRIVACY REGULATIONS
1. Canada: Privacy Act: Personal Information Protection and Electronic
Documents Act (PIPEDA); Canada Anti-Spam Law (CASL)
2. Ontario: Freedom of Information and Protection of Privacy Act (FIPPA)
3. US: HIPPA; PHIPA
4. Europe: General Data Protection Regulation (GDPR); EU Data Protection
Directive
5. Industry Specific: PCI DSS

Given that non-technical individuals are the key users of Self-Service analytics, they will likely lack the
technical understanding of how to accurately merge structured data with the likes of non-structured data ,
even with easy to use software and functions.
RISKS – Improper Education/User Skill Level
Potential Issues
• Faulty Findings
• Poor Decision Making
• Lack of Technical Skills
Risk Mitigation
• Develop a basic level of competency
• Technical training of business users
• Develop a certification plan
• Maintain a Centre of Excellence

PURCHASE CONSIDERATIONS

35
Purchase Considerations
•Will align to the key questions being asked - will support decision making
•Support the right FREQUENCY of Decisions (one-off, or high-volume)
•REPEATABILITY ( Low repeatability requires more exploratory nature, high, needs business more business rules)
•LATENCY how quickly does the decision have to be made?
•COMPLEXITY – low complexity can rely on factory made approach
Meets User Needs
•Categorization
•Clustering
•Link Analysis
•Pattern Discovery
•Visualization
Breadth of Functions
•Fast learning curve
Ease of Use
Product selection criteria should always begin with the delivery against user requirements.

• Delivery Options – Cloud? SaaS?
• Ability to rollout in phases (test and learn)
• Timeframe for delivery
Delivery Options:
• Sufficient expertise in the product available in market?
• Training, Consulting support available from provider?
Product Support
• Ability to expand enterprise-wide (need to future-proof)
Scalability
• Ability to share licenses for trial purposes before purchase
Licensing
Efforts and options associated with deployment need to be considered.

• Fits in with the existing architecture
Integration with Existing Systems
• Needs to be able to do ETL
Ability to manipulate the data
• Role security
• Access Restrictions
• Explorations
Data Security
• From CSV, Excel Files to SPSS, SAS
• Structured to Unstructured
Scope of data feed it ingests
The technical architecture also has to be a consideration, but shouldn’t be the biggest driver.

Traditional Technical Environment
Other Traditional Technical
Systems include:
• Mainframe
• Oracle, SQL, DB2, Sybase
& Other Databases

39
Business users
do not know
the all the data
they need,
until they start
playing with
the data, so
need more
flexibility in
data
processing for
the iterative
modelling
process
Purchase Considerations – Technical Architecture

Enhanced Technical Environment
Built in connectors that allow you to
connect with existing platforms through:
• Import tools
• OBDC connections
• Integrate with existing load jobs

Types of Data Supported
• Text & .CSV files
• Web Server Logs
• Facebook, Twitter, & other Social
Media data
• JSON
• XML
• Data from HBase, Cassandra, MySQL,
Oracle, DB2, etc.
• Sequence Files

Self Service Business Analytic
Tools
Cost and Pricing
Predixion • Annual pricing of single user $999.00 ( for a data scientist)
• Professional Edition (for a team) - $25,000
• End-Users Edition – starts at $100,000
DataRPM • Pricing dependent on data consumed
DataMeer • Yearly subscription model:
• Individual - $300
• Workgroup - $20,000
SAS Analytics Pro • Yearly subscription model
• Individual - $9,300 (Sector Dependent)
• Group – Discounts on bulk sale
• Subsequent Years: 28% of the first year price ($2,604)
IBM Watson • Freemium based model (100,000 rows, 50 columns (500MB) storage))
• Subscription based model (1,000,000 rows, 256 columns (2GB) Storage) $30
Variety of Cost Models – whatever works best for your needs
Purchasing Considerations - Pricing

SUMMARY RECOMMENDATIONS

WHY? WHY, NOW?PROS CONS
Pros and Cons
• Could save time / redirect effort of the IT
team who don’t have to do reports
• Helps with skills shortage of data analysts
• Helps drive a interest in analytics for the
business
• Market is still evolving – may want to wait for a
while to see who the natural leaders are
• Risks of untrained/unskilled users
• Risks of data privacy breaches
• No reason to think this will be better used than
existing CRM/BI tools
Bottom Line
Conclusions:
To be avoided
at all costs
Should be adopted
immediately
J/C

• Be clear on who the end-users are;
• Be clear on required functionality;
• Future-Proof for the long-term view;
• Ensure that governance and architecture in place
• Be realistic about the organization’s appetite:
• Open to new technologies / change management
• Tolerance to learning curve of new technology & delayed results;
• Be realistic to employee considerations:
• Ability/ Interest in new skills
• Technical competency
Key Success Factors
If you do go ahead…

QUESTIONS

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