ALPFA Leadership Summit 2013 Insider Look at Data Analytics

ALPFA Leadership
Summit 2013,
Philadelphia, PA -
An Insiders Look at
Data Analytics
19/23/2015 Copyright © 2013 www.DataMeans.com

• What is Big Data and Data Analytics ?
• Perceptions About Data Analytics
• Organizations Data Analytics Evolution and
Maturity Cycle
• Data Analytics as a business strategy
• Data Analytics Technology Considerations
Today’s Topics of
Discussion
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2

Big Data
• The Old is New Again
• Big data is not something new.
• In the 1990’s the popular term referring to Big Data was Data Warehousing.
We have had big data for a long time.
• What is new now is the rate of data grow, technology and capacity to
collect, process and analyze it.
• Another example of old becoming new is in the area of CQI (Continuous
Quality Improvement) originated in the 1930 at Bell labs, developed in to a
methodology by Edward Deming in 1950-70 and repackaged as Total
Quality Management (TQM)to fit different sectors in late 1980 to mid 1990
and the latest incarnation as Six-Sigma.
What is Data Analytics and Big Data
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Data Analytics Definitions
Wikipedia
• Data Analysis is a process of inspecting, cleaning, transforming, and modeling
data with the goal of discovering useful information, suggesting conclusions,
and supporting decision making.
• Analytics is the discovery and communication of meaningful patterns in data
Searchbusinessanalytics
• Big data analytics is the process of examining large amounts of data of a variety
of types (big data) to uncover hidden patterns, unknown correlations and other
useful information.
Techopedia
• Data analytics refers to qualitative and quantitative techniques and processes
used to enhance productivity and business gain
What is Data Analytics and Big Data
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• Vendors
• Software BI companies use the term Data Analytics to enhance the
value and outline certain functions and capabilities of their
products.
• Technology
• IT organizations relate to Data Analytics through the lens of
enterprise solutions, technology architecture, data management
optimization, business users requirements and data warehousing.
• Business Analytics
• Relate to Data Analytics through data analysis to provide business
insights, value and ongoing support to their business customers
• Executive Leaders
• Relate to Data Analytics through results and insights from data
analysis and reports that helps them gain a competitive edge,
predict, manage and strategize the business
Perceptions About Data Analytics
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Executive Leaders
Business
Analytics
Vendors
Technology
Lack of alignment on Data Analytics philosophy , roles and strategy
leads to duplication, increases cost and lack of fulfillment
Don’t get the all the
insights that they need
Don’t have accurate access to data,
resources or collaboration to answer
important business questions
Competing roles with Business
Analytics, lack of time and focus to
peel the onion for answers
Solution is not optimized or not well
spec. Not aligned to support clients
business grow. Happy and unhappy
customers
Small analytics convergence=Small Benefits
Lack of Analytics Vision Convergence has a Detrimental Effect

Lack of Analytics Vision Convergence Creates
• Unhealthy competition for resources and attention
• Competing visions about data assets management, technology
imperatives and transfer of knowledge
• Lack of unified vision of key business performance metrics
• Redundancy
• Sprout of data silos
• Struggle for control of data assets
• Hinders collaboration among teams
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Good Management of Data Analytics is Paramount to:
• Impact the Bottom line and sustain business grow
• Establish consistent versions of business Key Performance
Indicators KPIs
• Build synergies and efficiencies
• Reduce redundancy and cost
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Executive Leaders Business Organizations
Technology Organizations Technology Partners
Analytics Driving
Business

Data Analytics Evolution and Maturity Cycle
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Excellence on Data analytics is not about
• Getting state of the art technology to harness the value of big data
• Data warehousing with the best breed data base platform
• Data mining to uncover unknown relationships hidden in the data
• Contracting with the smartest software vendors, experts or analytics
companies
Excellence on Data Analytics is about
• Building the foundation to gain business insights using the available
data in an accurate and timely fashion
• Applying business knowledge and sound data analysis expertise to
answer specific business question
• Having the rigor and knowledge to systematically manage data assets
and transform insights into actionable results
• Continuous development of collaborative relationships with the
business, IT, Vendors and other partners

0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Lags Some Medium High Champion
Automation
Data & Process Efficiencies
Reporting
Advanced Analytics
Adhoc
Accuracy
Analytical Integration
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Know
+What…
+When….
Understand
+How….
Optimize Process
+Do it better +Grow
the market
+Increase sales
As we learn and
understand more,
there is no limit to
improve in making
better business
decisions

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Important Elements of a Data Analytics Organization
• Adequate # of Staff
• Analytical Skills (Stats, critical and outside the box thinking)
• Technical skills (data management, programming skills, problem solver)
• Availability of appropriate technology tools
• Business knowledge and Excellent communications Skills
• Efficient access to data
• Collaboration
• Clear vision of the future and ability to rally others around the vision

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Analytical Skills Data Accessibility
YES
NO
YES NO NO YES
NO
YES
Collaboration Technical Skills
Adequate # of
Staff
Cross
Functionality
Processes &
Standardization
in Placed
Business
Knowledge
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#1
•Data silos/Managed differently. Some not managed but stored
•Different business rules /Poor documentation
•Data is not normalized
•Manual creation of reports
•Kept in different formats(Excel, Access, SQL server, Oracle, DB2,
Cobol, txt, SAS….etc)
•No efficient data access
•No systematic data QC
#1
•Able to use properly statistical methods to answer a
business question
•Able to create business story from data results
•Draws business implications from data analysis and
reports
•Generates the urgency to react and act based on data
results
#2
•Sound process to standardized,
normalized, aggregate, combined,
validate and QC data at different
levels
•Creation of periodic reports must
be automated
•Centralized analytical data mart
#3
•Understands the business and
market trends
•Knowledge about products and
competitive landscape
•Understand sales and marketing
channel and sale force customer
interactions
#3
•No collaboration with IT partners
•No transfer of knowledge
•No sharing of best practice, tools and lessons
learned
•No responsive to the business partners and
continuous changes of requirements and questions
#4
•Appropriate data analysis and reporting technology platform
•Strong data management and analysis programming skills
•Likes to learn new things and welcomes challenges
•Excellent communications skills
•Team player
•Good management skills
#2
•Lack of
technical,
analytical or
managerial
staff.
•Projects under
staff
•Unable to
maintain
ongoing and
take on new
projects at the
same time
The 3 ChallengesThe 4 Achievements

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Optimum
Capabilities
Extremely
Valuable for the
Business
Stagnation/
Knowledge,
Technology and
Process
Dissemination
Middle
Capabilities
Adds Significant
Value to the
Business
Getting loss in the
corporate
organization
shuffle/Opportuni
ties to Optimize
Analytics
No
Capabilities
Provides Some
Value to the
Business
Becoming
Irrelevant/Signific
ant Opportunities
to Become a
Shining Star
Value
Risks/Opportunities

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Developing and maintaining talent is critical
for an analytics organization
• Have a pipeline for new talent
• Career path and career development for
existing talent
• Encourage Innovation and out of the box
thinking
• Build internal and external partnerships for
talent acquisition and development
Senior
MiddleJunior
Diverse
experience
levels are
important for
success

• Just as the quality of raw materials and process
are very important to produce good quality
goods that go to consumers, good quality data
and analytics are the essential inputs of
successful marketing, promotional and sales
campaigns that will grow the business bottom
line.
• Data Analytics must follow same good business
process practices that other disciplines follow
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Data Analytics as a Business Strategy

• Conduct a data sources audit
• What data is available
• When is it available
• Who owns it
• How it is used
• Where it is
• Eliminate data silos
• Reports Audit
• When, why and how
• Analytical Tools and skills audit
• Create analytics datamart to be used by Data Analytics
power users
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Getting the house in order

Rx Patient
Alignment
Calls
Activity
Demo
Promotion
Activity
Managed
Caret
Call
Plan
Market &
Products
Defs
Work hand in hand with
business users and IT
counterparts to ensure
the optimum solution
and process to integrate
data in support of
reporting, targeting and
analytics
Sandbox
Integrated
Data
Supports
•Innovation
•Call Plan
•Reporting
•Analytics
•Ad hoc
Drives Sales
Meet Targets
Call Plan
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Data Analytics as a
Business Strategy

Data Integration & Validation
Analytics &
Reporting
Rx & OTC
Data
Calls &
Samples
Alignment
Demographic
Promo &
Third Party
Call
Plan
Automated Data Process
Data Standardization,
Summarization & Validation
Analytical Data Creation
Targeting
Promotion
Response
Samples
Optimization
Segmentation
Customer Life
Time Value
Ad Hoc
Brand
Reviews
Marketing
Executive
Mangmnt
Field
Force
Support
Call Plan
The Data
The Data
The Processes
The AnalyticsThe Reports
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1 2 3 4 5 6
+Ideas
+Information
+Data
+Understand the
problem
+Set Goals
+Estimate
Opportunity
+Build Consensus
+Develop program
+Get support
+ Set work plan
+Evaluate
+Execute program
+Interim results
+Program adjusting
+Sales
+Productivity
Gains
+ Guidelines
Adherence
+Evaluate &
Measure
20
Inputs Prepare Execute Output EvaluateDevelop
The Promotional Event Process
Inputs Transformation Output Evaluation
Planning Execution Results
Project Cycle
Here is the CQI concept discuss at the beginning repackaged. The old become new!!
Helping to Answer Specific Business Questions

• Analytics Team should be able to play and dance with the data
at the same time without or with little preparation
• Classical
• Jazz, Rock, Pop and Rap
• Mambo, Salsa, Bachata and Merenge
• Tango, wayno, Candombe and Porro
• Any other music
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Analytics Team Orchestra or Dance group analogy
Answering Business Questions Requires Rigor and Flexibility

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• Diversity Metrics
Areas for key Performance Indicator (KPIs)
• Employees by Function and Area
• Promotions
• Training
• Complains
• Voluntary and Involuntary Terminations
• Support Operations
• Information Coverage
• Barrier Diagnosis
• Opportunity Identification
• Voluntary Bias Identification
• Streamline Reports
Example #1:
HR Analytics Strategic Imperatives
• Support Business Grow
– Increase Productivity
– Improve Global Market Opportunities
– Reduce Turnover
– Increase Legal Compliance
• Advanced Analytics
– Organization Assessment
– Change Management
– Geo and Area Analysis
– Staff Optimization and Simulation Models
– Churn Models
– ROI
– Total Quality Management
Data Integration, Standardization, Automation, Reporting & Analysis
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Demographics
Work Place
Outcomes
Employee
Attitudes
Organizational
&
Management
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Data Asset Types
HR Example

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Analyze Target
Track Report
Business
Grow
Maximizing Data
Assets Value
Demographics
Work Place
Outcomes
Employee
Attitudes
Organizational
&
Management
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HR Example

Business grow will be enhanced by
Diversity and inclusion initiatives.
A Diverse pool of professionals bring
different ways to embrace business
challenges
Data Assets
Key Performance
Indicators
KPIs Dashboard
Organizational
&
Management
Training
Terminations
Process/
Initiatives
Departments
Functions
Workplace
Outcomes
Promotions
Retention
Hires
Applicants
Pay and
Awards
Employee
Attitudes
Bias
Favoritism
Harassment
Inclusion
Job
Satisfaction
Demographics
Race
Disability
Sex
Age
Benchmarks
Business
Performance
Financial
Talent
Retention
Business Grow
&
Competitiveness
Minimized
Litigation Risk
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Reports & Analysis
Data Collection
Aligning with Business Strategy
Determine Needs &
Opportunities
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HR Example

Example #2:
Sales & Marketing Data Mart Strategic Imperatives
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• Reporting Business Performance
Key Performance Indicator Reports (KPIs)
– Customer Referrals
– Revenue (Net Sales, MC)
– Sales Force benchmarks
– Web/Portal Enrollment
• Support CRM/Portal Recruitment &
Promotional Offerings
– Customer Deciles
– Promotional & Messaging optimization
– New Customers
– Young customers
– Eco Digital Environment (Social Media)
• Support Multi Chanel Targeting
– Mailing Lists
– Email lists
– Conventions, Conferences..etc
• Advanced Analytics
– Segmentation
– Geo Sales and targeting Analysis
– Sales force sizing
– Promotion response
– Targeting campaign ROI
– Non personal promotion optimization
– Forecasting
Data Integration, Standardization, Automation, Reporting & Analysis
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Data Assets Types
Business
Performance
CRM/Customer
Relationship
Management
Recruitment Auxiliary
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Sales and Marketing Example

Data Assets
Analyze Target
Track Report
Business
Grow
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Business
Performance
CRM/Customer
Relationship
Management
Recruitment Auxiliary
Maximizing Data Assets Value
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Business grow will be driven by
Recruitment of customers into CRM
programs and measure by Key
Performance Indicators, KPIs
Data Mart Databases
•sales
•Distributor Sales
•Portal
Enrollment
•Samples
Key Performance
Indicators
KPIs Dashboard
CRM
Web Portal
Target Lists
Sales force
Institutional
Sales Force
Recruitment
Customer
Universe
Customer
Cross Selling
data
Customers
third party
data
Customer
Financial
Data
Acquisition
Lists
Other
Call Center
Subscriptions
data
Customer
satisfaction
Census
Business
Performance
Transactional
Sales Data
Customer
Referrals
Distributor Sales
samples
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Mailing Lists Campaigns
Reports & Analysis
Email Lists Campaigns
Aligning with Business Strategy
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Customer wants to
expand idea so it can
be used by more
people and with higher
level of details.
Data Sources
Efficient
Data
Processing &
Validation
Process
Final Data
work with
costumer to come
up and implement
the most efficient
and cost effective
solution for
customer needs
Dynamic &
efficient
process to
conduct data
analysis or
reporting
Organizations may reach a point
where their customers want more and
a technology solution should be
considered
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Data Analytics Technology Considerations

Customer is very
happy with the
business insights
your team has
provided and
your team ability
to deep dive and
help answer
important
business
question. He
wants to pass
this knowledge
to his entire
team
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9/23/2015
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•Dinner meetings
•Symposia
•Speaker training
•Teleconferences
•DTC
•Digital
•Multi Chanel Marketing
•Web casting
•Conferences
•Detailing and samples
•Journal advertisement
•Physician/Patient support programs
•Other
•Do you understand what you know?
•Do you know what you don’t know?
•How hard is to know and use what you know?
•What is the ROI of our
promotional dollars?
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Organization has become an analytical power house
33
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Requires an Enterprise Analytical Solutions Integration
34
Business
Intelligence
+
Data
Warehousing
+
Inventory
Management
+
Data Mining
+
Marketing
Optimization
+
Forecast
+
Marketing
Automation
+
Predictive
Modeling
+
Organizations work across
functional areas and build
synergies at the same time
Technical expertise
streamline data intensive
process and achieve
significant efficiencies
Continuous improvement
approach helps identify
opportunities , save time,
resources and reduce errors
Gain insight as to what, how,
where and when important
business factors are
changing.
Approach must be
systematic, manageable and
duplicative
Maximize and optimized the
value of their data

• Do not assume that technology is a solution in itself
• Organizations need to learn to walk before they can run
• They must develop internal expertise to complete, validate and
report analytical findings in their own.
• Be able to adjust to continuous changes and new questions from
their business customers.
• “By the way I forgot to tell you that…….”,
• “Your findings are very interesting can we look at……”
• “Your numbers do not make sense can you go back and check
that……”
• As part of your RFP process include a number of cases of study or
projects (you may modified the data), which you known the outcomes,
for your vendors to run them through their solution and for you to
compare the results
• Expect hick ups and bumps when implementing a technology solution
• Gain support from other groups such as IT to tap into their technical
expertise for assistance
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9/23/2015
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36
Successful Implementation = Successful QC by Analytics Team
•Functionality It does what it promises
•Data Quality Data is not created or destroyed without explanation. Understand,
Validate and document expected changes in data
•Customers are not lost or additional customers gain by the system
itself .
•Products do not get drop off by magic
•Transactions history is not changed
•Market Share, Sales….etc do not change
•Passes data audit
•Deliverables It delivers what it promises

Copyright © 2013 www.DataMeans.com 379/23/2015
Gartner: Big data will
help drive IT
spending to $3.8
trillion in 2014
Consider
multiple
vendors and
bring them in
house to
show case
their product
with your
case of
studies data
Gartner Magic Quadrant mayo 2014 de Software para Multichannel Campaign
Management

Gartner: Big data will help drive IT spending to $3.8 trillion in 2014
#1
Include in your pool of
vendor small vendors.
They may provide a good
dollar value proposition
and more innovation.
#2
Do your home work
before selecting vendors
to invite in your RFP.
#3
Be willing to spend
significant amount of
time in the selection
and negotiation
process
Magic Quadrant for Advanced Analytics Platforms

Do not negotiate
price until you had a
chance to evaluate
the product with your
data. If they want
your business they
will be flexible

Model Developed by TDWI
Gartner’s Market Analysis
According to Gartner’s report, the Big 5 vendors (SAP,
Oracle, SAS, IBM and Microsoft) continue to dominate,
owning 68 percent of the market share. In the BI
platform and CPM suite segments, they hold close to
two-thirds market share, while in pure statistics and
analytic applications, SAS dominates the market.
source: Business Analytics 3.0 blog
http://practicalanalytics.wordpress.com/2011/04/24/gartner-says-bi-and-analytics-a-10-5-bln-market/
Other Interesting Links about Gartner
• Customer experience trumps technical excellence – Gartner BI
reports
• Gartner splits the 2014 Business Intelligence Magic Quadrant in
two.

Contact Info
Alejandro Jaramillo
Tel:732-371-9512
Email:Alexj@datameans.com
Thank You

ALPFA Leadership Summit 2013 Insider Look at Data Analytics

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