2013 alpfa leadership submit, data analytics in practice

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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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 DefinitionsWikipedia

• 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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11

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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Data Analytics Evolution and Maturity Cycle#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/Significant Opportunities

to Become a Shining Star

Value

Risks/Opportunities

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Data Analytics Evolution and Maturity CycleDeveloping 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

DemoPromotion

ActivityManaged

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 Integration & Validation

Analytics & Reporting

Rx & OTCData

Calls & Samples

Alignment

Demographic

Promo & Third Party

Call Plan

Automated Data Process

Data Standardization, Summarization & Validation

Analytical Data Creation

TargetingPromotion Response

Samples Optimization

SegmentationCustomer Life

Time ValueAd 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 MetricsAreas 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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DemographicsWork Place Outcomes

Employee Attitudes

Organizational &

Management


Data Asset Types

Data Analytics as a Business StrategyHR Example

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Analyze Target

Track Report

Business Grow

Maximizing Data Assets Value

DemographicsWork Place Outcomes

Employee Attitudes

Organizational &

Management



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 & AnalysisData Collection

Aligning with Business Strategy

Determine Needs & Opportunities



Example #2:Sales & Marketing Data Mart Strategic Imperatives

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• Reporting Business PerformanceKey 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



Data Assets Types


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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CRM/Customer Relationship Management

Recruitment Auxiliary

Maximizing Data Assets Value




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


Transactional Sales Data

Customer Referrals

Distributor Sales

samples

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Mailing Lists Campaigns

Reports & AnalysisEmail Lists Campaigns

Aligning with Business Strategy




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

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•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

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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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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

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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


#1Include in your pool of vendor small vendors. They may provide a good dollar value proposition and more innovation.

#2Do your home work before selecting vendors to invite in your RFP.

#3Be 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

http://www.informationweek.com/big-data/big-data-analytics/gartner-bi-magic-quadrant-winners-and-losers/d/d-id/1114013?image_number=1

http://www.informationweek.com/big-data/big-data-analytics/gartner-bi-magic-quadrant-winners-and-losers/d/d-id/1114013?image_number=1


Model Developed by TDWI

Gartner’s Market AnalysisAccording 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.

https://practicalanalytics.files.wordpress.com/2011/04/gartner2012.png

https://practicalanalytics.files.wordpress.com/2011/04/gartner2012.png

http://practicalanalytics.wordpress.com/2011/04/24/gartner-says-bi-and-analytics-a-10-5-bln-market/

http://diginomica.com/2013/09/25/user-experience-trumps-technical-excellence-gartner-bi-reports/

http://www.applieddatalabs.com/content/gartner-splits-2014-business-intelligence-magic-quadrant-two

Contact Info


Alejandro JaramilloTel:732-371-9512Email:[email protected]

Thank You

tel:732-371-9512

2013 alpfa leadership submit, data analytics in practice

Data & Analytics