understanding business data analytics

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Table of Contents• Analytical Challenges• Imperatives• Road Map• Functions• Data Integration and Validation• Improvement Cycles

Understanding Business Data Analytics

Prepared by Alejandro Jaramillo Copyright © 2013 www.DataMeans.com

2Prepared by Alejandro Jaramillo Copyright © 2013

www.DataMeans.com

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

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12/15/2016Prepared by Alejandro Jaramillo Copyright © 2013

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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 organizational grid lock

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

5

Data silos

Hard to get data

Long turn around

times and high

cost

Unable to meet

business needs

on time

Too many cooks

cooking the data

Efficient

Access to

the data

Quick turn

around on

data analysis

Focus on

Answering

business

questions vs

getting and

fulfilling

requirements

and specs

Advanced

Analytics to

Drive

Business

Grow

Build

Efficiencies

and reduced

waste

Build

partnerships

with IT and

business units

Excellent

Business,

technical and

data analytics

skills

Operationalized

analytical

findings

• Too much emphasis on company data platform and adherence to use of IT tools, policies and procedures

• Too much reliance on specs and requirements

• If it is not in IT scope of work it won’t happen

• Every variation of work is associated with additional cost and approvals

Analytics organizations are structured:• For quick response to the business• To get the job done independently of tools

or platform• To adapt to changing business needs• To address a problem from a business

perspective ©2015 Data Means


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


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


Executive Leaders Business Organizations

Technology Organizations Technology Partners

Analytics Driving

Business

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© 2013 www.DataMeans.com

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Drive strategic

outcomes,

business insights

and answer

business

questions

Balance analysis

with information

needs to find

opportunities

Develop

sustainable and

transferable

analytical

knowledge

Define

performance

metrics, drive

change &

synergies

Manage change

to increase

efficiencies and

profitability

Manage, recruit &

staff Analytical

organizations.

Develop technical

analytical

capabilities.

Establish a single

representation of

business true

reality.

Integrate data

from multiple

Sources.


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Building & Management

Analytics Practice

Promotion Response

Models/Predictive Models

Customer Segmentation/Data

Analysis/ROI

Study Design/Pre and Post

Change Management Analytics

Sales Force Effectiveness/Field

Force Expansion/Call Plan

Custom Turnkey Analytical

Solutions

Multi Channel Marketing

Analytical Support

Data Integration, Data Marts,

Automation & Validation

Reporting Solutions / Reports

Automation & Rationalization

Digital Analytics

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TV & Journal

Ads

Email & DM

A 360 Degree view of customers is critical for business grow

Sales

Digital

Impressions

Sales Force

Activity

Coupons &

Vouchers

Costumer Surveys

Costumer Master

File

POS

Distributors

Financial & Cost

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• Customer satisfaction• Life Time Value• Segmentation• Circle of influences• Demographics• Attributes

• Email & DM Campaigns• Engagement Programs• Digital Impressions• Coupons & Vouchers• Loyalty Programs

The

Cu

sto

mer

• Sales Force Effectiveness• Call Planning• Incentive Compensation• Territory Alignment• Sampling• Lunch & Learn

Sales $

Explore

Customer

data to

develop

new

insights

Engage

with the

right

message in

the right

channel

Increase Sales

& Efficiencies

Reduce Cost

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Analyze Target

Track Report

Business

Grow

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Business

Performance

CRM/Customer

Relationship

Management

Recruitment Auxiliary

Business Analytics Support• Data Mining• Predictive Modeling• Decision Support Analysis & Reporting

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Client has a data analysis, reporting or processing critical need or idea that can not be met through current systems or resources

Data Sources

Efficient Data

Processing &

ValidationProcess

Final Data

work with client to come up and implement the most efficient and cost effective solution for clients needs

Dynamic & efficient process to conduct data analysis or reporting

Analytical Functions Reporting

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Defining change objective◦ Reduce Cost

◦ Improve Profitability

◦ Increase Efficiencies

Establish a quantifiable baseline

Develop a change process

Implement change

Measure change Impact

Recalibrate process

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Objective

Baseline

Metrics

Implement

Change

Measure

Impact

Recalibrate

Process

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18

Segmentation

Response Models

Sizing

Expansion

KPIs and Dashboard Reporting

Incentive Compensation

Geo Alignment

Effectiveness Measurement

Call Plan design and execution

Test & Control Geo tests

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0

20

40

60

80

100

120

140

160

1 2 3 4 5 6 7 8 9 10

Avg Sales

Calls Activity


1 2 3 4 5 6

Ideas

Information

Data

Understand

the

Problem

Set Goals

Estimate

Opportunity

Build

Consensus

Develop

Program

Get Support

Form Team

Set Work Plan

And

Milestones

Develop

Evaluation

Methodology

Run

Program

Review

Interim

Results

Make

Program

Adjustments

NRx Sales

Productivity

Gains

Adherence

Evaluate

&

Measure

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Inputs Prepare Execute Output EvaluateDevelop

The Promotional Event Process

Inputs Transformation Output Evaluation

Planning Execution Results

Project Cycle

Analytics Functions Promotion Response

©2015 Data Means


20

Population Of Interest

High Value

Targets

No

TargetedTargeted

Low Value

Targets

TargetedNo

Targeted

Targeted Shift targeting to Valuable Targets

• Optimized campaigns by finding the most valuable customers

• Redesigning targeting strategy based on data

• Measuring the impact of campaign using appropriate statistical methodology

• Make recommendations

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Repoder

Groups

Score

Range

#

Subscriber

# Cummulative

Subscriber

#

Responders

# Cumulative

Responders

Cumm %

Subscriber

Cumm %

Responders

1 510-806 5,255 5,255 3,000 3000 10% 22%

2 806-870 4,940 10,195 2,500 5,500 19% 41%

3 870-905 4,519 14,715 2,400 7,900 28% 59%

4 905-928 3,731 18,446 2,000 9,900 35% 74%

5 928-945 3,206 21,651 1,000 10,900 41% 82%

6 945-957 2,680 24,332 776 11,676 46% 87%

7 957-966 2,628 26,959 400 12,076 51% 90%

8 966-973 2,522 29,482 300 12,376 56% 93%

9 973-978 2,417 31,899 200 12,576 61% 94%

10 978-981 2,050 33,949 100 12,676 65% 95%

11 981-985 1,944 35,893 80 12,756 68% 96%

12 985-987 1,944 37,837 90 12,846 72% 96%

13 987-988 1,944 39,782 100 12,946 76% 97%

14 988-990 1,944 41,726 90 13,036 79% 98%

15 990-991 1,944 43,671 80 13,116 83% 98%

16 991-992 1,892 45,563 70 13,186 87% 99%

17 992-993 1,839 47,402 60 13,246 90% 99%

18 993-994 1,787 49,189 50 13,296 94% 100%

19 994-995 1,734 50,923 30 13,326 97% 100%

20 995+ 1,629 52,552 22 13,348 100% 100%

Total 52,552 13,348

Score models are used to predict the likely hood that a customer will respond to an offering or event.The score produced by the model is used to rank customers.The lower the score the higher the likelihood to respond

10%

19%

28%35%

41%46%

51%56%

61%65%

68%72%

76%79%

83%87%

90%94%

97%100%

22%

41%

59%

74%82%

87% 90% 93% 94% 95% 96% 96% 97% 98% 98% 99% 99% 100%100%100%

0%

20%

40%

60%

80%

100%

120%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Score Targeting strategy

Cumm % Subscriber Cumm % Responders

By targeting 35% of the subscribers we capture 75% of the responders

With scoring model client will be reaching about a more profitable groups of customers at a lower cost

21©2015 Data Means


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Business

Intelligence+

Data

Warehousing

+

Inventory

Management

+

Data

Mining

+Marketing

Optimization

+

Forecast

+

Marketing

Automation

+

Predictive

Modeling

+

Analytical Evolution

©2015 Data Means


Data Integration & Validation

Analytics &

Reporting

Rx Data

Calls & Samples

Alignment

Demographic

Promo & Third Party

Call Plan

Automated Data Process

Data Standardization

DataMart

TargetingPromotion

Response

Samples

Optimization

SegmentationCustomer Life

Time ValueAd Hoc

Brand

Reviews

Marketin

g

Executiv

e

Manage

ment

Field

Force

Support

Call Plan

The Data

The Data

The Processes

The AnalyticsThe Reports

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CurrentDatabase

NewDatabase

Both files Current and new

matched

It is only inthe currentdatabase

It is only inthe new database

Data Migration MakingSure that your Data is Right

run freqs on matching variables

List and compare a few raw records form bad files to get an idea of the source of mismatches

For large data warehouses migration validating the data is a daunting process

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Data Integration & Validation

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Data Validation Process

Develop process, for

series of files, in

anticipation of file

delivery.

A batch of

files to be

compared

is

delivered

Run QC

Programs on

the batch

files

Assemble

report on

batch files

(concurrent

w/ run)

QC Programming

Review/ annotate

FAIL

Investigate /

fix action

items

If files are close

user runs reports

with new file and

compares results

Pass

log as

file done

26©2015 Data Means



Excellence on Data analytics is not about

• Getting state of the art technology to harness the value of big data (Hadoop, Phyton, SAS, R…etc…)

• 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

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The Big Picture

Goals & Resources

How & When

Improve

Improve

•Integration•New Products Launch•Field Force Restructuring•Hiring Freeze•Reorganization•Recruitment

•Documented•Validated•Efficient•On Time•Within Budget•Flexible

Improve•Find•Screen•Recruit•Present•Engaged

Resources Needs

©2015 Data Means



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


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/Opportun

ities to Optimize

Analytics

No

Capabilities

Provides Some

Value to the

Business

Becoming

Irrelevant/Signific

ant Opportunities

to Become a

Shining Star

Value

RisksOpportunities


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

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Know

+What…

+When….

Understand

+How….

Optimize Process

+Do it better

+Grow the

market

+Increase sales

Organization’s Analytical Evolution

If organization knows and understands, there is no limit to improve in making better business decisions

©2015 Data Means


Alejandro Jaramillo

732-371-9512

[email protected]

37

understanding business data analytics

Data & Analytics