ba presentation - revenue generation through data management … · • ensure organization-wide...

REVENUE GENERATION THROUGH DATA MANAGEMENT AND ANALYTICSBob TinglestadConsulting [email protected]

AGENDA

• Business Analytics Overview• Business Intelligence and Data Integration• Budgeting and “What if” Analysis• Predictive Analytics

2

PILLARS OF ROCKEFELLER HABITS

•Priorities•Data•Rhythm

3

WE ARE SURROUNDED BY DATA

Within that data there are patterns.

Can you find those patterns?

4

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Variety of Information 80% of new data is unstructured

Volume of Digital Data Digital content doubles every 18 months

Velocity of Decision Making

THE 3 V’S = “ANALYTICS” ELIXIR

In 2001, there were 60 million transistors for every human on the planet ... in 2010 there were 1 billion transistors per human

In 2005 there were 1.3 billion RFID tags in circulation…last year there were 33 billion RFID tags and growing more than 100% each year.

Worldwide mobile telephone subscriptions reached 7 billion in 2014 (95.5% of world population).

THE POWER OF BUSINESS ANALYTICS

Relevant Information

Actionable Insights

SmarterDecisions

BetterOutcomes

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WHERE ARE ORGANIZATIONS ON THE BUSINESS ANALYTICS JOURNEY?

n = 5748 organizations

STEP 1: STEP 2:

BuilderSTEP 4:

MasterSTEP 3:

Leader

Manual, slow, error prone,cumbersome, fragmented

Data quality concerns

Automated, instant, accurate,seamless, converged

Data governance is in place

Novice

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NONPROFIT ORGANIZATIONS MANAGE AN ARRAY OF ACTIVITIES

Donor Relationship Management

Event Management

Campaign Management

Mail

Email

Phone

Social Media Pledges & DonationsProcessing

Financial Management

Donor Receipts & Acknowledgements

Premiums & Fulfillment

Grant Management

Membership Management

Donor ScreeningLarge Donor & Corporate Giving Management

Volunteer Management

ProgramManagement

Reporting and Analysis

HOW DOES THIS APPLY TO YOUR ORGANIZATION?

• How many of these activities don’t apply to your organization?

• How many systems are involved?

• Can you trust your data?• Can you access the information

you need, when you need it to make decisions?

• How many businesses do you run?

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Where to Find Opportunities in Non Profit• Research Best Donor Prospects• Performance by entity, donor types, range of gift, etc.• Predict and optimize campaign responses• Long and short range financial plan• Forecasts with membership and giving plans• Grant reporting• Align development, program and finance• “What if” modeling• Scorecard all priorities – grant management, campaign and event

management, membership management, pledges and giving

AGENDA

• Business Analytics Overview

• Business Intelligence and Data Integration• Budgeting and “What if” Analysis• Predictive Analytics

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ANALYTICS AT WORK

Deg

ree

of C

ompl

exity

Standard Reporting

Ad hoc Reporting

Query/drill Down

Alerts

Forecasting

Simulation

Predictive modeling

Optimization

What exactly is the problem?

What will happen next if ?

What could happen … ?

What if these trends continue?

What actions are needed?

How many, how often, where?

What happened?

Stochastic Optimization

Descriptive

Prescriptive

Predictive

How can we achieve the best outcome?

How can we achieve the best outcome including the effects of variability?

Prescriptive

Predictive

Descriptive

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BUSINESS INTELLIGENCE AND DATA INTEGRATION

We are data drivenbut data starved Information

requests are taking too long.

I don’t want tospend every weekend draggingdata out of Excel

Our reports don’t work

We have new statutory data requirements that we are unable to meet

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BUSINESS INTELLIGENCE AND DATA INTEGRATION• Information for every decision-maker

• Query, reporting, analysis, scorecards and dashboards

• Delivered where, when and how needed

• View of all time horizons: past, present & future

• Up to date and actionable

• Free to answer critical business questions

• Answer “how am I doing”, “why”, and “what should I be doing?”

• Ensure organization-wide insight and alignment

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DATA SOLUTION – IDEAL REPORTING SETUP

Revenues

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Revenue FactAmountAmount - Target# of Contributions# of Contributions - Target

Date DimensionDateMonthQuarterYearFiscal YearDay of WeekWeek of MonthWeek of YearFiscal Period

Funding DimFunding Purpose IDFunding PurposeFunding Objective

Contribution DimContribution Type IDContribution Type

Account DimAccount IDFirst NameLast NameStreet AddressCityStateZip-5Zip-4Loyalty LevelDonor Type

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Campaign DimCampaign IDCampaign NameCampaign Type

DATA SOLUTION – IDEAL REPORTING SETUPDate DimensionDateMonthQuarterYearFiscal YearDay of WeekWeek of MonthWeek of YearFiscal Period

(Total Revenues) 2014 2013 2012

Operations 101,523 118,563 108,293

Scholarships 253,231 367,843 387,210

Facilities 763,623 645,326 463,101

Drill down on 2014

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Account DimAccount ID….Loyalty LevelDonor Type


(Total Revenues) 2014 Q4 2014 Q3 2014 Q2 2014 Q1

Operations 38,579 25,381 21,320 16,244

Scholarships 73,437 68,372 58,243 53,179

Facilities 282,541 221,451 152,725 106,907

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Drill Down on Funding Objective of “Facilities”



Food Kitchen Reno 50,857 42,076 25,963 13,898

Mobile Fleet 45,206 33,218 16,800 12,829

Satellite Center 186,477 146,157 109,962 80,180

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Filter on Contribution Type of “Pledge”







Food Kitchen Reno 2,543 - 11,164 -

Mobile Fleet 18,535 12,955 - -

Satellite Center 98,833 93,541 - 48,910

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Filter on Donor Type of “Corporation”





DATA SOLUTION – IDEAL REPORTING SETUPDate DimensionDateMonthQuarterYearFiscal YearDay of WeekWeek of MonthWeek of YearFiscal Period


Food Kitchen Reno 1,710 - 6,600 -

Mobile Fleet 13,160 8,165 - -

Satellite Center 66,455 13,160 - 28,857

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DATA PROBLEM – DIRTY DATA

Does your data look like this? We need to clean it up!

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DIRTY DATA – COSTS TO THE BUSINESS

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DATA QUALITY BY EXAMPLE

First Name:Last Name:AddressL1:AddressL2:City:State:Zip Code:

First Name:Last Name:AddressL1: 1008 Avenues of the Americas AddressL2: Suite 7City: ManhattanState:Zip Code:

james smith1008 6th avenue suite 7Manhattten, newyourk 10002

First Name: JamesLast Name: SmithAddressL1: 1008 Avenues of the Americas AddressL2: Suite 7City: ManhattanState: New YorkZip Code: 10018

First Name: JimMid Name: J. Last Name: SmythAddressL1: 1008 Avenues of the Americas AddressL2: Suite 7City: ManhattanState: New YorkZip Code: 10018Phone: (212) 755-2551Email: [email protected]

First Name: JimMid Name: J. Last Name: SmythAddressL1: 1008 Avenues of the Americas AddressL2: Suite 7City: ManhattanState: New YorkZip Code: 10018-5402Longitude: 40.7325525Latitude: -74.004970Phone: (212) 755-2551Email: [email protected]_Category: Affluent Couples & FamiliesC_Group: Affluent Families

Jim J. Smyth Manhattan, NY [email protected](212) 755-2551

Profile

Parse Correct Standardize

Match

Consolidate

Enhance

CRM System

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MATCHING AND DE-DUPLICATION

DonorName

Number of Donations

Total Paid

Chris Froome 11 11000Mark Cavendish 7 7000Bradley M Wiggins 6 6000M.S. Cavendish 6 6000Bradley Marc Wiggins 5 5000BM Wiggins 4 4000Taylor Hamilton 3 3000Tyler Hamilton 2 2000Brad Wiggins 2 2000Bradley Wiggins 2 2000Tylor Hamilton 1 1000B.M. Wiggins 1 1000

Business Question: Who is my Top Donor?

DonorName

Number of Donations

Total Paid

Bradley Wiggins 20 20000Mark Cavendish 13 13000Chris Froome 11 11000Tyler Hamilton 6 6000

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STANDARDIZING

Value FreqM 352F 2561 132 26? 82N/A 46male 1009female 1320<NULL> 1400

Value FreqMale 1374Female 1602Unknown 1528

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STANDARDIZE MULTIPLE SOURCES

Value FreqM 352F 256? 82

Value FreqMale 1374Female 1602Unknown 1528

Value Freq1 132 26N/A 46

Value Freqmale 1009female 1320<NULL> 1400

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AGENDA

• Business Analytics Overview• Business Intelligence and Data Integration

• Budgeting and “What if” Analysis• Predictive Analytics

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Where’s the latest budget? Mine is missing Planned Giving numbers.

When I open that one I get a macro error.

Is that the one named “final v4”?

I emailed it…oh, no use the one I emailed at 9:46 instead of the one at 8:23.

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BUDGETING AND “WHAT IF” ANALYSIS Dynamic Planning

Annual Budgeting Rolling Forecasts Operational Planning

Modeling and Analysis Model What if Scenarios “Browse” and Analyze Data Share the Results for Informed Decisions

Financial Close and Management Reporting Consolidated Financial Results Regulatory Filings Board Report Packages Streamlined Preparation and Review Process

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BUDGETING AND FINANCIAL REPORTING

Financial Planning & Analysis

Financial Management & Control

• Budgeting by Business Unit• Budgeting by Department• Rolling Forecasts• Financial Analysis• Profitability Analysis

• Management Reporting• Integrated Financial Reporting• Forward-Looking Projections

31

AGENDA

• Business Analytics Overview• Business Intelligence and Data Integration• Budgeting and “What if” Analysis

• Predictive Analytics

33

ANALYTICS AT WORK

Deg

ree

of C

ompl

exity

Standard Reporting

Ad hoc Reporting

Query/drill Down

Alerts

Forecasting

Simulation

Predictive modeling

Optimization

What exactly is the problem?

What will happen next if ?

What could happen … ?

What if these trends continue?

What actions are needed?

How many, how often, where?

What happened?

Stochastic Optimization

Descriptive

Prescriptive

Predictive

How can we achieve the best outcome?

How can we achieve the best outcome including the effects of variability?

Prescriptive

Predictive

Descriptive

34

PREDICTIVE ANALYTICS

Your data assets can be tapped to make better predictions and better decisions!

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Making many decisions based on ‘gut feel’?

Or untimely information?

PREDICTIVE ANALYTICS

Predictive Analytics helps connect data to effective action by drawing reliable conclusions about current conditions andfuture events

Gareth Herschel, Research Director, Gartner Group

Enabling organizations to use predictive models to exploit patterns found in historical data to identify potential risksand opportunities before they occur.

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WE ENCOUNTER PREDICTIVE ANALYTICS IN OUR DAILY LIVES…

• Airline Ticket Pricing• Predictive pricing models used to

drive ticket pricing

• Recommendation Engines• Netflix -- Which movies might you

like?• Amazon – Which products might you

want to buy?• Pandora – Given the songs you like,

what other songs are you likely to enjoy?

• Google Search box auto suggest• Email Spam filtering

• FICO scores for credit cards, auto loans, home loans, etc.

• Predict likelihood you will pay

• Insurance underwriting for health, auto, life, etc.

• Predict likelihood you’ll make claims and expected value of claims

• USPS Zip Code recognition system

• Election campaigns – Which voters will respond to which messages?

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ANALYTIC-DRIVEN ORGANIZATIONS ARE DISTINGUISHED BY THEIR ABILITY TO LEVERAGE…

All perspectivesPast (historical, aggregated)

Present (real-time)Future (predictive)

At the pointof impact

All decisionsMajor and minor

Strategic and tacticalRoutine and exceptionsManual and automated

All informationTransaction dataApplication data

Machine data Social data

Enterprise content

All peopleAll departments

Experts and non-expertsExecutives and employees

Donors and grantorsVolunteers and program recipients

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HOW DOES IT ALL WORK TOGETHER?

Meta D

ata Layer

Data Collection

Reporting&Analytics

Optimized for reporting to support an enterprise view of information

ETL with Data Quality

GL CRMAR

IntegratedData

Repository

Cubes store aggregated data, and allow for data entry / collection:

PredictiveModeling

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WHAT ARE THE APPLICATIONS WITHIN NONPROFITS?

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What will our revenues be this quarter? This year?Which pledges are likely to become uncollectable?

Which recipients are most likely to benefit?Which alternative services are likely to yield best results?

Who are our best employees?How do we keep our best employees from leaving?Which prospects to recruit?Which prospects are most likely to volunteer?

Programs

Finance

Marketing & Fundraising

Human Resources

Who are our best donors?How do we find more like them?How large a donation should we request?When, and through what communication channel are they most likely to respond?What other things (such as programs, services, products, events) are they likely to be interested in?

Questions? [email protected] 720.980.9615

Know Your Data!

ba presentation - revenue generation through data management … · • ensure organization-wide...

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