Building a Business on Data: Challenges and Rewards Naras Eechambadi - - PowerPoint PPT Presentation

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Building a Business on Data: Challenges and Rewards Naras Eechambadi - - PowerPoint PPT Presentation

Jun 07, 2023 192 likes •434 views

Building a Business on Data: Challenges and Rewards Naras Eechambadi and Kurt Newman March 27, 2019 Todays Presentation Business Opportunity The Data Markets & Use Cases Challenges Solution Results

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SLIDE 1

Building a Business on Data: Challenges and Rewards

Naras Eechambadi and Kurt Newman March 27, 2019

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SLIDE 2

Today’s Presentation

  • Business Opportunity
  • The Data
  • Challenges
  • Markets & Use Cases
  • Solution
  • Results
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SLIDE 3

Business Opportunity

Rich Data Large Market New Revenue

A business is born

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SLIDE 4

Today’s Presentation

  • Business Opportunity
  • The Data
  • Challenges
  • Markets & Use Cases
  • Solution
  • Results
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SLIDE 5

ADP is one of the leaders in Payroll and HCM solutions

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SLIDE 6

Paychecks are the source of ADP data

EMPLOYEE ADDRESS EMPLOYER ADDRESS

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SLIDE 7

Paycheck: Data Anonymized & Aggregated

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SLIDE 8

Reported by Geographic Location Layers

Nation 1 Regions 4 Divisions 9 States 50 Counties ~3200 Census Tracts ~74K Block Groups ~211K Census Blocks ~11.2M

Zip Codes & MSAs

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SLIDE 9

Today’s Presentation

  • Business Opportunity
  • The Data
  • Challenges
  • Markets & Use Cases
  • Solution
  • Results
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SLIDE 10

Market Problem & Opportunities

Verticals

Macr cro E

  • Economic S

c Strategy:

Macro view of the US in context of net migration, employment income, demographics, industries and job types.

Micr cro Econ

  • nom
  • mic S

c Strategy:

Validates site selections. Identifies emerging and distressed areas. Provides ability to identify, segment and target populations at a local level.

Retail B l Bank nking ing: C Competit itiv ive Environment

Ability to view “direct deposits” via share of wallet, determined by pay check deposits.

Use C e Cases es

Capital Markets Research Real Estate Multiple Verticals Banking

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SLIDE 11

Today’s Presentation

  • Business Opportunity
  • The Data
  • Challenges
  • Markets & Use Cases
  • Solution
  • Results
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SLIDE 12

Problem Statement

We n e needed ed a an automated proces ess for data set et c crea eati tion, v validation a and d deliver ery to to c clien ents. The p e process w was r req equired to t to support: t:

  • Rapid i

iter erati tive f file e prep eparati tion f for c clien ents t that n t need eed t to e evaluate m multi tiple d data format mats.

  • Sched

eduled ed deliver ery o

  • f files c

clients ts n need e each m month/week eek.

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SLIDE 13

Today’s Presentation

  • Business Opportunity
  • The Data
  • Challenges
  • Markets & Use Cases
  • Solution
  • Results
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SLIDE 14

Automated and Scalable Data Environment

Implementation of a highly automated and scalable environment for quality control, automation and scalability launched Q2-FY19

Launched infrastructure that scales to ensure efficient sales fulfillment

New Ventures Environment

  • Dedicated Ventures Reporting & Analytics

environment

  • Secure ADP instance of AWS-hosted

environment (with Quaero CDP Platform)

Data Cloud Production

Current Ventures Data State

  • Increased data security (limited access)
  • Automation increases efficiency for data set preparation and

delivery

  • Scalability to support growth
  • Incorporation of 3rd Party data
  • Predictable, rapid responses to client needs
  • Automation reduced process time and the probability of errors
  • Many data files can be created, fully validated and delivered to

most clients in about 90 minutes

Bui uilt a a hi highl hly a aut utomated a d and nd scalable V Ventures Data Environment t to ens nsure effic icient hi high h qua quality da data de delivery

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SLIDE 15

Quaero CDP Architecture

  • CDP automates data

processing and creates data assets

  • Data assets are used in

client extracts, analytics and Explorer

  • CDP auto scales compute

and storage based on data volume and processing need

  • Role based permission is

enabled within applications and database layer

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SLIDE 16

Data Monetization is a multi-step process

Data Cloud team models and publishes Sources provide data Ventures team Collects, Analyzes, and Validates Commonly requested aggregations created for “Standard Files” Data Received, Discovered, Analyzed by Client Client uses Data, Revenue Booked Adjustment, analysis etc Iteration and refined data requests

Client Specific Data Set Requirements Include:

Filters Aggregation Fixed Panel Frequency Distribution

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SLIDE 17

Data Processing and Client Extract Process

Transformation Automated Data Delivery via MFT Automated Validation of Incoming Data Data passed the defined thresholds Process Stops No Data Assets Yes Aggregate Tables for “standard files” extract

1 2 3 4 5 6

Configure Extracts

7

Automated Extract File Validation

8

Data passed the defined thresholds

9

No Yes Automated File delivery to Clients via MFT

10

Automated notification to Client and ADP team

11

Process Stops

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SLIDE 18

Today’s Presentation

  • Business Opportunity
  • The Data
  • Challenges
  • Markets & Use Cases
  • Solution
  • Results
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SLIDE 19

Identify Work & Residence Populations

Where Employees Go To Work Where Employees Come From

Change Age Profession Tenure Commute 8% 5% 11% 21% 27% 18% 8% 14% 9% 23% 32% Income Industry

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SLIDE 20

Share of Wallet

$HARE OF WALLET

View c compe petitiv ive de depo posit l lands ndscape a across t the he U US us using ng relia iabl ble ADP pa pay c che heck de depo posit it a and nd pa payroll ll da data

Data extract visualization using Tableau Application tool for data analysis

Deposits tracked monthly:

  • Financial Institutions: Banks, Credit Unions
  • Total deposits (dollars & paychecks)

Demographics tracked monthly:

  • Income, age, gender & generation type

Data Aggregated:

  • State, County, City & Zip Code

Share of Wallet Measurement & Trends:

  • Total dollar deposits
  • Total paycheck deposits
  • Top Five Banks
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SLIDE 21

Data Grain

1 Nation

United States

Avail ilable at the he level of de depth h requ quir ired a and nd on a n a monthly ly ba basis

New York New York Metropolitan Area/Tri-State Area New York County

(Manhattan)

10036

(New York, NY)

1008

(Times Square)

Census Blocks

(11.2M)

ZIP Codes

(32K)

Counties

(3,200)

MSAs

(380)

States

(50)

bctcb2010: 10125001008 boro_code: 1 boro_name: Manhattan cb2010: 1008 ct2010: 012500 share_area: 31542.5183224 share_leng: 769.081961398

*

* For statistical purposes and graphical representation, the Census Bureau’s ZIP Code Tabulation Areas are used. ** ADP requires a minimum number of employees and employers to populate data for the next geo-layer. *** Census blocks hierarchy includes census blocks, block groups, and census tracts.

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Predict Case Shiller Change Over the Next 12 Months

  • An initial model using ADP data to predict Case Shiller Index changes over the next 12 months.
  • The observed values (blue dots) and predicted values (orange dots) are shown for Case Shiller MSAs.
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SLIDE 23

Summary/Conclusion Slide

Data collected for operational purposes can have potential value

  • utside the initial domain

The rewards are lower costs, faster and higher value realization Realizing this potential requires significant transformation Having the right tools can accelerate this process

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SLIDE 24

Thank You

Naras Eechambadi naras@quaero.com Kurt Newman kurt.newman@adp.com