Evolving Data Quality Management Capabilities in Complex ...mitiq.mit.edu/IQIS/Documents/CDOIQS_200777/Papers/01_01_1A.pdfEvolving Data Quality Management Capabilities in Complex Organizations:

EvolvingEvolving Data Quality Management Capabilities Data Quality Management Capabilities

in Complex Organizations:in Complex Organizations: The Health Care CaseThe Health Care Case

Bruce N. Davidson, Ph.D., M.P.H.Bruce N. Davidson, Ph.D., M.P.H.Director, Resource & Outcomes ManagementDirector, Resource & Outcomes Management

CedarsCedars--Sinai Health SystemSinai Health System

Proceedings of the MIT 2007 Information Quality Industry Symposium

PG 5

Bruce Davidson, PhD - copyright Cedars-Sinai Health System, 2007

The Context The Context CedarsCedars--Sinai Medical CenterSinai Medical Center

•• Academic Medical Center/Health SystemAcademic Medical Center/Health System

•• Largest NonLargest Non--Profit Hospital in the Western USProfit Hospital in the Western US

•• 950 Beds, 10,000 employees, 2000 MDs950 Beds, 10,000 employees, 2000 MDs

•• Basic Annual StatisticsBasic Annual Statistics

–– 55,000 inpatients55,000 inpatients

–– 280,000 outpatients280,000 outpatients

–– 55,000 ER visits55,000 ER visits

–– 7,000 deliveries7,000 deliveries


PG 6


Data Management ImplicationsData Management Implications

•• Complex, informationComplex, information--intensive organizationintensive organization

•• Distributed oversight responsibilitiesDistributed oversight responsibilities

•• Transactional data systems populated as Transactional data systems populated as byproduct of patient care byproduct of patient care

•• Information managed as departmental resource Information managed as departmental resource rather than as enterprise resourcerather than as enterprise resource


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Evolving from this viewEvolving from this view……

MISSIONMISSION

Patient carePatient care

TeachingTeaching

ResearchResearch

Community ServiceCommunity Service

RESOURCESRESOURCES

PeoplePeople

MoneyMoney

EquipmentEquipment


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……to this view, over the last 10 yearsto this view, over the last 10 years

MISSIONMISSION

Patient carePatient care

TeachingTeaching

ResearchResearch

Community ServiceCommunity Service

RESOURCESRESOURCES

PeoplePeople

MoneyMoney

EquipmentEquipment

InformationInformation


PG 9


•• 19971997

–– DPG convened to address DPG convened to address data crisesdata crises

•• 1998 1998

–– TDQM Summer CourseTDQM Summer Course

–– DQMWG Spun Off of DPGDQMWG Spun Off of DPG

–– IQ Survey, round 1IQ Survey, round 1

•• 1999 1999

–– DQ Concept KickDQ Concept Kick--OffOff


–– DQM Objectives first appear in DQM Objectives first appear in Annual PlanAnnual Plan

The The ““long and winding roadlong and winding road”” (1)(1)•• 2000 2000

–– Big DQ Improvement ProjectBig DQ Improvement Project

–– DQM Objectives appear again DQM Objectives appear again in Annual Planin Annual Plan

–– ROM Dept reorganization to ROM Dept reorganization to capitalize on DQ frameworkcapitalize on DQ framework

•• 20012001

–– DPG & DQMWG Charters DPG & DQMWG Charters renewedrenewed

–– DQM Objectives again in DQM Objectives again in Annual PlanAnnual Plan



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•• 20022002–– DPG thinks about proDPG thinks about pro--active active

DQM infrastructureDQM infrastructure–– ROM designated as data ROM designated as data

““clearinghouseclearinghouse”” for approval for approval of all clinical statistics of all clinical statistics reported outreported out

–– JCAHO accreditation JCAHO accreditation standards for MOI linked to standards for MOI linked to DQM initiativeDQM initiative

•• 20032003–– Data crisis: No P&Ls for 8 Data crisis: No P&Ls for 8

monthsmonths–– Initiate Data Warehouse Initiate Data Warehouse

Improvement ProjectImprovement Project

•• 20042004

–– Propose DQMU (x2)Propose DQMU (x2)

–– Implement Data Warehouse Implement Data Warehouse Improvement ProjectImprovement Project

–– DQMU funded as part of ROMDQMU funded as part of ROM–– IQ Survey, round 4IQ Survey, round 4–– Data crisis: No Management Data crisis: No Management

Reports for 5 months following Reports for 5 months following new PM system implementationnew PM system implementation

•• 20052005–– DWIP Objectives appear in Annual DWIP Objectives appear in Annual

PlanPlan

–– DQMU staffed and DQM Program DQMU staffed and DQM Program initiatedinitiated

–– DWIP Project Plan institutionalized DWIP Project Plan institutionalized as primary focusas primary focus for DPGfor DPG

The The ““long and winding roadlong and winding road”” (2)(2)Proceedings of the MIT 2007 Information Quality Industry Symposium

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•• 20062006–– DQ Objectives appear in DQ Objectives appear in

Annual PlanAnnual Plan–– DQ Objectives linked to DQ Objectives linked to

executive management executive management incentive compensationincentive compensation

–– Development of Data Development of Data Certification Program for Certification Program for ““High PriorityHigh Priority”” data elementsdata elements

–– Pilot estimation of DQ ROIPilot estimation of DQ ROI–– Development of explicit Development of explicit

criteria for resolving criteria for resolving ““High High PriorityPriority”” Data Quality Data Quality IncidentsIncidents

The The ““long and winding roadlong and winding road”” (3)(3)•• 20072007

–– IQ Survey, round 5IQ Survey, round 5–– 117 Data Quality Incidents 117 Data Quality Incidents

logged since initiation in logged since initiation in February 2005, of which 82 February 2005, of which 82 have been resolved and have been resolved and closedclosed

–– Collaboration with Internal Collaboration with Internal Audit department relative to Audit department relative to minimizing risk due to minimizing risk due to defective datadefective data

–– Continuing challenge to frame Continuing challenge to frame strategic issues in context of strategic issues in context of ““managing information as an managing information as an enterprise resourceenterprise resource””


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What We Hope ForWhat We Hope For

•• Quick resultsQuick results

•• Unflagging supportUnflagging support

•• Universal cooperationUniversal cooperation


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What We GetWhat We Get•• Often painfully slow progress Often painfully slow progress

with regular periods of with regular periods of stagnation, if not reversalstagnation, if not reversal

•• Occasional bursts of support with Occasional bursts of support with frequent periods of inattention, or frequent periods of inattention, or forgetfulnessforgetfulness

•• Gradually enhanced, but Gradually enhanced, but intermittent, cooperationintermittent, cooperation


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What Does It Mean?What Does It Mean?

•• Evolutionary processEvolutionary process

•• Limitations of hierarchical controlLimitations of hierarchical control

•• Development of Development of ““shared mental shared mental modelsmodels”” = work= work


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Galaxy Data Quality Program MIT IQ Industry Symposium

July 18-19, 2007IngenixUnited Health AnalyticsGalaxy – Shared Data WarehouseLaura Sebastian-ColemanIS Manager – Data Quality & End User Support


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©2006 Ingenix, Inc

Overview

Ingenix and GalaxyGalaxy’s DQ programEvolving business needs and the pace of changeData quality in relation to evolving business needs


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©2006 Ingenix, Inc

Ingenix Background

A global healthcare information companyFounded in 1996 to develop, acquire, and integrate some of the nation’s best-in-class healthcare information capabilities Significant and rapidly evolving portfolio of tools and servicesnow transform data into actionable, fact-based, technology-enabled decision supportRanked among the top 10 providers of informatics by Healthcare Informatics magazine in June 2006Today there is an Ingenix product at work in nearly every U.S. healthcare organization.Ingenix is a wholly owned subsidiary of UnitedHealth Group (UHG).


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©2006 Ingenix, Inc

Galaxy Overview

Atomic Data Warehouse with transformationsIntegrates data from more than a dozen subject areas (claim, membership, customer, provider, etc.) across multiple sourcesSize

350 source input files from more than 25 distinct internal and external sources (and counting)18 TB of data; 62 TB footprint3,159 attributes across 12,632 columns in 600 tables (and counting)Largest table: more than 1.5 billion rows

1,704,717,031 on Claim Statistical Service as of 5/3/07

UsageOver 1,000 registered users7,888 queries per day / 256,656 per month, on average Ad hoc, scheduled queries, production extracts to applications and martsDirect access to Galaxy via user-selected tools – Sagent is administratively supported


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©2006 Ingenix, Inc

Galaxy Physical Architecture

Man

agem

ent S

ervi

ces

Dom

ain

Tool

s R

esou

rce

(176

/ 49

)

Sto

rage

/Bac

kup

-IP

(40

/ 93)

Stor

age/

Bac

kup

-IP

(40)

Isol

ated

VLA

N (1

92.1

60.0

.0/1

6)Is

olat

ed V

LAN

(172

.16.

0.0/

16)


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©2006 Ingenix, Inc

System Components

Hardware7 IBM P-series Servers P5752 IBM P-series Servers P5101 IBM P-series Server P5704 EMC DMX 3000 Storage CabinetsAdditional supporting servers for Sagent, Autosys, etc.

SoftwareUDB with DPF v8.2AIX 5.3.0DataStage/PX 7.0.1Optiload 3.1CoSort 7.5.3Autosys 4.5Sagent 4.5i


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©2006 Ingenix, Inc

Galaxy Source Systems & Subject AreasProceedings of the MIT 2007 Information Quality Industry Symposium

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©2006 Ingenix, Inc

Functions of Galaxy Data

Galaxy is the single source of truth for key business functionsMedical Trend AnalyticsPricingProvider Utilization & ProfilingAppropriateness of CareNetwork AdequacyCare Management / Pattern of Care / Preventive CareFraud & AbuseCustomer ReportingHEDIS ReportingMember DemographicsProduct Penetration


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©2006 Ingenix, Inc

Galaxy’s Data Quality Program

Management recognized need for DQ when Galaxy was launched Theoretical / methodological foundations

Correct data problems at the sourceData as a productStatistical process control

Primary functions of DQ programMonitor, measure, and report on Galaxy’s Data QualityRecommend and implement actions based on findings

Biggest initial challenge = establishing useful metricsWhat to measure / how to measureHow to respond to the results of measurements

2003 Initiated metrics & reporting program2004 Implemented first automated measures 2004-2007: Deliver weekly/cyclic, monthly, quarterly, semi- annual reporting through largely automated processes


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©2006 Ingenix, Inc

Example of Weekly Measure

TGT_TBL_ETL_DT

Indi

vidu

al V

alue

5/24/

2007

4/2/20

07

2/10/

2007

12/27

/2006

11/2

/2006

9/17/

2006

7/26/20

06

6/2/20

06

4/14/20

06

2/23/

2006

2.5

2.0

1.5

1.0

0.5

0.0

_X=1.532

UCL=1.869

LCL=1.195

1111

111111

11

1

111

1

1

DQ 128 Procedure Revenue Code on Unet Claim Stat Header


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©2006 Ingenix, Inc

Quarterly Management Report

Baseline Data Quality –annual review and baseline assessmentQuality Indicators – key attribute monitoringGalaxy Data Issues Tracking

III . Galaxy Data Issues Tracking

Report Date: 9/16/05Galaxy Operations Contacts: Nancy Grimaldi, Eric Infeld, Laura Sebastian-Coleman

c.2005 UnitedHealth Group - Proprietary & Confidential

II . Quality Indicators -- Key Attribute Monitoring

Galaxy Data QualityAs of 9/16/2005

I . Baseline Data Quality -- Annual Review and Baseline Assessment Update

GALAXYThe DQ team conducted its Annual Baseline Assessment in July and August. This year’s Baseline reviewed 1483 primary and foreign keys on Galaxy’s fact tables for business expected values. The Baseline focuses on identiying and researching business illogical values and provides a benchmark for the validity and accuracy of key data. The results showed 99.73% of attributes exceeded 4 sigma, as compared with 99.44% in 2004 and 96.82% in 2003. This improvement was due to issues identified and resolved through previous baseline assessments and ongoing maintenance and quality improvement efforts.

For the past two years, we have measured values based on a four sigma standard. This year we also collecteded data for five and six sigma. • 99.46% of attributes exceeded 5 sigma • 99.12% of attributes exceeded 6 sigma

4 attributes in two subject areas did not meet 4 sigma • One involves invalid values being populated due to issues with a source feed (see P&I request 1613). • One involves a field that was hard-coded with a default value (see P&I request 1617) • Two involve blanks in fields where blanks are not expected; these need to be researched to determine whether blanks are being supplied by the source or caused by a Galaxy process (see P&I Requests 1618 and 1619)

• Additional automated reports on this model are now being implemented on newly integrated MAMSI data.

• By indicating whether Galaxy matching processes are operating as expected, these reports allow for early identification of potential data and processing issues.

• Galaxy data issues are identified by SDW business analysts or by end users, tracked through an internally maintained database, and addressed as part of ongoing operations."Issues" reported do not include funded projects or source system changes that require a work effort on the part of Galaxy Operations.

• The total number of issues quarter-to-quarter has remained relatively steady. Currently 83 issues are identified as medium to high priority, compared to 82 last quarter.

• Of the 83 identified issues, 3 are actively being resolved. This is 3.6% of identified issues, compared to 8.5% last quarter. See graph on the upper left.

• The graph on the lower left shows the number of requests closed per quarter. Requests include issues, external initiatives, internal initiatives, and source system changes. 18 issues were closed this quarter compared to 12 in Q2 2005. 17 external requests were closed this quarter, as opposed to 3 in Q2 2005.

• The graph on the right shows the distribution of issues across subject areas for the second and third quarters of 2005. In all subject areas, SDW will continue to leverage project teams to address issues as opportunities are identified to do so.

• The graph to the right above tracks results from the Pharmacy Claim control and compares the level of default Member System IDs on UNet and Prime claims.

• This quarter (Q3 2005) there have not been any spikes in the Pharmacy Claim matching process.

• The two dramatic spikes were caused by an increase of several hundred records above expected levels. The overall level of defaults remains at less than a tenth of a percentage. Before process changes in July 2004, the level for both source systems was around 0.10%.

• Control reporting tracks key quality indicators as part of the cyclic data prep and load process.

• The Unet Claim graph (above) illustrates how well Galaxy identifies CES membership and matches members to UNet claims. This process has been operating within expected limits at this level for over a year and is now showing a strong downward trend.

eaProv ider

ProductPharmacy

OrganizationMemberLab

GeographyFAOCustomer

C laim Statistical

Claim F inancial

35

30

25

20

15

10

5

0

VariableIn constructionOutstanding

Number of Issues Outstanding By Subject Area 6-15-2005

Graph represents a point-in-time snapshot of data on 6/16/05

1

5 1 1

82 Issues identified 7 Solutions under construction75 Issues outstanding

5

18

2

31

1

3

21

2

Load Date

Perc

ent

of 0

Mbr

Sys

ID

cla

ims

8/27/

2005

7/18/

2005

6/12/

2005

5/3/20

05

3/25/

2005

2/14/

2005

1/2/20

05

11/29

/2004

10/9/20

04

9/2/20

04

7/28/

2004

0.080

0.075

0.070

0.065

0.060

0.055

0.050

Accuracy MeasuresMAPE 6.26364MAD 0.00411MSD 0.00003

VariableActualF its

Trend Analysis Plot for Percent of 0 Mbr Sys ID claimsLinear Trend Model

Yt = 0.0714214 - 0.000162139*t

Load Date

Perc

enta

ge

7/28

/200

5

5/3/

2005

2/14

/200

5

12/3

/200

4

9/12

/200

4

6/26

/200

4

4/3/20

04

1/11

/200

4

10/24/

2003

8/2/20

03

5/21

/200

3

0.11

0.10

0.09

0.08

0.07

0.06

0.05

0.04

0.03

0.02

VariablePrime PercentageUnet Percentage

Percentage of 0 Member System IDs on Pharmacy Claim by Source SystemData as of 09-03-05

Num

ber

of R

equ

ests

Clo

sed

2005 - Q32005 - Q22005 - Q12004 - Q42004 - Q32004 - Q22004 - Q1

60

50

40

30

20

10

0

QuarterIssuesExternal initiativesInternal InitiativesSource system changes

Requests Closed Per Quarter 2004 - Q1 through 2005 Q3

aProv ider

ProductPharmacy

OrganizationMemberLab

GeographyFAOCustomer

C laim Statistical

Claim Financial

35

30

25

20

15

10

5

0

VariableIn constructionOutstanding

Number of Issues Outstanding by Subject Area 9-15-05

83 Issues Identified 3 Solutions under construction79 Issues outstanding

Graph represents point-in-time data on 9/16/05

1

28

2

41 1

2

1

30

6 7

Date

Num

ber

of I

ssu

es9/16/20056/16/20053/11/200512/09/20049/10/20046/16/20043/01/2004

90

80

70

60

50

40

30

20

10

0

Variable

Issues Outstanding

Issues IdentifiedSolutions in Construction

Addressing Identified Issues in Galaxy

Graph represents point-in-time data for dates taken once per quarter.

Q1 2004 - Q3 2005

Num

ber

of a

ttri

bute

s an

alyz

ed

Year 200520042003

1600

1400

1200

1000

800

600

400

200

0

VariableAttributes not meeting 4 sigmaAttributes meeting 4 sigma

Baseline Assessment Results 2003-2005

36

1101

8

1239

4

1480

96.82% met 4 sigma

99.44%met 4 sigma

99.73%met 4 sigma


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©2006 Ingenix, Inc

Current Situation

Galaxy = a mature, enterprise data warehouseHigh demand for data and for organizational servicesGalaxy’s DQ program also relatively mature

Defined metricsAutomated data collectionRegular reportingDQ Community

UHG growing, largely through acquisitions and partnershipsHealthcare industry changing – relation of government to health care, new products, esp. consumer driven


PG 27

©2006 Ingenix, Inc

Pace of Change for Galaxy

2004 Galaxy integrated data from MAMSI, a United Health Group acquisition

Used the existing structure1+ year to integrate

2006 Integrated data from three new source systems Developed a new subject area, RevenueSignificantly expanded Customer subject areaResponded to healthcare industry changes

Part D data HRA (Health Reimbursement Account) data

2007Integrate data from additional acquisitionsExpand the Revenue subject areaContinue to support the use and enhancement of existing data.

2008Two major integrations already scheduled Potential for several others


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©2006 Ingenix, Inc

Pace of Change for Galaxy DQ

Biggest challenge 2003 what to measure and how to measure2007 how to rapidly analyze and act on DQ data

Baseline Assessment of Galaxy Data Quality2003

800 person hours to pull and analyze data for first Baseline AssessmentDuration = more than 3 months Measured 1137 attributes

2006 Pulled 75% of data in less than 10 hours through an automated process Measured 1506 attributesPull data quarterly

Automated reports 2004: 4 reports2007: 80 reports Reports now implemented as part of standard development process.


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©2006 Ingenix, Inc

2007 – 2008 Key UHG Business Needs

UHG acquisitions and partnerships –More data for Galaxy More users need access

Users need data sooner –Time to integrate data into Galaxy must be shortened

Legacy data critical for ensuring reporting continuity and analytics –

Continued support is necessaryData consistency across sources critical for reporting continuity and analytics –

Integration methodologies need to promote and enforce consistency


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©2006 Ingenix, Inc

How to Respond?

Data Quality included in set of changes to improve efficiency and agility

Common Interface – puts more responsibility on source systems for data qualityGateway – changes how Galaxy prepares data.

DQ measuresMore comprehensiveTaken earlier in the process More fully automated


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©2006 Ingenix, Inc

Common Interface Approach

Galaxy defines standard requirements and layouts for dataSources map to these requirements and feed to GalaxyStreamlined transformation/load into GalaxyCommon model across the enterprise


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©2006 Ingenix, Inc

Common Interface Architecture – Views

Common InterfaceTable - Source 1

Common InterfaceTable - Source

2Common Interface

Table - SourceXExisting

UNet/COSMOS/MAMSI Table(s)

Physical Tables (Objects)

COMMON DATA ITEMS Source 1 +

Source 2 + Source N +Existing

UNet/COSMOS/MAMSI(common data items only;

updated field formats)

Enterprise View


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©2006 Ingenix, Inc

Gateway Integration Tool

Facilitates mapping disparate data sources into a Master Data DefinitionApplies generic transformation logic to the outputUtilizes reusable transformsPerforms automatic code generationEnsures consistency across source-to-target mappingsProvides true-to-code documentationIncorporates data quality modulesIncreases speed and reduces complexity of data integrations


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©2006 Ingenix, Inc

Gateway Integration Tool

Gateway Mapping

and Validation DatabaseTransformation

New DataSources

GatewayMaster DataFile Format

Standard reusabletransformations


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©2006 Ingenix, Inc

Gateway – Data Quality Features

DQ functionsMonitor and react to events in processing Collect trend data

Field validationData type checkingValue range checkingValid value list checkingAssignment of default valuesInformational, error and warning messages

File validationFormat checkingField counts / record length validationSummary of field error and warning messagesThresholds of summary counts of errors and warnings that allow job to be aborted if counts or percentages exceeded – generate alerts


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©2006 Ingenix, Inc


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©2006 Ingenix, Inc

Back to Basic DQ

Data in the warehouse is only as good as data in the source

Ensuring sources to supply better data through the Common Interface

Manufacturing model: Data as a product produced through a process

Executing processes more consistently across the database through the Gateway

Measure to improve Gateway integrates and executes DQ measures consistently across the database. Both tools measure ETL processes (timing of jobs, etc.) that affect other aspects of data quality from end-to-end


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©2006 Ingenix, Inc

DQ: Chicken or Egg?

After 4 years – back to the beginning Applying theory/methodology more fully Applying at the beginning of integrations Applying more comprehensively across the warehouse

Major re-thinking of all Galaxy processes Interacting with customersWriting specificationsObtain source filesMapping source-to-targetImplementing ETLBuilding physical tablesTaking DQ measures

DQ still requires championing New problem: How to analyze and respond to findings from the data gathered through new process.


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April 4, 2007

DSOWebInnovation in ETLTransforming Raw Data into the Building Blocks of Intelligence


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© Ingenix, Inc. 2

About Ingenix - Data Services Organization (DSO)

The Ingenix Data Services Organization (DSO) annually processes and integrates millions of service lines and other claim-related data from more than 500 diverse health and productivity related data sources for more than 125 large employers and hundreds of small employers. This translates to:

4250 feeds processed in 20058964 feeds processed in 200618,000+/- feeds processed in 2007


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© Ingenix, Inc. 3

About Ingenix - DSO Processes

Data is received across several media types related to eligibility, medical claim (includes vision and mental health), pharmacy claim, workers compensation, short-term disability, long-term disability, FMLA, lab results, disease management, health risk appraisal, payroll information, etc.Implementation is the design phase focusing on data layout, client account structure, initial data quality plan, conversion rules and successful completion of the first cycle. Update cycle is the periodic receipt of data from carriers/clients, focusing on data review, investigation/resolution of inconsistencies to ensure clean data, and loading onto analytical environment


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© Ingenix, Inc. 4

Business and Operational Challenges

Long turnaround time for data deliveryNeed for a cost effective data management solutionAbsence of streamlined data quality assessment and investigation processAbsence of company-wide standardized data intake processQuality review process that is manual and subjective leading to errors, rework and decreased confidence in resultsHigh maintenance costs


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© Ingenix, Inc. 5

Business Needs

Increase customer satisfaction through faster turnaround time and higher data qualityHigher productivity through faster learning curveElimination of manual intervention and continuous inspection of dataContinually increasing automationMeet growing demand for faster data delivery and higher level of data qualityEfficient data profiling and data managementFlawless data delivery to analytical environmentsAbility to leverage transformed data across multiple Ingenix products


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© Ingenix, Inc. 6

DSOWeb – Innovative ETL & DQ SolutionStandardized data processing by data types – eligibility, medical, drug, disability, WC, FMLA, HRA, lab and disease managementStandardized incoming data into common format by data typeAutomated data quality checksAutomated data trendingAutomated file processing and job monitoringUser ‘friendly’ interfaceData quality and trending failure analysesTransformation and mapping functionalitiesIntegrated data quality investigation functionalitiesValidated client and carrier details from profiles such as:

– Employee status, employee type and types of coverage – Number of covered lives and products

Operational and data quality metrics


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© Ingenix, Inc. 7

DSOWeb - Express eVal – UI Driven Mapping Functionality

Enables users to create source to common data mappingPoint and click functionality; functions, operators and attribute selection easily added to transformation boxDynamic Help; displays syntax and example for each functionOn-screen listing of source attributes, mapped attributes and look-ups for transformationsText box to capture business logic for each transformationAll transformations are stored in meta data tables and available for hard copy documentation and searches


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© Ingenix, Inc. 8

DSOWeb – Data Quality

Data Quality Rule engine systematically compares data results to rule thresholds and only flags exceptions for DSOWeb user interventionMetadata Driven Rules Engine - Quick turnaround for adding, deleting or modifying rulesMore than 7,000 data quality rules tailored to each data typeDifferent rule types for column property enforcement, structure enforcement and business rules enforcementFormulated from verified client claims, eligibility, lab results, HRA and workforce productivity data experienceBased on record distribution and benefits paid threshold


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© Ingenix, Inc. 9

DSOWeb – Trending

Flags unexpected trends in data. Two levels are:Month-to-date trending with the previous month’s dataYear-to-date trending with the previous year’s data.

– For year-to-date, the trending is done up to the month the data is being processed

– Example: If the plan begins in January and the current processing month is June, then trending is performed between received year’s January-June and data for January-June of the previous year.

Metadata driven trending rules engine; quick turn around for adding, deleting or modifying rulesConfigurable tolerance ranges for trending rulesExamples of tolerable ranges

Record Count Passing Range Less Than 10000 5% to +5% 10001 -25000 -3% to +3% Greater Than 25000 -1% to +1%


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© Ingenix, Inc. 10

DSOWeb – Job and File Processing Dashboard

ABC Client

ABC Client

ABC Client

ABC Client

ABC Client

ABC Client

ABC Client

ABC Client

Carrier A

Carrier B

Carrier C

abc_a_a_F20051001 _T20051231_VEL60

22.txt

abc_b_b_F2006040 1_T20060630_vEL2

267.txt

abc_c_c_F20051201 _T20051231_V6D00

58.txt


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© Ingenix, Inc. 11

DSOWeb – UI Driven DQ Failure Analysis and Decision Making

ABC Client

Abc_car1_car2_F20051001_T20051231_VEL6447.txt

Car1


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© Ingenix, Inc. 12

DSOWeb – Integrated DQ Investigation Tool

Data investigations and validation environment integrated within applicationUses BI tool allowing users to query converted data without needing to understand the data structure or a query languageAd-hoc and standard reports by data typeAutomatically schedules and creates standard reports with each production update


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© Ingenix, Inc. 13

DSOWeb – Raw Data Analysis Tool

Raw data investigation tool integrated within applicationUses home grown tool that generates SAS queries without the need to understand SASAbility to save queries for re-useAutomatically emails formatted results to end user for printing or sharing


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© Ingenix, Inc. 14

DSOWeb – Benefits

Cost-effective data management solutionFacilitates timely data investigationFaster turnaround time for data deliveryAutomated process eliminates human errorsStreamlined process ensures scalabilityMetadata based DQ engine; ease of rules maintenanceHuman intervention only targeted at exception cases; increases productivity and maximizes quality outputTimely issue resolutionMetrics on data quality and operational processes can be reported as needed with current dataReduces dependency on technologists in transformation process


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© Ingenix, Inc. 15

Ingenix - DSOWeb

Thank You


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Cambridge, Massachusetts, USA

The MIT 2007 Information Quality Industry Symposium

Proceedings of the MIT 2007 Information Quality Industry

Symposium

9 - 10:30 AM Session 1B: Federal Data Architecture Moderator: Skip Slone, Lockheed Martin 1. Adel Harris, Citizant, Inc 2. Mark Amspoker, Citizant, Inc 3. Burton Cutting, Royal Bank of Canada

E51-372


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