Data quality practical guide

Data Quality From Theory to Practice

Paul Ormonde-James Group Intelligence

MBF Australia

MARCUS EVANS CONFERENCES

Agenda

• Why is data quality important?

The problem to be solved…..

Target

You

are

here

Paul Ormonde-James – Presenter, Speaker, Author

• Paul, has spent many years in the field of

Change Management. He has

qualifications in Cybernetics (Robotics &

Artificial Intelligence), Computer Sciences,

a Master of Business Administration (MBA)

and post grad in Company Law. He has

driven many change programs and used

Business Intelligence, Competitive

Intelligence and Knowledge Management

to drive such cultural change to increase

company value.

Business Intelligence, simply…..

At the

To the

More effective decision making

Corporate Survival & Growth “Growing through Knowing”

Business BI

Why do we need data quality?

• There is a growing cross-industry recognition of the value of high-quality data.

• 2001 PricewaterhouseCoopers Global Data Management Survey, 75 percent of the senior executives reported significant problems as a result of defective data.

• In 2002, The Data Warehousing Institute published its Report on Data Quality in which they estimated that the cost of poor data quality to U.S. businesses exceeds $600 billion each year.

• Two critical pieces of legislation passed within the past few years impose strict information quality requirements on both public corporations in the U.S. (the Sarbanes-Oxley Act of 2002)

• As well as U.S. federal agencies (The Data Quality Act of 2001).

• Both of these laws require organizations to provide auditable details as to the levels of their information quality.

Do Managers really understand data quality?????

• How many times does a manager or executive ask where the data comes from?

• How many executives understand the numerous data sources in and across an organisation

• How many Executives understand the errors generated as incorrect data is summarised.

• Are executives more focussed on quality data the more important the decision?

The Journey…..

Back End Processing

Components

Back Office Departments

Contract Administration and Support Applications

Customer Contact ChannelsExternal Organisations and

Components

Front End Processing Components

Integrated Data Domains

Enterprise Business Reporting & Analytics

(DW Strategy)Place Logo Here

Enterprise Business Reporting & Analytics (DW Strategy)

Current Applications Model Business Unit Classification

Version 1

Effective Date: 13 July, 2005

File: Copy (1) of MBF Business Intelligence Current Applications Model - BU (1.2).vsd

Contact: Paul Ormonde-James, MBF - Business Intelligence - (02) 9323 9783

Author: Marie-France Gara & Tamer Galil, Chartres Business Solutions (Email [email protected])

This model has been prepared with due care from the information made available,

and is a true representation of the subject area as it was presented to the Author.

Commercial in Confidence

Legend

Current Applications Model

Business Unit Classification

The Chartres Path®

Oracle Financial Suite

Notes Data Flow Mode

Data Warehouse

MS Access Databases

Business Unit Ownership

Mainframe Domain (Subject Areas)

Diamond Domain (Subject Areas)

Customers

Staff Assisted

Membership, Product,

Account Payable,

Claims, Provider,

Premium Billing,

Bemis/DDS, Customer

Service

DIAMOND

Summarisation &

Reformatting.

- Prosthesis

- Hospital

- Medical

- Ancillary

- Customer/Membership

HCP, Providers

SAS DATABASE EAP37

4 5

Membership, Claims,

Travel, Revenue,

Sales – Other, Health

Rewards

HEALTH INSURANCE

SYSTEM

(HIS MAINFRAME)

Description

PRIVATE HEALTH

INSURANCE (PHI)

Eg. Investment &

Actuarial

RISK

MANAGEMENT &

TREASURYDescription

UNCATEGORISED

Various spreadsheets

used by Investments &

Actuarial for

Analysis & Reporting

Eg GL Variances

Calculations

MISC

SPREADSHEETS

Business Solutions - DW

(Group IT)

CORPORATE PROFITABILITY REINSURANCE CLAIMSWEEKLY SALES(HCP) HOSPITAL CASE MIX

PROTOCOLS

Replacing Corporate Profitability

MGM

(MEMBERSHIP GROSS MARGIN)CLAIMSMEMBERSHIP ACTIVITYREINSURANCE

Oracle

Balance Score Card

Web Front-End.

OBSC PORTAL

Description

GROUP

MARKETING

- General Ledger

- AP / AR

- Asset Management

- Purchasing

ORACLE

FINANCIALS

1

Not being used1

ORACLE BALANCED

SCORE CARD

(OBSC)

Belongs to PHI

BUSINESS &

CLINICAL

ANALYSIS

Risk Management &

Treasury

- Base SAS (H&B Stats)

- Enterprise Miner

(Data Mining & Modelling)

- Enterprise Guide

- SAS Stat

- SAS Graph

SAS PRODUCTS

SUITE

2 4 5

Aka Online Cubes for

Self-Service Usage.

Analysis & Reporting

Written In SAS

FUTRIX

4 5

- Contract Modelling &

Reporting.

- Self-service

- Based On Excel.

TM1

Based On SAS AF.

Benchmarking

Providers Against

Industry Profiles &

Trends

XRAY

Belongs to

Group Finance &

Corporate Services

BUSINESS

INTELLIGENCE

Data Summarization

& Reporting.

Part Of Cognos Suite.

IMPROMPTU

Replicated Diamond

Environment for

Reporting & Testing

DIAMOND RPT

- ITS

- Business Solutions/

Data Warehouse

- HIT, ...

GROUP IT &

PROGRAM

MANAGEMENT

Description

PHYSICAL DATABASE

MBF Health Policies

Data

WHICS

Business & Clinical Analysis

(PHI)

Predefined Corporate

Key Reports.

3 Claims are Updated.

WEB-BASED

CORPORATE

DOCUMENTS

Financial Services

Customer Experience

& Satisfaction Surveys.

(MS Access Database)

Used By Customer

Service & Retention

CUSTOMER

EXPERIENCE DB

Tracks Leads

Transferred From

Member Centers To

Clearview.


MBF LEAD

REFERRAL SYSTEM

Clearview & MBF Life

Client & Product Data

FINTECHNIX

CRM Siebel Database

SALES

OPPORTUNITY

MANAGEMENT

(SOM) SERVER

Retention Campaign

Data.

Tracks Reasons Why

Customer Redeem

Clearview Funds.


CUSTOMER

WATCHLIST

Data in Crystal

Enterprise.

Use of DW

is Recommended

excluding for

Operational Reporting

CRYSTAL REPORTS/

CRYST. ENTERPRISE

Replicated Fintechnix

Environment For

Reporting (Lifeprod).

Life, Superannuation,

Managed Funds Data

FINTECHNIX

REPORT SERVER

Tracks External Funds

Managers’ Rollovers.

Assirt V6


To Be Phased Out.

OPERATIONS

TRANSFER

TRACKING SYSTEM

PHIMBF Health

(PHI)

Financial Services

Description

GROUP FINANCE &

CORPORATE

SERVICES

Research Agencies

Private Health Insurance

Administration Council

(PHIAC)

Board

Belongs to PHI

MBF HEALTH

Group Marketing

Group Marketing PHI

Group Strategy & New

Business Development

Group Finance &

Corporate Services

Risk Management &

Treasury

Group Finance &

Corporate Services

Group IT & Program

Management

Business Intelligence

(Group Finance)

Commonwealth

Department of Health

& Ageing

- MBF Prothesis Price

List 2004

- CMBS 2000

- AMA

- Diagnostic Schedule


MISC PRICING DATA

1

- Hospital Details

- Contract Modelling

Maintained By Account

Managers.

To Be Fed by Diamond

MAXIMISER

PHI

Extract, Match, Format


- 7 State Sales DB

- 7 Weekly Member DB

- 7 Month Member DB

- Misc DB: Sales Rep...

MISC SALES &

MEMBERSHIP DB

Preventive, Asthma &

Diabetes Programs.

Updated Monthly


HEALTH

MANAGEMENT

PROGRAMS

New Items

(Provided by IT-DW)

To Be Matched to

Category of

Procedures.

Updated Monthly

ITEMS DATABASE

Balanced Score Card Data (via Exchange of Flat File)

Uncategorised

Point To Point

Middleware

File Transfer

Internal File

Email

Fax

Web

Voice

Screen

Hard Copy

External Memory

In2Life Campaign Reports (Monthly)

Claims, Provider,

Health Program ReportsClearview Product Reports (Ad hoc)P&L & OPEX

Reports

Customer Matrix (Monthly)

Claims Reports

Ad-hoc Reports

Ad-hoc

Membership

Reports

Investment &

Actuarial Reports

Claims Leakage & Exception Reports (via Excel)

Financial

Data

(via ETL)

MBF & MBFH

Sales &

Membership

Reports

- New Prices04

- Schedule 5

- File Upload, Master

- Human Tissue Items

- Deleted / Replaced

Items - Price Summary

MISC PROSTHESIS

DATABASES

- Claims 2003

- Claims 2004

- Claims Fy04

- Claims Fy04 V97

- Hospital

MISC HOSPITAL

DATABASES

- Duplicate Services

V2000

- Fy04 Gap Survey

- Contract Modelling

- CHC931 Medical

Claims

MISC MEDICAL

DATABASES

- 3 Claims - Dental

- 6 Monthly Reports

(Ancillary, Dental

Optical, Pharmacy,

Physio, Membercare)

- Quarterly Report

MISC ANCILLARY

DATABASES

PHI Health Program Reports

Misc Data Entry

Reinsurance Provision Details

Misc. Data

Revenue Data

Ad-hoc Data Extract

Health Programs

Data (Monthly)

Synchronised Data

Around 50 MS Access

Databases

MISC AD-HOC

DATABASES

Analysis Reports

(eg. Customer,

Competitor)

Analysis Cubes. IBM.

Decommissioned

in favour of

Business Objects XI

META CUBES

EOM & Ad-hoc

Reports (via Excel)

Reporting

Analysis Cubes

& Data Extraction.

BUSINESS OBJECTS

5.9

Clearview Leads Reports (Weekly / Ad hoc) (via MS PowerPoint)

Funds Sales Reports (Weekly / Ad hoc) (via MS PowerPoint)

Census Data

CDATA

- MS Access

- MS Excel

Membership Reporting

MISC. AD HOC

REPORTING

MBF & MBFH

Misc. Membership & Competitors

ad-hoc Analysis & Reports

EOM Provisioning (via MS Excel & Access)

Investment & Actuarial / Membership, Benefits & Sales Reports & Analytics


Used by Customer

Service & Retention

MISC REPORTING

DATABASES

GTX-PROD Database

(Graham Technology)

Call Center Data

CUSTOMER FACING

SYSTEM (CFS)Avaya IVR

Call Tracking System

CENTREVU

(MS Word)

CONSOLIDATED

FINANCIAL

REPORTING

Reports Formatting

CRYSTAL REPORTS

(MS Excel)

Data Extraction &

Formatting

MISC. MBF HEALTH

REPORTING

MBF Health

(PHI)

Sales Reports (Weekly,Monthly)

Membership & Sales Reports

Membership &

Sales ReportsMBF Health KPI (via MS Excel)

Sales Reporting

is around

All Products

(see Business

Behavioral Model)

- Customer Analysis

- Competitor Analysis

- Investment

Performance Tracking

MISC. PRODUCT

ANALYSIS

REPORTING

Budget & Rate Rise Reports

Corporate Culture &

Communication

Ad-hoc Reports FTE Reports

HCP & Claims Data

Matching & Cleansing

3M GROUPER

- Clearview

- MBF Life

FINANCIAL

SERVICES

Description

GROUP STRATEGY

AND NEW

BUSINESS

DEVELOPMENT

Regrouped DRG

Reporting on Investment, Regulatory, Budgeting, Pricing, Variances, EOM Provisioning, Claims Analysis

3 Cubes:

- Investment

- FTE

- Oracle GL

Based On Excel.

TM1

Data Formatting &

Consolidation

BASE SAS


MONTH END

DATABASE

Misc. Clearview &

MBF Life Reports


Misc. Clearview & MBF Life Reports

Funds Data

Sales & Membership

Data (via Flat File)

Call Center Management Reports (Weekly)

Customer Retention & Sales Reports

Sales & Membership Data By State excl. Tasmania (via Text File)

Membership Data (Monthly)


Financials & Balanced Score Card Data

Misc. Reports

Ancillary & Commissions Extract (Monthly)

Clinical & Business Analysis Data (Monthly)

Manipulated Clinical &

Business Analysis Data

Clinical & Business

Analysis DataManipulated Clinical & Business Analysis Data

HCP Data

Standard Claims & Membership Data

Hospital Details

from Hospital &

Benefit Management

Hospital Details

Claims & Revenue Data

Membership & Sales Reports (Weekly, Monthly)

Membership &

Sales Reports

Customer, Claims,

Membership & Sales Data

National Custodian

Services (NCS)

Outsourced

Modelling Agency

Prosthesis Provider Claims Data (Monthly)

Hospital Provider Claims Data (Monthly)

Ancillary Provider Claims Data (Monthly)

Synchronised Data

- Customer Profiling

- Data Mining

- Statistical Analysis

SPSS

Customer, Brand & Product Data

Customer Profiling & Demographic Data

Complete Claims History Data (Monthly) (via Flat File)

Standard Detailed Claims Data Revenue, Membership

& Claims Data

Levies, Revenue & Reinsurance Data (Monthly)

Misc. Data (via Dynamic Link)

Misc. Data

HCP (Monthly), Ad-hoc All Providers Claims, Financial, Membership Data (via Flat file from DW / Mainframe Domain)

HCP (Monthly), Ad-hoc Provider Contract / Modelling Data (from DW via Text file)

Sales &

Membership

Data

(from DW)

Report Query Data

Month End Claims Data

Claims Data

Clinical & Business

Analysis Reports

Clinical & Business Analysis Data

Tasmania Sales &

Membership Data

(from Diamond)

Membership & Claims Data Extract (from Diamond)

Misc Data

Claims Reports

P&L, Cost Centre OPEX (Monthly) (via OPEX Excel Spreadsheet)

Synchronised

Data

Claims & Medical Provider / Hospital Pricing Data (Monthly)

Medical Pricing Data (Monthly)

Internal Reporting (Membership, Claims, Sales Activity) (Weekly)

Summurisation of Revenue & Membership Data (Monthly)

Customer, Claims,

Membership &

Sales Data

Clearview Exit Reports (Weekly / Ad hoc) (via MS PowerPoint)



Executive Pack Reports

Executives Commentary

Satisfaction Surveys (via Excel)

Service Call Survey Data

Item/Category Data

General, KPI &

Performance Reports

Misc. Reports

(excl. Financial Services & IT)

Regrouped DRG

Hospital To Be Modelled

Synchronised Data

(Weekly)

Medical & Prosthesis Pricing (Monthly)Hospital Price Modelling & Ad-hoc Data

Business Intelligence

(Group Finance)

KPI / Balanced Score Card (via spreadsheet or flat file on J Drive)

Balanced Score Card

Data (Via J drive)

Exceptional

Request /

Reports

Services Utilization Reports

Misc Reports

Services Utilization Reports

PMP Compliance, Risk & Contract Reports (via Excel for Formatting)

Dashboard Data from all MBF Business Units

Customer Reports, Investment Performance Reports (Quarterly, Monthly)

Research. Data

Call Centre Data

- National Claims Reports (via Excel & Word) (Daily & Monthly)

- Member Services Reports (via Excel & Word) (Daily, Weekly & Monthly)

- Customer Enquiry, Complaints & Compliments Reports (via Excel & Powerpoint)

- Membership, Members’ Exits & Exit Interviews Reports

- Teams’ Performance Reports (via Powerpoint)

KPI (via MS Excel)

Customer Retention (Cancellations) & Sales Data (via MS Access)

Actuarial Reports

Customer Enquiry, Complaints & Compliments Reports (via Excel & Powerpoint) / Membership, Members’ Exits & Exit Interviews Reports

Membership Movements, Gains and Exits Data

EOM Aggregate Customer Feedback

Hospital Data

Industry & Regulatory Data (Quarterly)

FTE (via Excel),

Revenue, Claims &

Expenses, Account

Balances (Monthly)

GL

Journal

Investment Data (via Excel)

Ad-hoc Financial Data Extract (from DW / Mainframe Domain)

Census Data

Census Data

Propensity Modelling

Activity &

Analysis Data

Membership Report

MS Excel/Access

MISC WEBSITE

ANALYSIS TOOLS

Sales Reporting By Channel

Prosthesis Pricing Data (Monthly)

Sales Reports (Monthly)Ad-hoc Reports

Dashboard Suite of Reports (Monthly)

Membership Reports

Self-Service ReportsRetail, Corporate & Travel

Sales eBusiness Reports

from Web Channel

(Weekly, Monthly, Ad-Hoc)

Duplication, Confusion, Inefficient

Streamlined

Efficient

Effective

Self Service Model

Federated Service Model

Questions Regarding Data Ownership

• Unless there is a set of clearly defined data ownership and stewardship policies, there are bound to be some questions regarding responsibility, accountability and authority associated with auditing and reviewing the quality of data sets.

• This needs to be a collaboration between business and technical IT

• Needs to have the business understand the uses of the data

Application-Based Data Management

• The systems and applications that comprise an enterprise environment may be structured in way that the business manager for each system has authority over the information used within that system.

• Consequently, each application in isolation has its own requirements for data quality.

• In the development of an enterprise architecture, it is possible that application data may be used in ways never intended by the original implementers.

• This, in turn, may introduce new data quality requirements that may be more stringent than the original, yet there may be hesitation by the application teams to invest resources in addressing issues not relevant within their specific applications.

Administrative Authority

• In some instances, the information used in an application originates from a source that is outside of the application manager's administrative authority. For example, in an application that is used to aggregate information from many information partners, many of the data quality issues are associated with problems at the partner level, not within the aggregated system.

• Because the problems occur outside of the centralized administrative authority, even if the data is modified/corrected at the centralized repository, it does not guarantee that the next submission would not still include instances of the same problems.

Data Quality in an Advisory Role

• In application-oriented organizations, another impediment to data quality coordination relates to how one deals with improving the quality of specific data used within an application when that data is sourced from an external data supplier, and is consequently managed outside of the application manager's jurisdiction.

• Although in some organizations the project structures may already have an associated data qualtiy function, the more important issue is whether in practice all participants will cooperate with the data quality improvement process.

Data Quality as a Business Problem

• In many organizations, business clients assume that any non compliance with expectations results from data quality issues and needs to be addressed by the technical teams.

• However, in reality, the business rules with which the data appears to be noncompliant are associated with the running of the business.

• Consequently, those rules should be owned and managed by the business client as opposed to the technical team members, whose subject-matter expertise is less likely to be appropriate to address the problems.

Impact Analysis

• Anecdotal evidence may frequently inspire attitudes about requirements for data quality.

• However, in the absence of a true understanding of the kinds of problems that take place, the scope of the problem and the impacts associated with the problems, it is difficult to determine the proper approach to fixing the problem as well as eventually measuring improvement.

• There must be cooperation between business and IT to determine the scope of the problem

Reactive versus Proactive Data Quality:

• Most data quality programs are designed to react to data quality events instead of determining how to prevent problems from occurring in the first place.

• A mature data quality program determines where the risks are, the objective metrics for determining levels and impact of data quality compliance, and approaches to ensure high levels of quality.

• Once again the business and IT must work together to deliver this approach

Personal Objections

• While it is unlikely that any individual would specifically disagree with any of the data quality concepts that constitute an effective improvement program, that does not necessarily guarantee any specific individual's participation. Providing a clear business case that demonstrates how specific data quality issues impede the stated business objectives, as well as a discussion of the steps that need to be taken to address the problem, will make the decision to introduce the improvement very clear.

• Must be some guiding principles for development and approval of data quality guidelines,

• as well as approaches to integration of the best practices into the enterprise. Our approach has been to adapt data quality best practices within a documented guideline structure that conforms to internal policies and standards and can be approved through internal procedures. When a particular activity has been approved through the standard internal channels, it transitions from "guidance" into organizational policy, with all the compliance requirements that implies.

What is metadata…..

• As the term implies, metadata is data about data.

• Metadata is, of course, data as well, and has its own metadata (meta-metadata), implying that metadata exists at many levels of granularity.

• Metadata is generally defined in terms of usage as technical or business metadata.

– Technical metadata describes the technical aspects of the data such as field type, length, allowable values, and so on.

– Business metadata describes the business context of the data, such as: “This forecast data was submitted in march 2005 and includes revenue....”

Metadata – View by pattern:

• Syntactic metadata:

– Data describing the syntax of the data; for example, language syntax, format

• Structural metadata:

– Data describing the structure of the data; for example, document structures, DTDs, XML schemas, relational schemas

• Semantic metadata:

– Data describing the meaning of the data in a specific domain, such as definitions of “product,” “accounting period,” “total revenues”

Metadata – Another key concept is ontologies

• Data that formally specifies how to represent objects, concepts, and other entities in a domain of interest, as well as the relationships that bind them; for example, a taxonomy of typical data domains such as “product” and “market” and their relationships.

• Whereas taxonomy is simply the hierarchical structure or classification of the data (e.g., “product” attributes and their structure), ontology is an overarching term that includes not just the taxonomy, but also the relationships and mapping between the attributes.

•

• Therefore, ontologies will include not just entity structures and relationships, but acceptable values of the entity attributes, their synonyms, word variants, abbreviations, etc.

• Industry standard ontologies exist for common data domains (such as “product,” “country,” etc.) and provide a pre-built base of metadata for an enterprise.

• These ontologies can be adapted for specific enterprise needs, and mappings created for data structures across enterprise entities or divisions

Why Should Enterprises Care about Metadata Integration

• Data in a typical organization is scattered across multiple databases, files, and networks. To link data from disparate sources, enterprises have developed many levels of integration: business process integration, UI integration, application integration, etc.

• However, what is most critical to effective decision making and innovation is that the integrated data be accurate, complete, consistent, and timely, and be insulated from changes in underlying data structures.

• Metadata is what decouples integrated data from changes in granular data sources. Further, this metadata is most effective when shared across the enterprise, requiring enterprise metadata integration.

A Prerequisite for Defining an Enterprise Metadata Strategy

• Before you can think of implementing metadata, you need to build an awareness of data integration needs.

• Typically, an organization uses data either for operational needs or for business intelligence. For operational systems, source data may be relatively homogeneous (syntactically, semantically, and structurally) and data integration may not require extensive metadata validation.

• However, in complex data flows between operational systems, or systems providing business intelligence, metadata assumes much greater significance. Data typically flows through distinct steps in the organization’s information supply chain:

– · Acquisition: acquiring data from multiple sources

– · Consolidation: normalizing data within a given source

– · Aggregation: integrating data across multiple sources

– · Dissemination: delivering data to end users

• If these steps are not supported by an underlying metadata infrastructure, disseminated data will be degraded, inconsistent, inaccurate, and lack integrity. Therefore, it is imperative to identify sources of record for given data domains and optimize data platforms to serve one or more of the above functions.


• Key Requirements for successful metadata – Checklist.

Effectiveness

Provide users more useful information than they currently get

Extensibility

Allow metadata to be extended easily to reflect the addition of business such as a new product or market

Reusability

Provide reusable metadata across different business functions in an organization

Interoperability

Connect to disparate systems to glean metadata. The solution should also provide APIs or services to access metadata by different systems

Efficiency and Performance

Process a user’s metadata request in a timely and efficient manner

Evolution

Allow metadata built around one business unit or function at a time to be promoted to the enterprise

Entitlement

Allow delegated administration, data entry, and access

Flexibility

Allow for changes to the underlying data structures without affecting existing business processes

Segregation

Distribute and segregate metadata into several sub-systems, maintained at a business-unit level

User interface

Provide a friendly user interface

Versioning

Control metadata versions

Versatility

Deal with different types of metadata such as syntactic, structural, semantic, and ontological

Low maintenance cost

Provide ease of administration and a self-maintenance model

Others

Store metadata for semi-structured or unstructured data; transform metadata into different formats: human readable, machine readable, etc.; and analyze patterns and detect inconsistencies in stored metadata


Usability - What and Why

• Usability is the "make it or break it" of any BI initiative. Just because a BI tool is in place does not mean people will use it. If people do not use it, it does not matter how well the solution is designed or developed.

• The solution should have an intuitive user interface, enabling users to get the information they need with just a click - without calls to tech support. The BI tool should make operations easier, not more complicated. Ease-of-use drives early user acceptance and early adoption. An intuitive interface minimizes training and reduces costs in the long run. Most importantly, the simple interface maximizes use, so decision-makers will be more likely to use the system on day one.

• A usable BI solution will provide the ROI it should. The initiative must result in delivering something that can be used immediately to resolve today's problems and assist in accomplishing the business goals that have been set.

Questions

[email protected]

Data quality practical guide

Technology

Transcript of Data quality practical guide