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Lecture-23
Total DQM
TDQM in a DWH
Many DW projects do not deliver to full potential because they treat data quality as a one -time
undertaking as part of user acceptance testing (UAT) and then forgetting a out it. It is very
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important that data quality management is undertaken as a continuous improvement process...not
a one-shot deal!
Use an iterative approach to achieve data quality. Start by defining the quality measurements, and
then take the measurements. Go through the cycle as shown in Figure 23.1, this will drive you to
the stable stage i.e. CMM -5, where you have a small amount of acceptable data quality defects.
Here acceptable is based on cost vs. the value.
Data Quality Management Process
Figure-23.1: Data Quality Management Process
1. Establish Data Quality Management Environment
Securing a commitment to the Data Quality Management process is accomplished by establishing
the data quality management environment between information system project managers and
establishing conditions to encourage team work between functional and information system
development professionals. Functional users of legacy information systems know data quality
problems of the current systems but do not know how to systematically improve existing data.
Information system developers know how to identify data quality problems but do not know how
to change the functional requirements that drive the systemic improvement of data. Given the
existing barriers to communication, establishing the data quality environment involves
participation of both functional users and information system administrators.
2. Scope Data Quality Projects & Develop Implementation Plan
For each data quality analysis project selected, the data quality project manager defines the scope
of the project and defines the level of analysis that will be the most beneficial for the project
under question. Draft an initial plan that addresses the following elements.
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Task Summary: Project goals, scope, and potential benefits
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Task Description: Describe data quality analysis tasks
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Project Approach: Summarize tasks and tools used to provide a baseline of existing data
quality
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Schedule: Identify task start, completion dates, and project milestones
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Resources: Identify resources required to complete the data quality assessment. Include
costs connected with tools acquisition, labor hours (by labor category), training, travel,
and other direct and indirect costs
3. Implement Data Quality Projects (Define, Measure, Analyze, Improve)
A data quality analysis project consists of four activities. The data quality project manager
performs these activities with input from the functional users of the data, system developers, and
database administrators of the legac y and target database systems.
·  Define: Identify functional user data quality requirements and establish data quality
metrics.
·  Measure: Measure conformance to current business rules and develop exception reports.
·  Analyze: Verify, validate, and assess poor d ta quality causes. Define improvement
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opportunities.
·  Improve: Select/prioritize data quality improvement opportunities.
Improving data quality may lead to changing data entry procedures, updating data validation
rules, and/or use of company data standards to prescribe a uniform representation of data used
throughout the company.
4. Evaluate Data Quality Management Methods
The last step in the Data Quality Management process is to evaluate and assess progress made in
implementing data quality initiatives and/or projects. All participants in the Data Quality
Management process (functional users, program managers, developers, and the Office of Data
Management) should review progress with respect to: (1) modifying or rejuvenating existing
methods to data qualit y management and/or (2) determining whether data quality projects have
helped to achieve demonstrable goals and benefits. Evaluating and assessing data quality work
reinforces the idea that Data Quality Management is not a program, but a new way of doing
business.
The House of Quality
In 1972 the Mitsubishi Shipyards in Kobe developed a technique in which customer wants were
linked to product specifications via a matrix format as shown in Figure 23.2. This technique is
known today as The House of Quality and is one of many techniques of Quality Function
Deployment, which can briefly be defined as "a system for translating customer requirements into
appropriate company requirements".
The purpose of the technique is to reduce two types of risk. First, the risk that the product
specification does not comply with the wants of the predetermined target group of customers.
Secondly, the risk that the final product does not comply with the product specification.
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The House of Quality Matrix
Technical Correlation
Matrix
Customer
Technical Design
Requirements
Requirements
Interrelationship
Matrix
Figure-23.2: The House of Quality Matrix
The House of Quality Data Model
The House of Quality concept was used and introduced with reference to data quality in 1988.
The idea is that the end user applications and the data characteristics for those applications such
as date of birth etc are tied together, such that for each application tolerances on data quality are
placed, including no needs. If the tolerances are not within (say) x%, then the corresponding data
can not be used. So we are looking at:
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Establishing specifications for data in the data warehouse.
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Establishing applications for data in the data warehouse.
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Establishing tolerances for conformity to specifications for each combination of
applications and data specifications.
If it costs more to deliver th e tolerance than the value of the application then the company is not
ready to do the application yet. It means the company has to make a business decision not to do
the application with dirty data, or to do the application and pretend that the data is of high quality.
The House of Data Quality: Next Step
Getting more sophisticated, we can say that if you give me data withx% data quality, the value of
the application will be y millions of dollars. If you give me 2x% data quality, then the value of the
application is 3y millions of dollars. Why? Because now the predictive model is more accurate.
If you give me date of birth 90% of the time, then I can make you a million dollars on better
customer retention. But if you give me date of birth 95% of the time, then the accuracy goes
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much higher, and how much is that worth to you? The question is. What is the value of the
application, relative to the specific data quality specifications?
How to improve Data Quality?
The four categories of Data Quality Imp rovement
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Process
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System
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Policy & Procedure
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Data Design
Process Improvement: Improve the functional processes used to create, manage, access, and use
data. Functional process changes may encourage centralized data entry, eliminate non -value
added activities, and place data quality responsibilities where data is entered into the data set
(e.g., certification of data)
System Improvement: Software, hardware, and telecommunication changes can improve data
quality. For example, security software can minimi ze damage done by malicious updates to
databases by unauthorized users. Hardware improvements may make batch loads faster and
thereby make it unnecessary to turn off edit and validation constraints when loading data to a
database. Telecommunications improv ements (e.g. increasing bandwidth) may provide easier
access to data and improve both the accuracy and timeliness of data. Other system improvements
may include updating end user, operation, and maintenance manuals, and providing additional
user training.
Policy and Procedure Improvement: Resolve conflicts in existing policies and procedures and
institutionalize behaviors that promote good data quality. Develop Standard Operating Procedures
for the information system to document the data quality rule sets/filters used to measure data
quality. Perform periodic data quality checks as part of the Standard Operating Procedures to
increase data quality.
Data Design Improvement: Improve the overall data design and use data standards. Adding
primary key constraints, indexes, unique key constraints, triggers, stored functions and
procedures, controlling administration of user privileges, enforcing security features, and
referential integrity constraints can improve database design.
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Quality Management Maturity Grid
Table-23.1: Quality Management Maturity Grid
Table 23.1 shows the quality management maturity grid. Each of the five stages will be discussed
in detail.
Stage 1: Uncertainty
Stage-1 is denial. People don't think data quality is an issue. It is the responsibility of the people
collecting the data. It is their problem. There is no data quality inspection; nobody actually cares
about the data quality in the processes. The problems are handled on a fire fighting basis, you
discover problems (or they pop up) and you fix them using a band aid approach and the problems
are rarely resolved. There are a large number of companies in this stage. Some may not even have
a quality department, or even when they have one, not paying enough attention and committin g
resources.
Stage 2: Awakening
People think that data quality management does have a value. Management talks about it, but
does nothing. This is like all talk and no action stage. People go around giving presentations, but
really there is no action, just motivational stuff and not doing any thing. If there is a major
problem, the approach is to put together an ad hoc team, that team will attack and resolve the
problem. In the end they say that somebody messed up.
Stage 3: Enlightenment
A light goes on, may be there is a problem. The quality department starts reporting instead of
hiding the problems in the closet. You actually start to measure data quality and are reasonably
close. Actually the management is now committed, and have invested resources.
Stage 4: Wisdom
Value of quality is seen as something that is needful and meaningful. Data quality starts coming
on the performance evaluation of employees i.e. what are they doing for data quality. There is a
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14 step program for continuous improvement. They actually start going through it, and start
taking proactive actions to prevent data quality defects from happening. So rather than fixing
problems AFTER they have occurred, you start taking steps BEFORE the problems occur i.e.
practicing prevention. Quality comes from prevention. And prevention is a result of training,
discipline, example, leadership, and more. So there is a fundamental shift. Now you are looking at
organizational processes, and changing them.
How do you know an organization is in stage 4? They should have looked at their data quality
processes and found out why data quality problems are reoccurring and fixed them. If they are
still discovering data quality problems, then certainly they are not in stage -4.
There are actually a very small number of organizations in stage-4. Usually they have a DW in
place and discovered how bad the quality of their data is before they start moving on this maturity
grid.
Stage 5: Certainty
The approach is "I know about the quality of my data" and/or "I am measuring it for real".
Quality improvement is NOT something that is done on ad-hoc basis, but is part of everything.
People know why they are doing it, and why they do not have data quality problems.
Misconceptions on Data Quality
1. You Can Fix Data
2. Data Quality is an IT Problem
1. You Can Fix Data
Fixing implies that there was something wrong with the original data, and you can fix it once and
be done with it. In reality, the problem may have been not with the data itself, but rather in the
way it was used. When you manage data you manage data quality. It's an ongoing process. Data
cleansing is not the answer to data quality issues. Yes, data cleansing does address some
important data quality problems has and offers a solid business value ROI, but it is only one
element of the data quality puzzle. Too often the business purchases a data cleansing tool and
thinks the problem is solved. In other cases, because the cost of data cleansing tools is high, a
business may decide that it is too expensive for them to deal with the problem.
2. Data Quality is an IT Problem
Data quality is a company problem that costs a business in many ways. Although IT can help
address the problem of data quality, the business has to own the data and the business processes
that create or use it. The business has to define the metrics for data quality - its completeness,
consistency, relevancy and timeliness. The business has to determine the threshold between data
quality and ROI. IT can enable the processes and manage data thro ugh technology, but the
business has to define it. For an enterprise-wide data quality effort to be initiated and successful
on an ongoing basis, it needs to be truly a joint business and IT effort.
Misconceptions on Data Quality
3. All) Problem is in the Data Sources or Data Entry
4. The Data Warehouse will provide a single source of truth
5. Compare with the master copy will fix the problem
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3. The Problem is in the Data Sources or Data Entry
Data entry or operational systems are often blamed for data quality problems. Although
incorrectly entered or missing data is a problem, it is far from the only data quality problem. Also,
everyone blames their data quality problems on the systems that they sourced the data from.
Although some of that may be true, a large part of the data quality issue is the consistency,
relevancy and timeliness of the data. If two divisions are using different customer identifiers or
product numbers, does it mean that one of them has the wrong numbers or is the problem on e of
consistency between the divisions? If the problem is consistency, then it is an enterprise issue,
not a divisional issue. The long-term solution may be for all divisions to use the same codes, but
that has to be an enterprise decision.
4. The Data Warehouse will provide a Single Version of the Truth
In an ideal world, every report or analysis performed by the business exclusively uses data
sourced from the data warehouse - data that has gone through data cleansing and quality
processes and includes constant interpretations such as profit or sales calculations. If everyone
uses the data warehouse's data exclusively and it meets your data quality metrics then it is the
single version of the truth.
However, two significant conditions lessen the likelihood that the data warehouse solves your
data quality issues by itself. First, people get data for their reports and analysis from a variety of
data sources - data warehouse (sometimes there are multiple data warehouses in an enterprise),
data marts and cubes (that you hope were sourced from the data warehouse). They also get data
from systems such as ERP, CRM, and budgeting and planning systems that may be sourced into
the data warehouse themselves. In these cases, ensuring data quality in the data warehouse alone
is not enough. Multiple data silos mean multiple versions of the truth and multiple interpretations
of the truth. Data quality has to be addressed across these data silos, not just in the data
warehouse.
Second, data quality involves the source data and its transformation into information. That means
that even if every report and analysis gets data from the same data warehouse, if the business
transformations and interpretations in these reports are different then there still are significant
data quality issues. Data quality processes need to involve data creation; the staging of data in
data warehouses, data marts, cubes and data shadow systems; and information consumption in the
form of reports and business analysis. Applying data quality to the dat a itself and not its usage as
information is not sufficient.
Normally source system reconciliation is used to check if the ETL is working properly. This
technique is useful when you are doing test and validation of the ETL, but is not recommended on
a long term basis. This will only be done when major changes are made in the ETL. Since the
process is manual, it is human intensive, so it will be very expensive and inefficient.
All the other four techniques discussed in the previous sections can be automat ed by writing
scripts, and running them every month to collect statistics, generating reports, and identifying
problems and subsequently noting the progress of quality control. But one has to be very clever to
write scripts for source system reconciliation . It can turn out to be a nightmare. The main
problems are that you may have garbage in the DWH, garbage in the legacy system, and that does
not really tell you about the data quality. Recall Orr's Law #4 - "Data quality problems increase
with the age of t he system!"
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TDQM: Successful Data Sourcing
Figure-23.3: TDQM: Successful Data Sourcing
If you take data from downstream data sources it's likely that not only you are going to
accumulate the data quality defects of the upstream sources, but also the compromises that the
downstream may have added, especially the summarizations. Once you summarize, you can not
trace back to detailed data. Go as high upstream for the gold copy of the data as possible as
shown in Figure 23.3.
You also h ave to be aware of the synchronization issues. You take the transaction data and the
accounts data and there are some transactions that don't exist in the accounts yet, so you have to
be careful of the order in which you take the data for synchronization. Synchronization is
probably the biggest reason we have referential integrity problems in a DWH i.e. lack of
synchronization between different extracts of the source system.
Misconceptions on Data Quality
It came from the legacy system so must be correct.
Another misconception is, "It came from the production transaction processing environment, so it
must be correct." However the reality is, the elements required for decision support are often
quite different than those required for transaction process ing. The objective of OLTP systems is
to ensure that all the sales data is in, the numbers sum up correctly, accounts are balanced etc. If
the gender data for some (or even most) sales is not present, it does not change the sales dollars.
Why bother? Why not enter some junk data in the gender field? This may be fine from the
operational point of view, but critical for decision support, when you are interested in determining
your market segmentation.
Redefines and other embedded logic in legacy programs often puts data quality delivered to data
warehouse in severe question. Y2K is a good example.
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Table of Contents:
  1. Need of Data Warehousing
  2. Why a DWH, Warehousing
  3. The Basic Concept of Data Warehousing
  4. Classical SDLC and DWH SDLC, CLDS, Online Transaction Processing
  5. Types of Data Warehouses: Financial, Telecommunication, Insurance, Human Resource
  6. Normalization: Anomalies, 1NF, 2NF, INSERT, UPDATE, DELETE
  7. De-Normalization: Balance between Normalization and De-Normalization
  8. DeNormalization Techniques: Splitting Tables, Horizontal splitting, Vertical Splitting, Pre-Joining Tables, Adding Redundant Columns, Derived Attributes
  9. Issues of De-Normalization: Storage, Performance, Maintenance, Ease-of-use
  10. Online Analytical Processing OLAP: DWH and OLAP, OLTP
  11. OLAP Implementations: MOLAP, ROLAP, HOLAP, DOLAP
  12. ROLAP: Relational Database, ROLAP cube, Issues
  13. Dimensional Modeling DM: ER modeling, The Paradox, ER vs. DM,
  14. Process of Dimensional Modeling: Four Step: Choose Business Process, Grain, Facts, Dimensions
  15. Issues of Dimensional Modeling: Additive vs Non-Additive facts, Classification of Aggregation Functions
  16. Extract Transform Load ETL: ETL Cycle, Processing, Data Extraction, Data Transformation
  17. Issues of ETL: Diversity in source systems and platforms
  18. Issues of ETL: legacy data, Web scrapping, data quality, ETL vs ELT
  19. ETL Detail: Data Cleansing: data scrubbing, Dirty Data, Lexical Errors, Irregularities, Integrity Constraint Violation, Duplication
  20. Data Duplication Elimination and BSN Method: Record linkage, Merge, purge, Entity reconciliation, List washing and data cleansing
  21. Introduction to Data Quality Management: Intrinsic, Realistic, Orr’s Laws of Data Quality, TQM
  22. DQM: Quantifying Data Quality: Free-of-error, Completeness, Consistency, Ratios
  23. Total DQM: TDQM in a DWH, Data Quality Management Process
  24. Need for Speed: Parallelism: Scalability, Terminology, Parallelization OLTP Vs DSS
  25. Need for Speed: Hardware Techniques: Data Parallelism Concept
  26. Conventional Indexing Techniques: Concept, Goals, Dense Index, Sparse Index
  27. Special Indexing Techniques: Inverted, Bit map, Cluster, Join indexes
  28. Join Techniques: Nested loop, Sort Merge, Hash based join
  29. Data mining (DM): Knowledge Discovery in Databases KDD
  30. Data Mining: CLASSIFICATION, ESTIMATION, PREDICTION, CLUSTERING,
  31. Data Structures, types of Data Mining, Min-Max Distance, One-way, K-Means Clustering
  32. DWH Lifecycle: Data-Driven, Goal-Driven, User-Driven Methodologies
  33. DWH Implementation: Goal Driven Approach
  34. DWH Implementation: Goal Driven Approach
  35. DWH Life Cycle: Pitfalls, Mistakes, Tips
  36. Course Project
  37. Contents of Project Reports
  38. Case Study: Agri-Data Warehouse
  39. Web Warehousing: Drawbacks of traditional web sear ches, web search, Web traffic record: Log files
  40. Web Warehousing: Issues, Time-contiguous Log Entries, Transient Cookies, SSL, session ID Ping-pong, Persistent Cookies
  41. Data Transfer Service (DTS)
  42. Lab Data Set: Multi -Campus University
  43. Extracting Data Using Wizard
  44. Data Profiling