Comparative Analysis of Data Cleaning Tools Using SQL

IJCAT - International Journal of Computing and Technology, Volume 3, Issue 7, July 2016
ISSN : 2348 - 6090
Comparative Analysis of Data Cleaning Tools Using
SQL Server and Winpure Tool
Dr. Abdelrahman Elsharif Karrar, 2 Moez Mutasim Ali
Taibah University
Saudi Arabia
University of Science and Technology
Abstract - Data cleaning based on similarities involves
identification of “close” tuples, where closeness is evaluated
using a variety of similarity functions chosen to suit the domain
and application. Current approaches for efficiently
implementing such similarity joins are tightly tied to the chosen
similarity function. In this paper, we compare between two
cleaning tools. The two cleaning tools considered are Microsoft
SQL Server2012 Data Quality Services and Winpure clean and
match software. Data Quality Services is a knowledge-based
system that performs both computer-assisted and interactive
cleansing and matching processes using the created knowledge
base. WinPure Clean & Match 2009 is the latest edition,
following on from the award-winning Clean & Match 2007. It
builds upon its data duplication module and now features
advanced fuzzy matching logic to identify and remove more
duplications. The comparison between the above two tools is
carried out using academic datasets, with the Weather dataset
as its input.
representations and elimination of duplicate information
become necessary. [2]
Databases and data warehouses require and provide
extensive support for data cleaning. They load and
continuously refresh huge amounts of data from a variety
of sources so the probability that some of the sources
contain “dirty data” is high. Furthermore, data warehouses
are used for decision making, so that the correctness of
their data is vital to avoid wrong conclusions. For instance,
duplicated or missing information will produce incorrect
or misleading statistics (“garbage in, garbage out”). Due to
the wide range of possible data inconsistencies and the
sheer data volume, data cleaning is considered to be one of
the biggest problems in data warehousing. [3]
2. Data Cleaning
Data cleaning also called data cleansing or data scrubbing,
is a process used to determine inaccurate, incomplete, or
unreasonable data and then improving the quality through
correction of detected errors and omissions. [4]
Keywords - Data Cleaning, Data Cleansing, Data Quality
Services, Winpure, Datasets.
1. Introduction
An organization in a data-intensive field like banking,
insurance, retailing, telecommunications, or transportation
or Universities might use a data cleaning tools to
systematically examine data for flaws by using rules,
algorithms, and look-up tables.
Data cleaning, also called data cleansing or scrubbing, deals
with detecting and removing errors and inconsistencies from
data in order to improve the quality of data. [1] Data quality
problems are present in single data collections, such as files
and databases, e.g., due to misspellings during data entry,
missing information or other invalid data. When multiple
data sources need to be integrated, e.g., in data warehouses,
federated database systems or global web-based information
systems, the need for data cleaning increases significantly.
This is because the sources often contain redundant data in
different representations. In order to provide access to
accurate and consistent data, consolidation of different data
Typically, a database cleaning tools includes programs that
are capable of correcting a number of specific types of
mistakes, such as adding missing zip codes or finding
duplicate records. Using a data cleaning tools can save
a database administrator a significant amount of time and
can be less costly than fixing errors manually.
IJCAT - International Journal of Computing and Technology, Volume 3, Issue 7, July 2016
ISSN : 2348 - 6090
large amount of metadata, such as schemas,
instance-level data characteristics, transformation
mappings, workflow definitions, etc. For
consistency, flexibility and ease of reuse, this
metadata should be maintained in a DBMS-based
repository. To support data quality, detailed
information about the transformation process is to
be recorded, both in the repository and in the
transformed instances, in particular information
about the completeness and freshness of source
data and lineage information about the origin of
transformed objects and the changes applied to
them. [5]
2.1 Data Cleaning Approaches
In general, data cleaning involves several phases
Data analysis: In order to detect which kinds of
errors and inconsistencies are to be removed, a
detailed data analysis is required. In addition to a
manual inspection of the data or data samples,
analysis programs should be used to gain metadata
about the data properties and detect data quality
problems. [5]
Definition of transformation workflow and
mapping rules: Depending on the number of data
sources, their degree of heterogeneity and the
“dirtiness” of the data, a large number of data
transformation and cleaning steps may have to be
executed. Early data cleaning steps can correct
single-source instance problems and prepare the
data for integration. Later steps deal with
schema/data integration and cleaning multi-source
instance problems, e.g., duplicates. The schemarelated data transformations as well as the cleaning
steps should be specified by a declarative query and
mapping language as far as possible, to enable
automatic generation of the transformation code. In
addition, it should be possible to invoke userwritten cleaning code and special purpose tools
during a data transformation workflow. The
transformation steps may request user feedback on
data instances for which they have no built-in
cleaning logic. [5]
Verification: The correctness and effectiveness of
a transformation workflow and the transformation
definitions should be tested and evaluated, e.g., on
a sample or copy of the source data, to improve the
definitions if necessary. Multiple iterations of the
analysis, design and verification steps may be
needed, e.g., since some errors only become
apparent after applying some transformations. [5]
Transformation: Execution of the transformation
steps either by running the ETL workflow for
loading and refreshing a data warehouse or during
answering queries on multiple sources. [5]
3. Data Cleaning Tools
Data cleaning tools are software’s applications that help
users to clean data by identifying incomplete, incorrect,
inaccurate, irrelevant, etc. parts of the data and then
replacing modifying or deleting this dirty data.
Most of the cleaning tools go throw the five phases that
mentioned in 2.1. Focusing more specifically on data
cleaning, there are many techniques in the research
literature, and many products in the marketplace. The
space of techniques and products can be categorized fairly
neatly by the types of data that they target. Here we
provide a brief overview for two data cleaning tools, using
academic data to clean it with these two tools, and at last
we compare the final results of these tools.
One of the most popular tools is Microsoft SQL Server
2012. Organizations can use SQL Server 2012 to
efficiently protect, unlock, and scale the power of their
data across the desktop, mobile device, datacenter, and
either a private or public cloud. SQL Server 2012 has
made a strong impact on organizations worldwide with its
significant capabilities. It provides organizations with
mission-critical performance and availability, as well as
the potential to unlock breakthrough insights with
pervasive data discovery across the organization. Finally,
SQL Server 2012 delivers a variety of hybrid solutions
you can choose from.
Microsoft SQL Server 2012 provides some services to deal
with the big data. One of these services is Data Quality
services which used to build a knowledge base and use it
to perform a data cleaning in different tasks, including
correction, enrichment, standardization, and de-duplication.
Backflow of cleaned data: After (single-source)
errors are removed, the cleaned data should also
replace the dirty data in the original sources in
order to give legacy applications the improved data
too and to avoid redoing the cleaning work for
future data extractions. For data warehousing, the
cleaned data is available from the data staging area.
The transformation process obviously requires a
There are some other data cleaning tools, the most known
is WinPure Clean & Match 2013. This is award-winning
list cleaning, data cleansing and data deduplication
software, because it offers a collection of affordable data
cleaning tools.
IJCAT - International Journal of Computing and Technology, Volume 3, Issue 7, July 2016
ISSN : 2348 - 6090
3.1 Microsoft SQL Server 2012 Data Quality
Data cleansing in Data Quality Services (DQS) includes a
computer-assisted process that analyzes how data
conforms to the knowledge in a knowledge base, and an
interactive process that enables the data steward to review
and modify computer-assisted process results to ensure
that the data cleansing is exactly as they want to be done.
The data cleansing feature in DQS has the following
Identifies incomplete or incorrect data in your data source
(Excel file or SQL Server database), and then corrects or
alerts you about the invalid data.
Figure (1) Cleaning in Data Quality Client [6]
Provides two-step process to cleanse the data: computerassisted and interactive.
A data quality project tends to have two
elements to it. One is an initial fix to clean
up bad data. This is known as a data
cleansing project. As the name implies, the
end goal is to have a set of clean data. The
tools look through the data, transforming
values to match a standard, flagging
outlying values that might be anomalies and
suggesting changes that could be made. It
also hunts for possible duplicates through
data matching, applying policies to look for
entries in the database that might refer to the
same thing.
The second part of a data quality project is
what happens next to keep the data clean. As
with Master Data Management, this isn’t a
fix-once act. It’s very easy for data quality
issues to creep back in after the cleansing
has taken place so an implementation of
Data Quality Services needs to bear in mind
what should happen next. The processes and
policies need to be defined to ensure that the
data quality knowledgebase is used in future
to maintain the quality of the data. It’s also
important to identify the data stewards who
will be responsible for fixing any problems
the knowledgebase flags.
It’s also important to think of the
knowledgebase as an on-going project. The
set of knowledge and rules within the
knowledgebase can grow over time,
bringing more control and accuracy to data.
As more data passing through the
knowledgebase, it becomes more tuned to
picking out anomalies and better at
identifying what the correct value should be.
The computer-assisted process uses the
knowledge in a DQS knowledge base to
automatically process the data, and suggest
Interactive allows the data steward to
approve, reject, or modify the changes
proposed by the DQS during the computerassisted cleansing.
Standardizes and enriches customer data by using
domain values, domain rules, and reference data.
For example, standardize term usage by changing
“St.” to “Street”, enrich data by filling in missing
elements by changing “1 Microsoft way Redmond
98006” to “1 Microsoft Way, Redmond, WA
Provides a simple, intuitive, and consistent wizardlike interface to the user to navigate data and
inspect errors amongst a very large set of data.
DQS categorizes the data under the following five tabs:
Invalid: values that failed a domain rule or
reference data.
New: Valid values for which DQS does not have
enough information.
Suggested: Values for which DQS found
Corrected: Values that are corrected by DQS.
Correct: Values that were found correct.
The following figure displays how data cleansing is done
in DQS:
IJCAT - International Journal of Computing and Technology, Volume 3, Issue 7, July 2016
ISSN : 2348 - 6090
A Data Quality Services project should
include both the plan for how to clean the
data initially and how to maintain quality
moving forward.
3.3 Comparing Data Quality Services with WinPure
Clean and Match
Microsoft Data Quality Services and WinPure Clean and
Match 2013 Software are two of the leading and powerful
tools in the data cleaning world, the Data Quality Services
is provided by Microsoft SQL Server 2012, and the
WinPure Clean and Match is provided by WinPure
Before either of these can start, however, we
need to define what we want our data to
look like. A key part of a data quality project
is working out what we want to be the
correct value. Once we’ve done that, we can
start applying the rules to change the other
values so you end up with consistency
across our whole data set.
Comparing between these two tools is so hard because
every tool have its own features.
So when we’re starting to work with Data
Quality Services, first take a look at our
existing data and decide what we’d like it to
look like. Then we can do data cleansing
and data matching to give ourselves a clean
and accurate set of data to start with. Then
we need to hook our knowledgebase into our
processes to ensure data quality moving
The final result of Data Quality Services goes throw the
following four stages:
• A mapping stage where users can identify the
data source to be cleansed, and map it to
required domains in a knowledge base.
Before creating data cleansing project must
have a relevant knowledge base to use in the
data quality project for the cleansing, after
creating a knowledge base project the
cleansing project can be created to use that
knowledge base project on it.
A computer-assisted cleansing stage where
DQS applies the knowledge base to the data to
be cleansed, and proposes/makes changes to the
source data.
An interactive cleansing stage where data
stewards can analyze the data changes, and
accept/reject the data changes.
The export stage that lets users export the
cleansed data.
3.2 WinPure Clean & Match 2013 Software
WinPure are a worldwide leading provider of
list and data quality solutions that are powerful,
simple to use, inexpensive and most
importantly can be used by anyone rather than
just IT specialists or data cleaning experts. [7]
WinPure Clean & Match 2013 is the latest
edition. It builds upon its data deduplication
module and new features advanced fuzzy
matching logic to identify and remove more
duplications. Already acclaimed for its easy-touse interface and powerful data cleansing
functions, WinPure have now added fuzzy
matching logic onto its data deduplication
module. It provides a range of data services
that are aimed at further improving data quality
and providing a clean starting point for ongoing
database management.
Businesses around the world are now using
WinPure software to help improve the quality
of their information, helping them to increase
profitability through more accurate data, and
reducing costs by eliminating duplications,
spelling errors and mistakes. [7]
In the third stage there is a confidence level value for the
suggested correct answers, this value is based on the
knowledge base that has been built in DQS against a highquality data set that was created before we start the
cleaning process.
Figure (2) Correct Confidence Level Value
After performing all the stages The DQS provides
statistics, these statistics about the source data and the
IJCAT - International Journal of Computing and Technology, Volume 3, Issue 7, July 2016
ISSN : 2348 - 6090
cleaning results that enable users to make informed
decisions about data cleansing, in the last step we export
the data and the final result was:
Figure (3) Data in SQL Server before Cleaning
Figure (5) Statistics Module
The second module is Text Cleaner module, a very
powerful module that will quickly and effectively remove
unwanted characters from data. At a press of a button, the
text cleaner automatically remove non-printable characters,
leading or trailing spaces, and even repetition of certain
characters. [8]
Figure (4) Data in SQL Server after Cleaning
The final result came by importing the academic data into
the WinPure Clean and Match 2013 software using , and
using two of the seven powerful cleaning modules that
provided by WinPure Clean and Match 2013 software, to
easily clean, correct, standardize and remove duplications
from these academic data in a matter of minutes, rather
than hours.
The first module used here is Statistics module, the main
idea of this module is to identify which filled/columns
have missing values, how much of data is fully populated
(eg. How many missing names or postcodes exist on the
data, how many contacts have missing email addresses,
etc). [8]
IJCAT - International Journal of Computing and Technology, Volume 3, Issue 7, July 2016
ISSN : 2348 - 6090
Figure (6) Text Cleaner Module
Figure (7) Data in WinPure after Cleaning
Table (1): Comparison between Data Quality Services and WinPure Clean and Match 2013 Software
Cleaning Tool Name
Import and
Performance time
Microsoft SQL Server 2012 Data
Quality Services
complex and go
throw several
go throw
several steps
depend on the
depend on the hardware
consideration and data size
WinPure Clean and Match 2013
easy and it’s just
a click of a
done with a
click of a
depend on the used
cleaning module
depend on data size
IJCAT - International Journal of Computing and Technology, Volume 3, Issue 7, July 2016
ISSN : 2348 - 6090
4. Discussion
In the Comparison between Data Quality Services (DQS)
and WinPure Clean and Match 2013 Software we depends
on four elements:
How each tool import and export the files.
How is the cleaning process flow in each tool.
The cleaning accuracy.
The cleaning performance time.
Referring to Table (1) we can infer that WinPure Clean
and Match 2013 Software takes less time in cleaning
process than Data Quality Services, because it didn’t give
consideration to the hardware.
Data Quality Services Provide several steps to begin the
cleaning process, in the other way WinPure Clean and
Match 2013 Software provide easy
Data Quality Services Provide options for an automated
process to clean the source data or manually go over the
cleansing results and fix issues that are found, in the other
way WinPure Clean and Match 2013 Software don’t
provide the manual option.
E. Rahm, "Data Cleaning: Problems and current
approaches", 2004.
V. G. Surajit Chaudhuri, Raghav Kaushik, "A
Primitive Operator for Similarity Joins in Data
Cleaning," 2006.
M. Li Lee, T. Wang Ling, Y. Teng Ko, "Cleansing data
for mining and warehousing", August 2003.
A. Chapman, "Principles and Methods of Data
Cleaning," July 2005.
M. Hellerstein, "Quantitative Data Cleaning for Large
Databases", February 27, 2008.
Microsoft®, "Data Quality Services", 2012.
D. Leivesley, "WinPure Clean & Match 2013,
Powerful Data Quality Software Featuring Advanced
Fuzzy Matching Data Deduplication," 2013.
WinPure®, "WinPure Clean & Match 2013," 2013.