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DATA QUALITY - Turning insights into actions throughout the entire data
cleansing process
An enterprise platform
for profiling, cleansing, augmenting, integrating and monitoring
data to create consistent and reliable information
Inaccurate and unreliable data affects all organizations. And with data volumes
constantly on the rise, it’s no wonder that improving data quality has become a
key concern for most. Many factors affect the ability to accurately consolidate
data and provide reliable information - increasing numbers of systems and
standards, third-party data that is not easily integrated, and duplicate data
and applications. In many cases, data standards are not available or are not
followed throughout the enterprise.
Many organizations are unaware of their data quality problems. Business
users may not even know their data is inaccurate —until something goes wrong.
When strategic, corporate projects fail to produce expected returns, the problem
can often be traced to redundancies and inconsistencies in data.
Data Quality is a critical factor for effective reporting and data
analysis. By integrating data quality within the Extraction, Transformation &
Loading (ETL) process, organizations can transform and combine disparate data,
remove inaccuracies, standardize on common values, parse values and cleanse
dirty data, to create consistent and trustworthy information.
SAS is the only vendor with a fully integrated offering in data quality
and ETL. Desktop cleansing tools and wizards provide the ability to quickly and
effectively analyze and identify data quality problems and integrate the
transformations into the ETL environment, which translates into consistent
information and fast ROI. Integrating data quality within business intelligence
solutions creates high-impact results and ensures an acceptable return on
investment.
SAS Data Quality
Solution has a strong Quality Knowledge Base which has an
Indian Locale.
As part of the Indian Locale of SAS Data Quality Solution, characteristics
such as ‘Names’, ‘Addresses’, ‘E-Mail’, ‘Organizations’, ‘Phone/Mobile Numbers’,
‘City, State/Province and Postal Code’, ‘Global definitions for Names,
Addresses, Website, Date, Text and Account Number’, have been specially
customized to operate on Indian data. To complement this, the data has also been
enriched with Indian phonetics.
The SAS Data Quality Solution aims at helping organisations in the Banking
& Financial Services, Insurance, Telecommunications, Retail, Manufacturing,
Pharmaceutical & Life Sciences, and Government sectors gain competitive
advantage by providing them with clean and accurate data for meaningful
analysis.
Key Features
- Easy-to-use interface for uncovering inconsistencies and report
inaccuracies in data.
- Robust server environment with the power to analyze data quality across
entire organizations.
- Match code generation, house-holding capabilities and address verification
in multiple languages and locales.
- Customization of parsing, standardization and matching algorithms.
- Self-documenting through integrated metadata.
Key Benefits
- Data Standardization
- Data can be standardized, which means that all occurrences of MUM, MUMI,
MUM90BAI, BOMBAY etc. can be consolidated to MUMBAI.
- Matching
- Customer records can be matched based on names, addresses, phone numbers,
organizations etc. This will enable organizations to have a single view of their
customers
across functional areas.
- De-duplication
- This enables Positive de-duplication for cross-sell and up-sell opportunities.
In addition to this,
Negative de-duplication can be carried out as a validation
while disbursing loans, offering
credit cards etc.
- House holding
- Customer names, addresses, telephone numbers etc can be taken as the
criteria for grouping
customers residing within the same household. In this
way single envelope policy or better
products for the family as a whole can be
identified.
- Parsing
- Breaking down the customer information into gainful chunks for analysis can be
carried out
through parsing. So we can identify the suburb, landmark,
house/society details and area
where a customer resides from his address. This
can be useful for generating a target list for a
promotional or marketing
campaign.
- Integrates and standardizes data across multiple systems and sources.
- Reduces redundancy in corporate data.
- Improves accuracy of decisions by ensuring a consistent version of the
truth
- Accelerates data cleansing by analyzing and eliminating duplicate and
inaccurate data as it is drawn through ETL processes.
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