Access to Good Quality Data Saves Time and Resources
With “the world’s information doubling every two years,” data has overtaken intuition as the key advantage to drive business growth and increase market share. Numerous sources indicate that a large percentage of mission-critical systems —such as ERPs, CRM and data warehouses — are not producing full value due to faulty data.
High volumes of data can increase the likelihood of ‘dirty’ or poor quality data which can negatively impact your business. Incomplete and inconsistent data such as having incorrect customer, product and financial data can lead to problems such as duplicate marketing mail-outs, unpaid invoices, misidentified customers, and duplicate payments. This can potentially lead to lost revenue, stalled operations and compromised credibility.
CaseWare™ Analytics Data Quality Module (DQM)
CaseWare Analytics DQM helps businesses to manage data effectively by automating the data quality process, ensuring that all data is accurate and complete. The solution helps organizations embed data quality directly into their operations to serve a greater good, such as monitoring the controls of core business processes.
The solution works to transform your data from all sources in four stages: Profiling, Parsing, Matching and Cleansing. After your data is transformed, CaseWare Analytics DQM ensures ongoing data quality by continuously monitoring both existing and new data, and by employing early detection systems. CaseWare Analytics DQM also allows for a collaborative approach to data quality, alerting multiple people within your organization to data quality issues, so they can be corrected.
- High Quality & Consistent Data
With constantly reliable data, data analytics can provide tremendous insights to help prevent revenue leakage and improve business operations.
- Accurate View of Operations
Ensure reliable, consistent and timely data that provides a true snapshot of your business at all points in time. This helps to improve decision making and achieve greater revenue potential.
 IDC 2011 Digital Universe Study
Get a complete picture of the accuracy of your data by assessing and validating your data against approved data standards.
Cleanse your data by standardizing, de-duplicating, validating addresses and geocoding to prevent billing, payment or inventory errors. Data is checked against rules set by you, and is corrected accordingly.
- Standardizing corrects inconsistently entered data or data with no structure.
- De-duplicating purges duplicate records to create a single instance of the relevant data.
- Address validating automatically finds postal codes and zip codes for Canada, the U.S., and more than 240 other countries.
- Geo-coding pinpoints the exact geographic location of a customer or product in your database.
Scheduled and automated monitoring of your data helps to maintain its quality, prevent future errors and improve data collection processes that can mitigate against having ‘dirty’ data in your system.
Integrated Workflow for Remediation
Once data quality issues are identified, alerts (equipped with resolution guidelines) are automatically sent to the business stakeholder who can resolve the issues. The workflow engine is completely configurable and will result in a streamlined process including multiple levels of escalation, setting deadlines, etc.
Cross-Matching Data from Multiple Sources
- Cross-match data from multiple sources that share common entities (customers, suppliers, etc.)
- Identify, link or merge related entries
- Consolidate data to correctly identify one customer who is entered into the database multiple times
- Group similar data from multiple sources about a person, family, household or company
- Split full names into component parts
- Standardize name prefixes, including honorifics, professional titles and salutations
- Assign gender to names through probabilistic gender assignment
- Add custom name files for cultural name differences and known aliases
- Find postal codes and zip codes for the U.S Canada and more than 240 other countries automatically
- Identify name and address parts in non-standard addresses
- Standardize data entered inconsistently, improperly fielded or with no structure
- Create unique records to avoid errors
- Purge duplicate records
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