Data Quality

SAP Data Services for Accurate Analytics: Overview Video

SAP Data Services for Accurate Analytics: Overview Video
Watch this video to learn how SAP Data Services extends the scope and reach of "Big Data" analytics by accessing relevant data regardless of data type and delivering complete, accurate, and trusted data - empowering both IT and business users to govern data.  

OBJECTIVES

 

Base your decisions on accurate, trusted data


    • Ensure trust in information by governing data quality, correcting issues during data movement, and defining policies to know when data is fit for use.
    • SAP Data Quality Management (DQM) software supports data quality management to help you make more-informed decisions and improve business process efficiencies with accurate, consistent, and complete data.
    • Missing information, lack of consistency, and incorrect values might also be subtly masking your organization’s true potential by choking efficiency and agility, opening the door to unnecessary risks and costs, and compromising decisions big and small.
    • If you lack consistent information, you lack the ability to make critical decisions that support compliance, innovation, and growth.
    • A carefully planned data quality initiative is essential to any successful data management initiative – be it a business intelligence (BI) or data warehousing (DW) project, a new implementation of a customer relationship management (CRM) system, or a data migration (DM) project.
    • Taking the first step to ensure your data is reliable and complete results in better, more confident decision making and more-efficient business processes.

SOLUTION

 

Deliver trusted data with data cleansing


    • With SAP Data Quality Management (DQM), data cleansing functionality lets you improve data by parsing, standardizing, and cleansing data from any source, domain, or type.
    • Often, multiple data elements are collected and stored in a database grouped into single fields.
    • Parsing identifies individual data elements and breaks them down into their component parts.
    • It rearranges data elements in a single field or moves multiple data elements from a single data field to multiple discrete fields.
    • After your data has been parsed, the next step is to ensure consistency across all your records.
    • This is necessary to prepare the data for validation, correction, and accurate record matching.
    • Standardization includes business rules around formats, abbreviations, acronyms, punctuation, greetings, casing, order, and pattern matching – all examples of elements you can control to meet your business requirements.
    • Data with incorrect elements is known as “dirty data.” Cleansing dirty data involves correcting it and adding missing elements.
    • Data cleansing can occur on a wide variety of data types.
    • Depending on the data type, you can remove or correct dirty data using sophisticated algorithms and rules in conjunction with referential data.
    • You can use address information obtained from the U.S. National Postal service to correct and standardize address data for CASS™, Zip+4®, LACSLink, NCOALink, SuiteLink, DPV®, and RDI™.
    • SAP Data Quality Management (DQM) software offers comprehensive global data cleansing coverage with support for over 230 countries.
    • Fully integrated data quality connectors for real-time and batch address cleansing and duplicate detection are available for SAP CRM & ECC, Siebel CRM & UCM and Informatica PowerCenter.  

 

Obtain greater insight and opportunity with enhanced data


    • Enhance data with internal or external sources to maximize the value of your data.
    • Data enhancement is the process of enriching your existing data set by appending additional data to it.
    • This provides a more complete view of your data that can help you, for example, more effectively target customers and prospects, take advantage of cross-selling opportunities, and gain deeper insights into your business.
    • With SAP Data Quality Management (DQM), enhancement options include:
      • Geocoding longitude and latitude information to records for marketing initiatives that are geographically or demographically based.
      • Geospatial assignment of customer addresses for tax jurisdictions, insurance rating territories, and insurance hazards.
      • Use of third-party referential data to enhance records.

 

Reveal potential issues with data matching and consolidation



    • Consolidate data to uncover hidden relationships and provide a single version of the truth.
    • Problems start when you have incorrect information about entities, such as invalid contact information; improper addresses, identification numbers, or ship-to or bill-to information; or incorrect material or product attributes.
    • Problems are exacerbated if duplicates enter the system.
    • It then becomes difficult to identify the correct entity to enter new information against and to verify even basic information such as how many customers you have, which products they own, and which products come from which suppliers.
    • Duplicate records often exist in one or more source systems; data matching can determine whether records refer to the same entity by evaluating how well the individual fields, or record attributes, match each other.
    • With SAP Data Quality Management (DQM), matching algorithms can help correct data entry errors, character transposition, and other data errors.
    • You can set rules based on combinations of various elements matching at a certain threshold – for example, you may require the address line information and the first-name information to match in order for records to be flagged as a possible match.
    • Once matches have been identified, data from these matched groups can be salvaged and posted to form a single best record or posted to update all matching records.

 

Using data profiling and data quality metrics to govern data



    • SAP Information Steward software helps you to understand and analyze the trustworthiness of your enterprise information and get continuous insight into the quality of your data.
    • It works in conjunction with SAP Data Quality Management (DQM) software and offers a specialized user interface that enables collaboration between IT and business for data profiling and metadata management.
    • Data profiling helps you:
      • Define and implement data policies, assess data quality, and remediate data problems.
      • Gain continuous insight about whether data is fit to use based on its quality.
      • Deepen understanding of data quality metrics with intuitive dashboards and scorecards
      • Data quality assessment is the inspection, measurement, and analysis of data.
      • By assessing data quality, your business users and data stewards can understand data defects and the impact of those defects upon the business.
      • Data profiling helps you to automatically recognize business rules and data relationships (across columns or fields) that might otherwise go unnoticed.
      • With SAP Information Steward, you perform the profiling directly within a current data warehouse or store, or you can load the data into the software and profile your data from there.
      • The software lets you establish data-range parameters and create automated alerts that will notify you if the analysis results exceed a specific parameter.
      • The data profiling functionality provides a rich user experience with concise analytic tools that help your people understand the impact of poor data.
      • With an easy-to-read dashboard of data benchmarks – such as integrity, uniqueness, conformity, completeness, and accuracy – both business users and IT personnel gain a better perspective on how data quality is impacting your organization.

BENEFITS

 

 

Making better decisions with reliable, trusted data


    • SAP Data Quality Management (DQM) enables you to improve data quality for more-effective decision making and business operations.
    • You can correct data issues as they arise and prevent quality issues before they occur.
    • SAP DQM features can be used in real-time or batch processing.
    • SAP DQM is built on Web services, so you can use the functionality within a wide variety of applications, platforms, and databases – including SAP, third-party, or proprietary software.
    • With SAP Data Quality Management (DQM) and SAP Information Steward (IS), you can:
      • Define and implement data policies, assess data quality, and remediate data problems.
      • Deepen understanding of data quality metrics with intuitive dashboards and scorecards.
      • Improve data by parsing, standardizing, and cleansing data from any source, domain, or type.
      • Enhance data with internal or external sources to maximize the value of your data.
      • Consolidate data to uncover hidden relationships and provide a single version of the truth.

 

 

Maximize the return on your data


    • SAP Data Quality Management (DQM) offers data management functionality that provides highly advanced data quality support.
    • You can use the software to help deliver the accurate and complete information that your business requires.
Firstlogic, Firstlogic Solutions, ACE, DataRight IQ and Match/Consolidate are registered trademarks of Firstlogic Solutions, LLC.  Postalsoft is a registered trademark of SAP America, Inc.  SAP America, Inc. is a certified licensed NCOALink Interface Distributor of the United States Postal Service®. The following trademarks are owned by the United States Postal Service: CASS, CASS Certified, DPV, RDI, eLOT, First-Class, DSF2, LACSLink, NCOALink, SuiteLink, USPS, U.S. Postal Service, United States Postal Service, United States Post Office, ZIP, ZIP + 4, ZIP Code.