Enterprise Data Management

Transcription

1 Enterprise Data Management A Comprehensive Data Approach for CSPs Sivaprakasam S.R. Enterprise Data Management (EDM) helps Communication Service Providers (CSPs) address the challenges caused by convergence of technologies and frequent mergers and acquisitions. It provides a single view of the truth, unique reference data and a unified data quality framework to integrate, validate and migrate data. In addition, it enables continuous monitoring of the quality of data and establishes standards across the enterprise data lifecycle. Our white paper discusses the need for EDM in the telecommunications sector, the benefits and challenges in implementing EDM, and the components of an effective enterprise data management solution. Aug 2010

2 Executive Summary Mergers and Acquisitions (M&As), convergence of technologies and rapid changes in global regulations are revolutionizing the telecommunications industry. To address the dynamic market conditions as well as the competition from established companies and new entrants, Communications Service Providers (CSPs) must constantly provide new features and opportunities to customers. In addition, they must foresee market changes and act decisively by capitalizing on their primary asset operational and analytical data for precise metrics and predictions. However, CSPs must implement and govern enterprise data effectively to meet their business intelligence needs. Enterprise Data Management Enterprise Data Management (EDM) helps CSPs manage heterogeneous data sources, validate the quality of data, devise a common data model by integrating information, build analytical and presentation layers, and manage end-to-end metadata in the analytical and presentation layers. EDM also guides and governs the data architecture, while managing data assets. A typical EDM architecture diagram is depicted in Figure 1. Source Data Data Integration Target Data Data Services End User Layer DQ / Exception Rule Engine Error Logging Sn Excep on Audit Repository Excep on OLAP DB Business Analytics, Score card and Dashboard Reporting Enterprise Service Bus Ra onalized Systems Enterprise Information Portal Data Profiling Data Cleansing Data Validation Data De-duplication Exception Investigation Shared Business Services Operational Data Store (ODS) S3 BPM Workflow Engine Enterprise Data Model ETL / EAI TOOL S2 Shared Security Services Mapping Metamodel Shared Data Services S1 Business Rules Metamodel Enterprise Data Warehouse DQ Rules Metamodel Downstream DB Enterprise Metadata Repository Intranet Customers End Users Helpdesk Decision Makers Enterprise Data Governance Figure 1: Typical enterprise data management architecture Benefits of EDM EDM ensures consistency of information with a single version of truth by providing reference data requirements on an integrated data platform. It supports operations and enhances decision-making capabilities by helping CSPs migrate from disparate data silos to an integrated, enterprise-wide data environment. 2 Infosys View Point

3 EDM delivers several benefits - Provides a single, accurate view of end-to-end enterprise data Consolidates, profiles and integrates data from disparate systems, thereby enhancing Data Quality (DQ) and eliminating exceptions. It increases the trust factor of data. Insulates systems and processes from change, enabling rationalization of legacy systems and facilitating mergers and acquisitions Improves the accuracy of decisions Enables on-demand extraction of ad hoc operational reports Facilitates rapid analysis of customer retention, customer satisfaction, customer lifetime value, and cross-sell and upsell data Enables risk-benefit analysis of business opportunities Enables reuse of the EDM framework in new regions, markets and product categories Dimensions of Enterprise Data Management Enterprise Data Management Enterprise Metadata Management Enterprise Data Model Data Standard Data Security Data Quality Management Data Architecture Data Stewardship Enterprise Data Governance Figure 2: Enterprise data management framework The components of an enterprise data management framework are shown in Figure 2, and are discussed in subsequent sections. Enterprise data can be managed along various angles/ layers to meet the goals of EDM: Data stewardship Data governance Data standards Data definitions and taxonomies Technology standards Data retention Data quality management Data architecture Data integration Data migration Master/ reference data management Metadata management Data warehousing Data portal Data security Infosys View Point 3

4 Data Stewardship EDM requires the owners of source data to manage data assets effectively. A data steward s responsibilities include managing data standards, formats and trust factor, and establishing and enforcing data standards. Data quality management provides quality analysis reports that enable stewards to improve the quality of data, reduce data redundancy and improve data management capabilities across the enterprise. Source data owners provide the overall governance and oversight to the data management activities within an enterprise, while data stewards - Define business terms and establish rules for quality and exception validation Identify business-critical attributes and ensure data validity Prioritize and ensure data quality of exceptions raised by the quality management team Coordinate between business, application and IT teams to enhance the trustworthiness of data Address target and downstream data requirements Create awareness about data management principles, data security and retention policy, and best practices across the enterprise Data Governance A data governance committee must be instituted to develop the principles of managing data-related processes and enforcing them across the enterprise. It must be responsible for nominating teams for programs such as data stewardship, data quality management, data modeling, data integration, data migration, data warehousing, master data management, data architecture, data security, and metadata management. A data governance framework that focuses on people, process and technology ensures accessibility, availability, quality, consistency, security, and audit-readiness of data. Typically, senior managers form the steering committee, while the operational team of the data governance committee includes business users, data architects and data stewards. Data governance plans are framed by a working group and approved by the steering committee. The committee must ensure that data assets are managed effectively across the enterprise. Standards Standards and and Rules Rules - Approvals Approvals Senior Management Steering Committee Working Group Data Data Governance Governance Management Management Data Architect Data Steward Business User Figure 3: Data governance structure Key roles and responsibilities of the data governance committee: Steering committee Articulate the vision and arrange funds for the data governance initiative Source data owners Prioritize and execute data management Data stewards Address issues in data quality and standards such as merger or deletion of data, data enrichment, etc. Data stewards must ideally be from the business side. Data architects Help data stewards access, integrate and manipulate data with their technical expertise 4 Infosys View Point

5 Data Standards Data standards are framed by the data governance steering committee to ensure that all data elements of an enterprise comply with standard terms, definitions and values. The working group, in turn, ensures that all associated parties agree with the data values and adhere to the standards. It helps the data stewards frame validation rules to filter business exceptional data and improve its trustworthiness. DQ issues identified by validation against standards must be closely monitored and tracked by data stewards until resolution. By ensuring that each and every data element of the enterprise data model adheres to the rules and definitions of the SID model1, CSPs can meet the data requirements of emerging technologies such as wireline, wireless, cable, and IPTV. In addition, they can avoid redundant data and ensure consistency of data. Data elements can be classified based on their logical and physical properties. Logical property refers to the definition, origin and data type. Physical properties of the data element include data length, validation rules, and how data is stored, presented to end users and labeled. For example, elements such as the date and currency fields have specific display formats, standards, regional settings, and validations. Data Quality Management Data Quality Management (DQM) is a key enterprise data management process that addresses issues in the quality of data, and identifies exceptions in data elements that can be classified into industry standard quality dimensions as shown in Figure 4. It requires strategic data profiling and data quality software to be a part of the data management process. DB Connec vity Configuring Validation Rules DQ Analysis Completeness Does it provide all the information required? Is it within acceptable parameters for the business? Validity Is it relevant for its intended purpose? Data Steward Analysis Is it up-to-date and available whenever required? Uniqueness Relevance Timeliness Are the values repeated? Monitor DQ Metrics Consistency Is it consistent and easily understood? Is it correct, objective and can it be validated? Accuracy Figure 4: Classification of exceptional data elements 1 The Shared Information/ Data Model (SID), framed by the TM Forum, provides a single set of terms for business entities and attributes across the telecommunications industry. SID enables business users to use the same terms to describe business objects, practices and relationships across the enterprise. Infosys View Point 5

6 The quality of data may be affected at various stages of the data element lifecycle such as data entry, data transformation, conversion from operational to analytical data, master data and reference data transformation, and migration of data from legacy systems. Some practical examples of anomalies in data quality: Accuracy Mary Pierce, 1408 North Any Street, Germantown, MD, Is this a valid postal address? Consistency Alan Smith, 4200, Weston Park, Cary, NC - Is there a suffix to the street? - Is there a postal code? Completeness Street => St, Str Account => Acct, Accnt, A/c - Are they business terms with variations in value? Correctness Mary Brown, 1408 North Any street, Germantown, MD, 9AB Is this a valid zip code format? Uniqueness First Merit Bank and 1st Merit Bank - Are they the same or different Relevance Robert Smith, 123 Peach Tree lane, Pleasanton CA Julia Smith, 123 Peach lane, CA - Are the two from the same household? Timeliness Activation date and time: 12 May, 2009, 12:00 AM, posted into the system during day-end batch processing. However, the service was activated during business hours of the previous day. Interpretability First name attribute contains: Tai-Tai Cheung Mei Lee Wang - Is this a personal name or company name? - Is the gender male or female? Data management tools can be used for profiling and standardizing data, matching and merging data, monitoring quality, and tracking and addressing issues in data quality. Challenges in Data Quality Management Ownership of quality issues Continuous monitoring of quality by the DQM process enables real-time reporting of issues. However, it is seen that units/ departments rarely take ownership to rectify them or approve automatic cleansing. A data governance committee is needed to enforce cross-functional collaboration and sensitize the organization about the importance of resolving DQ issues. It requires huge manpower, and the Return on Investment on data quality fixes is relatively low. Identifying master/ reference data The lack of attribute-level standards for data quality, M&As without a data integration strategy and federated source data stores result in multiple versions of data, the lack of a golden record and incomplete master attributes. Metadata-driven DQM A DQM process driven by metadata management is recommended for CSPs with frequent Merger and Acquisitions, multiple source systems or strategic plans to integrate data from multiple technologies. The cleansing maps in the integrated process and its in-built mechanism to alert and track issues help refine DQ, exception and notification rules and resolution techniques. The DQ system notifies exceptions to quality standards and violation of data quality rules to data stewards, who can rectify the issues with the help of data architects. New DQ issues must be updated in the rule repository. Quality analysis reports contain the results of DQ measurements in a pre-determined format. The senior management normally requires reports to be presented with color codes akin to the traffic signal usable data (green), partly usable (yellow) and not usable (red). 6 Infosys View Point

7 Source Systems Data Profiling, Quality Data Analysis Consolidation Data Profiler Customer Data Cleansing Corrective Ac on DQ Rules EDM Network Service Product Billing Usage ODS Faults Contract Enterprise Data Model DQ Engine ETL Processing Excep on Repository Continuous DQ Monitoring by SME Figure 5: Data quality management Data quality-driven cleansing The DQ process meets business requirements by cleansing and standardizing the database through continuous monitoring. The data governance committee and data stewards frame the rules for cleansing, exception management and standardization of data. Data quality scorecards help data stewards monitor and track DQ issues, and refine data cleansing rules. Cleansing occurs at various instances of the data management lifecycle: Front-office (real-time) cleansing Back-office (batch cleansing) Cross-office (cleansing during data transfer between businesses) Data collaboration between cross-functions The DQM process is burdened with ambiguity and risk when an attribute has multiple owners or a change in an attribute influences other attributes and downstream applications. It can be avoided by storing details of the creation of data in the taxonomy of data items. The information to be stored include author, date and time of creation, application details, domain values, derivation details, derivation logic (if required), dependant fields, data item hierarchy, and propagation of changes. As a DQM best practice, CSPs must start with limited data profiling and data cleansing activities, and incrementally develop a robust and scalable data quality management platform for a cross-organizational, 360-degree view of business. Infosys View Point 7

8 Data Architecture Data architecture is a fundamental aspect of enterprise data management. It is a multi-layered set of models that defines the enterprise data strategy and management policy on data collection, and identifies the need for improvement in business decisions. Billing and Payments Contracts STATS_FORECASTING BANK &BUILDING INSURANCE STATISTICS FORECASTING EDI-FAX- GATEWAY SOCIETY GWAY_ACS GPS_BANK ACS_STATS All media BANK_UPAY Share Save Eligibility GPS_INS ACS_GWAY FORECASTING_TACT-FCST Personnel Detail ACS TACT-FCST PENSION GPS_PSION GPS ACS_INV CUSTOMER PSION_UPAY ACS_GRD BUDGET BACS MODELLING UPAY_PSION TELEWARE SYSTEM PAY_BACS GRD_FCS PAYROLL TELE_GRD INV_CUST AP_BACS GPS_EIS GL_FCS FCS_GRD GRD_TELE PAY_AP INVOICING MEN_INV INV_GRD EIS KEL GRD CORPORATION GL(SAL)_GRD TAX INV_SL KEL_MEN GRD_GL(SAL) GRD_GL(SE) Corporate MEN_KEL GL(SE)_GRD AP-SE-SAL FOCUS FCS_GL AICC_CTAX MENTOR GL(SE & SAL) AP(SAL)_PROC GIS_MEN PROC_EIS MEN_GIS SALES_LEDGER PROC_AP(SE) MMS_EIS PROP_EIS FOCUS_PROP GIS AP(SE)_PROC GL_PROC Surveys PROP_GIS PROC_AP(SAL) PROP_SL AICC PROC-GL GIS_PROP AP_MMS PROP_MMS PROPERTY MMS_GIS D&CPS_GIS PROCUREMENT (attack) PA_AICC Maintenance Costs Drawings and MMS_PROP PAS_PROC object data MMS Maintenance Costs PA_PR PROC_PAS PROC_PA BMS_MMS AUTOCAD BMS PA-SE-SAL OC 3DMOD&VIS PROC_CCPS PROC_ PM_PROC MDS_PROC PROC_FAX CCPS_PROC PROC_MDS CCPS_PM D&CPS_AUTOCAD Final Accounts EDI_PROC PROC_EDI Cost Plan Drawings PA_CCPS DES-OPT_3DMOD&VIS PM_CCPS PM D&CPS_CCPS MDS CCPS Cost Phasing EDI-FAX- 3DMOD&VIS Cost plans GATEWAY DES OPT D&CCPS D&CPS Models Customers and Sales Channels Marketing Products and Services Service Provisioning Service Assurance Figure 6: Multi-layered data architecture The enterprise data strategy comprises strategic initiatives such as data integration, legacy data migration, legacy system rationalization, master data management, metadata management, and business intelligence and reporting. CSPs can integrate independent applications into an enterprise data model to address challenges such as dynamic business processes, convergence of technology, changes in regulations, and increased competition. In addition, they can view and monitor enterprise performance by integrating and migrating data into the enterprise model, enabling rationalization of legacy systems. Data architects design the enterprise data model, automate data capture and validation, and implement an audit trail mechanism for business-critical data. They are also responsible for: Entity relationship diagrams that depict the relationship of business entities across subject areas Data flow diagrams that display data flow between enterprise applications and databases Identifying data stewards across subject areas, associated business units, business processes of each business function, and enterprise applications Setting standards and best practices for naming conventions to define data elements in the enterprise data model Designing and managing a metadata repository across the enterprise. The repository stores attribute name, type, length, owner of the data element, business unit, valid values, last updated user, last updated date and time, etc. Data profiling; data modeling; Extract, Transform and Load (ETL); and reporting tools exchange metadata with the enterprise metadata repository. 8 Infosys View Point

9 Location Finance Accounting Internal Transaction Individual Party Organiza on Users Contact Channel Content Store Party Role Map Presence Info Address SCM Service Provider Customer Customer Site Sales Channels Marketing Campaigns Third Party Service Provider Service Orchestration / Authoriza on / Metadata Customer Profile Customer Accounts Sales / Opportunity Inter-Carrier Accounts Network Elements Partner and Profile Product Catalog Service Sample Data Model Product Billing Account Usage and Billing Network Equipment Interaction Trouble Ticket Physical Resource Customer Order Credit Risk Scoring Class of Service Logical Resource Agreement Figure 7: Entity relationship diagram The aim of the enterprise data strategy must be to provide cleansed, consistent, integrated, and well-managed reference or master data. Since master data is a core component, any issue with it will affect the entire enterprise, exposing risks due to data inconsistency. Data Security CSPs need a robust strategy to ensure security of sensitive, business-critical data such as customer profile, payment details, contact, contracts, subscription, and product details. The data governance team must frame rules and regulations to Manage the change control board that authorizes changes to the data structure for sensitive data. Frequent changes lead to an unstable business and multiple versions of the business entities. Enhance the confidentiality and availability of data in hard and soft copy Protect data from unauthorized access, modification and destruction Prevent improper disclosure of data Avoid security breach of information and related loss to business, legal implications, etc. The data security strategy assigns stewards for all data sources, and authorizes them to grant access rights, maintain the list of authorized users and ensure accuracy of data. Stewards must also ensure that data is not duplicated in any format unless there is a business process requirement and copies are controlled across the enterprise. Infosys View Point 9

10 Challenges in Enterprise Data Management To manage terabytes of enterprise data in a complex landscape of legacy applications, CSPs require an enterprise data model that is compliant with the SID framework. They also need well-defined strategies for data integration, modernization of applications, migration of data from legacy applications, rationalization of legacy systems, and presenting data in the enterprise portal. It involves the following challenges: Duplication of data due to heterogeneous applications with independent data systems Data impurity due to the lack of data quality principles across the enterprise Non-standard data and exceptions in data range, type and length due to the absence of a metadata platform Multiple owners maintain data pertaining to different technologies in various data structures Lack of a data governance committee to enforce data quality principles and quality standards, and create awareness among source system owners about the importance of data as corporate assets Multiple versions of operational reports with different sub-functions and duplication of data in different formats across upstream/ downstream data systems due to uncontrolled data distribution, resulting in legal and regulatory noncompliance Conclusion Enterprise data management helps CSPs improve the quality of data, prevent revenue leakage and roll out methodologies for data governance, metadata management, master data management, data architecture, and data security. It also enables informed decision making through customer behavior analysis, market behavior analysis, competitor analysis, single view of enterprise data, and converged billing. However, the success of an EDM implementation depends on effective collaboration between business sub-functional heads, data architects, the CIO and CEO. About the Author Sivaprakasam S.R. is a Principal Technology Architect and mentors the database and business intelligence track at Infosys Communication, Media and Entertainment business unit. His interests include enterprise data modeling, enterprise data integration, enterprise data warehousing, enterprise data quality management, and semantic data integration. He can be reached at sivaprakasam_s@infosys.com