ENTERPRISE DATA INTELLIGENCE Let s Get Serious

Transcription

1 ENTERPRISE DATA INTELLIGENCE Let s Get Serious SCOTT SNIVELY Strategic Sales Specialist, Data Intelligence Scott.Snively@ASG.com April 19, 2017

2 AGENDA ASG Technologies Why Should We Care About Data Intelligence Business and Technical Data Governance What is an Enterprise Data Intelligence Solution - Demonstration Q&A

3 ASG TECHNOLOGIES - WHO WE ARE ASG Technologies brings peace of mind to every enterprise with information access, management and control from legacy to leading edge environments. PEOPLE COUNTRIES 70% OF FORTUNE 500 COMPANIES RUN ON ASG SOLUTIONS ANNUAL REVENUE INVESTORS

4 WHY SHOULD WE CARE ABOUT DATA INTELLIGENCE Large, Multi-Billion Dollar Retail Organization does not separate In-Store Sales from On-Line Sales Mid-Size University has three different full-time student counts on website low to high count 213 $20,000 in revenue per student $4,260,000!!!! Large Hi-Tech firm has multiple Business Intelligence tools employed in different but related departments, cannot agree on numbers. (SAP Hana, Tableau, Spreadsheets, etc.)

5 BUILDING THE CASE FOR ENTERPRISE DATA INTELLIGENCE 1 Avoid Negative Corporate Impact a. PII Information b. $7 Million Average Data Breach 2 Reduce Direct Costs a. Spreadsheet & DB Metadata b. Impact Analysis c. Saving 2/3 time = $150,000 / Year 3 Efficient Audit Process a. Streamlined Audit Process b. Saving 10% of Audit Process Costs 4 Tribal Knowledge a. Critical Data Element Changes Documentation b. 75% Savings on Learning Curve for Stewards & SME 5 Data Driven Decisions a. 1% Positive Revenue Impact 6 Lineage & Self Service a. 10% Savings of Effort of Business Users

6 REGULATORY PRESSURE MAP EVOLVING BANKING REGULATIONS It been reported: $100B fines to US banks since Financial firms are too big to manage 1 Regulatory pressure and ability to deal with them are in a varying degrees of maturity across the regions 2 A key to sustainable risk governance is developing, attracting, and retaining talent 3 With above as the backdrop: How should global financial firms orchestrate Data Governance initiatives? How to prioritize technology initiative in alignment with regulations? what s the organizations core technology capability What is the pathway to developing, attracting and retaining key IT talent? KPMG PAPER ON EVOLVING BANKING REGULATION - REGULATORY PRESSURE INDEX

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9 CONVERGENCE OF BUSINESS AND IT Similar evolving challenges and needs An equal, an unfolding business/it collaboration process Policies Standards Business Rules Business Events Key Business Elements BUSINESS I.T. Critical Data Elements Reference Data ETL Rules BDL BI Reports Source of Record Data Profiling Metrics Operationalizing Data Governance

10 ASG DATA INTELLIGENCE Keeping Business and I.T. on the Same Page ZERO GAP DATA LINEAGE DATA GOVERNANCE + LINEAGE ANYWHERE LOB / LIFECYCLE Zero Gap Data Lineage VP of Finance Dir of Acctg / Compliance Financial Controller Quality Mgr Governance Process Governance Mgr IT Architect Sales Ext Docs, URLs, etc. Business Analyst Models Applications DW IT Architect Databases ETL Metadata/Code Repository Repository Reports Invites active participation across business and IT communities while still providing control / governance over change.

11 MINING FOR ENTERPRISE DATA INTELLIGENCE More than 220 out of the box interfaces Databases (11) ETL (12) Business Intelligence (13) Enterprise Architecture (18) DB2/zOS; UD Greenplum IMS/DB IDMS (f) MS SQL Server MySQL Netezza (f) Oracle SAS Data Set (f) Sybase ASE Teradata DataStage DB2 Stored Proc (d) Informatica PowerCenter MS SSIS Oracle PL/SQL Oracle Data Integrator (f) Data Warehouse Builder Golden Gate (f, d) SAP BODI (f) Data Services (f, r) SAS Data Integration (d) Teradata BTEQ/SQL Cognos Essbase (f) Hyperion (f,r) IBI WebFocus (f,d) MicroStrategy MS SSAS MS SSRS SAP Business Objects Crystal Reports SAS BI (f) OBIEE (f) QlikView (f, r) Tableau LEGEND: <-> Bi-Directional Interface; (f) Field Developed Interface; (d) In Development ; (r) In Research SAG Predict <-> ARIS Toolset <-> Borland CaliberRM (f) Together Control/Center <-> Casewise Corporate Modeler <-> COOL:Biz <->; COOL:gen <-> PowerDesigner <-> ER/Studio <-> ERwin <-> Rational Data Architect <-> Software Modeler (RSM) <-> Software Architect (RSA) <-> System Architect <-> Systems Developer <-> Rose <-> Rational Rose XDE <-> mictrotool case/4/0 <->

12 MINING FOR ENTERPRISE DATA INTELLIGENCE More than 220 out of the box interfaces ERP (7) Big Data (7) Other (5) Reference Data (6) Unique Value Scanner e-business Suite PeopleSoft JD Edwards SAP SAP BW HANA Siebel Hadoop Falcon Flume Hive/HCatalog HiveQL Cloudera Navigator HDFS Sqoop CSV MS Excel (f) XML SAP BOMM (f) Trillium DQ tool Oracle SQL Teradata Sybase Db2 DB2 UDB LEGEND: <-> Bi-Directional Interface; (f) Field Developed Interface; (d) In Development ; (r) In Research

13 MINING FOR ENTERPRISE DATA INTELLIGENCE More than 220 out of the box interfaces Distrib. Technologies (27) Microfocus COBOL CORBA IDL Dynamic SQL Hibernate HP Vugen ibatis (MyBATIS) Java JAXB JAX-WS JDO JPA (d) JSF (d) JSP J2EE Packaging MQ Series Perl PHP SAP ABAP SAP Dictionary Spring Beans SQLJ Struts/Tiles Unix Shell Script Web Services (WSDL) XML XML Schema (XSD) XSLT Microsoft Technologies (20) MS Access Schema MS Access Application ADO.Net Entity Framework ASP ASP.NET BizTalk/Messaging BizTalk/Orchestration C# C/C++ LINQ.Net MS-DOS Shell NHibernate Pro*C SQL Server Schema SQL Server SQL Script SQL Server Stored Proc (T-SQL) Visual Basic Visual Basic.NET Windows Catalog Windows Communication Foundation (WCF) Distributed Databases (16) DB2 UDB Schema Distributed DB2 UDB SQL Script DB2 UDB Stored Proc Distributed Informix Schema JDBC/ODBC Schema MySQL Schema Oracle Schema Oracle SQL Script Oracle Stored Proc (PL/SQL) Sybase Schema Sybase Stored Proc Teradata Schema Teradata SQL Script Teradata Stored Procedure (SPL) HP Vertica Schema HP Vertica SQL Scripts DataStage MS SSIS Informatica ETL (3) Distributed Schedules (6) ASG Zena CA-Autosys (Workload Automation AE) CA Unicenter TNG Workload CONTROL-M Distributed Cron TWS Distributed Distributed SCM (11) Borland StarTeam CA-SCM CVS Git (d) Rational ClearCase Rational Team Concert (RTC) SAP Serena Dimensions CM Subversion SVN Team Foundation Version Control (TFS) Visual Source Safe (VSS)

14 MINING FOR ENTERPRISE DATA INTELLIGENCE More than 220 out of the box interfaces z/os Technologies (29) APS Assembler z/os BMS z/os CA ADS/O CA Easytrieve z/os CA Fast Unload for DB2 z/os CA IDEAL CA IDMS/DC CA Telon z/os CICS z/os CICSPLEX SM CICS Application File Control (CAFC) COBOL z/os (Cobol Enterprise 5.2) DELINK FORTRAN IMS/DC JCL z/os MANTIS z/os Menu Support MFS z/os MQ Series NATURAL z/os ObjectStar PL/1 REXX z/os SAS Base z/os SMF z/os SMP/E z/os z/os Catalog z/os SCM (6) ASG LCM CA Endevor CA Librarian CA Panvalet IBM SCLM z/os Serena ChangeMan iseries (7) AS/400 Catalog AS/400 COBOL AS/400 Command Language (CL) AS/400 RPG AS/400 DB2 UDB Schema AS/400 DB2 UDB Stored Proc AS/400 Library File System z/os Databases z/os (10) ADABAS CA DATACOMM CA IDMS/DB DB2 Schema z/os DB2 Stored Proc z/os IMS/DB (DL/1) Model 204 QMF z/os SPITAB z/os Teradata z/os 223 z/os Schedulers (9) ASG Zeke ASG Cortex CA-ESP z/os CA Jobtrac z/os CA Scheduler z/os CA 7 z/os CONTROL M z/os HS5000/APM z/os OPC TWS z/os

15 General Data Protection Regulation (GDPR) The General Data Protection Regulation is a Regulation by which the European Commission intends to strengthen and unify data protection for individuals within the European Union (EU). It also addresses export of personal data outside the EU. A Risk Based Approach Principles Driven Increased Penalties The Commission's primary objectives of the GDPR are to give citizens back the control of their personal data and to simplify the regulatory environment for international business by unifying the regulation within the EU.GDPR has put in place substantial fines as an incentive for compliance. Major Considerations Organization Readiness 72 Hour turnaround time for data breaches Fast-tracked application May, 2018 DPO Data Protection Officer Risks and Penalty Mitigation Focus on Quality and Governance Illustrative: for discussion

16 GDPR Structure in ASG Data Intelligence Right to be Forgotten Right to Understand Right to be informed (Hacked) Right to Data Protection Context Compliance GDPR Anti-Trust Sarbanes Oxley CCAR/KYC/BCBS 239 State International General Data Protection Regulation (GDPR) Data Intelligence Structure to inform Business Community Guidelines for Developers Policy GDPR Standard Right to Forget Right to Understand Right to be informed (Hacking) Right to Data Protection Processes to be in Compliance Business Rule to ensure in Compliance

17 DEMONSTRATION OF DATA INTELLIGENCE

18 Q & A NEXT STEPS

19 THANK YOU