ENTERPRISE DATA INTELLIGENCE Let’s Get Serious...Apr 19, 2017 · •CA-Autosys (Workload Automation AE) •CA–Unicenter TNG Workload® •CONTROL-M® Distributed •Cron •TWS

© 2016 ASG Technologies Group, Inc.All rights reserved

ENTERPRISE DATA INTELLIGENCE

Let’s Get Serious

SCOTT SNIVELYStrategic Sales Specialist, Data [email protected]

April 19, 2017

mailto:[email protected]


ASG Technologies

Why Should We Care About Data Intelligence

Business and Technical Data Governance

What is an Enterprise Data Intelligence Solution - Demonstration

Q&A

AGENDA


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

ANNUAL REVENUE INVESTORS

COUNTRIES

70%OF FORTUNE 500 COMPANIES

RUN ON ASG SOLUTIONS


• 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 students @ $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.)

WHY SHOULD WE CARE ABOUT DATA INTELLIGENCE


BUILDING THE CASE FOR ENTERPRISE DATA INTELLIGENCE

1 | Avoid Negative Corporate Impacta. PII Information b. $7 Million Average Data Breach

2 | Reduce Direct Costsa. Spreadsheet & DB Metadatab. Impact Analysisc. Saving 2/3 time = $150,000 / Year

3 | Efficient Audit Processa. Streamlined Audit Processb. Saving 10% of Audit Process Costs

4 | Tribal Knowledgea. Critical Data Element Changes Documentationb. 75% Savings on Learning Curve for

Stewards & SME

5 | Data Driven Decisionsa. 1% Positive Revenue Impact

6 | Lineage & Self Servicea. 10% Savings of Effort of Business

Users

“REGULATORY PRESSURE MAP…” EVOLVING BANKING REGULATIONS

KPMG PAPER ON EVOLVING BANKING REGULATION - REGULATORY PRESSURE INDEX

It been reported:

$100B fines to US banks since 20081

Financial firms are “too big to manage1”

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 talent3

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?


CONVERGENCE OF BUSINESS AND ITSimilar evolving challenges and needs

• Policies

• Standards

• Business Rules

• Business Events

• Key Business Elements

• Critical Data Elements

• Reference Data

• ETL Rules

• BDL

• BI Reports

• Source of Record

• Data Profiling Metrics

BUSINESS I.T.

Operationalizing Data Governance

An equal, an unfolding business/IT collaboration process…


Applications

ETL

Reports

Databases

DW

Metadata Repository

Models

Sales

BusinessAnalyst

ITArchitect

FinancialController

ASG DATA INTELLIGENCE

• Keeping Business and I.T. on the Same Page

Ext Docs, URLs, etc.

VP of Finance

Dir of Acctg / Compliance

QualityMgr

GovernanceMgr

IT Architect

Metadata/Code Repository

Reports

Databases

DW

Invites active participation across business and IT communities…while still providing control / governance over change.

Governance Process

Zero Gap Data Lineage

ZERO GAPDATA LINEAGE

LINEAGEANYWHERE

DATA GOVERNANCE

LOB /LIFECYCLE

+


MINING FOR ENTERPRISE DATA INTELLIGENCEMore than 220 “out of the box” interfaces

Databases (11)

• DB2/zOS; UD

• Greenplum

• IMS/DB

• IDMS (f)

• MS SQL Server

• MySQL

• Netezza (f)

• Oracle

• SAS Data Set (f)

• Sybase ASE

• Teradata

ETL (12)

• 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

Business Intelligence (13)

• 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

Enterprise Architecture (18)

• 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 <->LEGEND: <-> – Bi-Directional Interface; (f) – Field Developed Interface; (d) – In Development ; (r) – In Research


ERP (7)

• e-Business Suite

• PeopleSoft

• JD Edwards

• SAP

• SAP

• BW

• HANA

• Siebel

Big Data (7)

• Hadoop

• Falcon

• Flume

• Hive/HCatalog

• HiveQL

• Cloudera Navigator

• HDFS

• Sqoop

Other (5)

• CSV

• MS Excel (f)

• XML

• SAP BOMM (f)

• Trillium DQ tool

LEGEND: <-> – Bi-Directional Interface; (f) – Field Developed Interface; (d) – In Development ; (r) – In Research

Reference Data (6)

Unique Value Scanner

• Oracle• SQL• Teradata• Sybase• Db2• DB2 UDB




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

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)

ETL (3)

•DataStage

•MS SSIS

•Informatica



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®

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

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

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

223

General Data Protection Regulation (GDPR)

A “Risk Based” Approach

Increased Penalties

DPO – Data Protection Officer

Focus on Quality and Governance

Principles Driven

Risks and Penalty Mitigation

The General Data Protection Regulation is a Regulation by which the European Commissionintends to strengthen and unify data protection for individuals within the European Union (EU). It also addresses export of personal data outside the EU.

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

Illustrative: for discussion

General Data Protection Regulation (GDPR)

Context Compliance GDPR Anti-Trust Sarbanes Oxley CCAR/KYC/BCBS 239 State International

Data Intelligence Structure to inform Business Community Guidelines for Developers

Business Rule to ensure in Compliance

Processes to be in Compliance

Standard Right to Forget Right to Understand Right to be informed

(Hacking) Right to Data Protection

PolicyGDPR

Right to be Forgotten Right to UnderstandRight to be informed

(Hacked)Right to Data Protection

GDPR Structure in ASG Data Intelligence

Context Compliance GDPR Anti-Trust Sarbanes Oxley CCAR/KYC/BCBS 239 State International

DEMONSTRATION OF

DATA INTELLIGENCE

Q & ANEXT STEPS

THANK YOU

Documents

ENTERPRISE DATA INTELLIGENCE Let’s Get Serious...Apr 19, 2017 · •CA-Autosys (Workload Automation AE) •CA–Unicenter TNG Workload® •CONTROL-M® Distributed •Cron •TWS