The First Step in EIM: Big Data & Big Data Governance · 2013. 11. 6. · • Data driven mind-set...

Proprietary & Confidential

The First Step in EIM

Big Data &

Big Data Governance

pg 2 Proprietary and Confidential

Table of Contents

•  Big Data Overview

•  Enterprise Information Management

•  Big Data Management •  Big Data Governance

•  Ensuring Success


[ BIG DATA OVERVIEW ]


Big Data Definition

•  Extremely large data sets that can’t be dealt with using traditional technologies

•  Can be structured, non-structured or multi-structured

•  Key characteristics:

  Volume

  Velocity

•  Types of Big Data

  Web and Social Media

  Machine Generated

  Biometrics

  Variety   Value

  Unstructured Content   Transactional


Business Drivers for Big Data


Big Data Industry Landscape

Industry Analysis

Computer and Electronic Products

and Information Sectors

•  Computer and electronic products and information sectors have already been experiencing strong productivity growth and are poised to gain significantly from the use of big data

Finance, Insurance, and Government

•  Finance, insurance and government are positioned to benefit as well, as long as barriers can be overcome e.g. overall ease to capture data, talent, IT infrastructure, low IT investment, data driven mind-set, data availability, etc.

Health Care •  Health Care has shown early success in the use of big data

Retail and Consumer Products

•  Big data offers significant new opportunities to create value (higher margins and productivity) in the retail industry

Manufacturing •  Manufacturing has historically been a productivity leader, and big data can help

extend gains. Manufactures can use big data across the value chain

Public Sector

•  The public sector faces a significant performance challenge due to : •  Lack of talent •  Data driven mind-set •  Low IT investment

•  The public sector leaders need to address these issues to use big data effectively

Researches state that, while the use of big data will matter across industry sectors, some sectors are set for greater gains. Opportunities and challenges vary from sector to sector:

Source Big Data: The Next Frontier, McKinsey Global Institute


Big Data Importance

•  When integrated with other enterprise data, organizations can develop more insightful understanding of their business which can lead to:

  A stronger competitive edge   Improve business processes   Greater product innovation and improvements   Increase in growth and revenue   Increased employee productivity through streamlined business processes

Source Big Data: The Next Frontier, McKinsey Global Institute


Implications of Big Data

•  How will organizations have to be designed, organized, and managed?

•  What existing business models are likely to be disrupted?

•  How will organizations’ legacy business models and technology compete?

•  How will business processes change?

•  How will marketing functions and activities have to evolve?

•  How will organizations leverage and value their data assets?

•  How will executives help their organizations take advantage of the change that is under way?

•  Where do they start and how? Current technologies and data management structures in organizations no

longer work in this new era of big data


[ ENTERPRISE INFORMATION

MANAGEMENT ]


A Comprehensive Framework

Provides a holistic view of data in order to manage data as a corporate asset

Enterprise Information Management

Information Strategy

Architecture and Technology Enablement

Content Delivery

Business Intelligence and Performance Management

GOVERNANCE

ORGANIZATIONAL ALIGNMENT

Content Management

Data Management Information Asset Management


Develop and execute architectures, policies and procedures to manage the full data lifecycle

How Big Data Fits

Enterprise Data Management Ensure data is available, accurate, complete and secure

Data Quality Management Data Architecture Data

Retention/Archiving

Master Data Management

Big Data Management

Metadata Management

Reference Data Management

Privacy/Security

DATA GOVERNANCE


[ BIG DATA MANAGEMENT ]


Foundation to Harness Internal and External Data

BDM provides foundational capabilities to integrate and analyze data from non-traditional data sources in order to find insights in new types of data

Process Automation

Architectural Improvements

Flexible Data Architecture

IT Transformation

and Adaptability

PAST PRESENT FUTURE

Transaction Management

Data Warehousing

Master Data Management

Integrated Information Management and Delivery

Process automation and management of transactions with application specific data within isolated business applications including ERP, CRM, SCM, eCommerce and other systems over the past decade

Data extraction and normalization for operational as well as management reporting and functional analytics. Data integrity and lack of standards have constrained the maturity of analytics in the past

MDM is management of foundational data domains that support core business processes, information and insight creation. It provides for flexibility data integration, directly supporting enterprise information architecture vision

EIM and adaptive architecture to deliver business capabilities and flexibility to future changes

Big Data Management

BDM is integrating and managing big data and its relationship across the enterprise through people, processes and technology. It provides opportunity to find insights in new types of data and content, to make organizations more agile, and to answer questions that were previously considered beyond reach


Big Data Lifecycle Process

Listen Capture Process

Integrate Analyze Consume

Measure Retain Destroy


Big Data Infrastructure & Architecture

As with any IT platform or a data warehouse, an infrastructure for big data has unique requirements. The end goal is to easily integrate big data with enterprise

data to allow complex and deep analytics

Distributed File Systems

Key Value Store

MapReduce Solutions Analysis & Reporting

DBMS OLTP

ETL Data Warehouse

No SQL Flexible

Specialized Developer-

centric

SQL Trusted Secure

Administered

Listen & Capture

Process & Integrate

Analyze, & Consume

Data Mining Dashboards

Measure & Report


Types of Data

Data Disciplines are expanding. Most types of data are not completely independent. Big Data often has a relationship to other data types. Management of these data sets addresses: •  Data Quality

•  Enrichment /Enhancement

•  Relevance

•  Privacy and Security

•  Governance

Small Data

Big Data

Reference Data Master Data

Metadata


Data Types Work Together

Master Data

Enhanced/Enriched

Master Data (360 degree View)

Examples: •  Social Media Influence •  Social Media Account IDs •  Demographic Information •  Relationships •  Email IDs •  Validated Master Data

Examples include: •  Address validation

through location broadcasts and geo-location data

Big Data (Interaction Data)

Big or Small Data

(Transactional Data)

Reference Data

(Statistic non-volatile data)


[ BIG DATA GOVERNANCE ]


Data Governance Definition

Data Governance is the organizing framework for establishing strategy, objectives and policy for effectively managing corporate data.

It consists of the processes, policies, organization and technologies required to manage and ensure the availability, usability, integrity, consistency, audit ability and security of your data.

Communication and Metrics

Data Strategy

Data Policies and Processes

Data Standards

and Modeling

A Data Governance Program consists of the inter-workings of strategy, standards, policies and communication.


•  Vision & Mission •  Objectives & Goals •  Alignment with Corporate

Objectives •  Alignment with Business

Strategy •  Guiding Principles

•  Statistics and Analysis •  Tracking of progress •  Monitoring of issues •  Continuous Improvement •  Score-carding

•  Policies & Rules •  Processes •  Controls •  Data Standards & Definitions •  Metadata, Taxonomy,

Cataloging, and Classification •  Operating Model •  Arbiters & Escalation points •  Data Governance

Organization Members •  Roles and Responsibilities •  Data Ownership &

Accountability

•  Collaboration & Information Life Cycle Tools

•  Data Mastering & Sharing •  Data Architecture & Security •  Data Quality & Stewardship

Workflow •  Metadata Repository

•  Communication Plan •  Mass Communication •  Individual Updates •  Mechanisms •  Training Strategy

•  Business Impact & Readiness •  IT Operations & Readiness •  Training & Awareness •  Stakeholder Management & Communication •  Defining Ownership & Accountability

Change Management

Data Governance Components


Competing Priorities

Business Insight

Security & Control









Accountability






Change Management

Strategy

licocntta

etatata

Mass Communication • Individual Updates

Training• Stakeholder Management & Communication

i

•  Extension of overall Data Governance Strategy and Scope

•  Business purpose and value unique of Big Data

•  Understanding of impacted business processes and key requirements

•  Incorporation of new risks









Accountability






Change Management

Organization

Ruless

dardsTaxon

g andg, and

• Training Strategy

•• IT• Tr• Stak• Defining

•  New Stakeholders

•  Extended participation at all levels to include Privacy, new Lines of Business

•  Extended RACI to cover new data types

•  Redefine role and scope of Data Steward; identify new stewards

•  New roles (i.e. Data Scientists)

•  New Regions









Accountability






Change Management

Policies, Processes & Standards

s

onon

act && Rearene

Managership

CChhManaaaa

•  Extension of Security, Privacy Policies

•  Policies around data masking in testing and/or production, and “unmasking”

•  Understanding of Intellectual Property considerations and Appropriate Use

•  Extension of Data Retention Policy   Archiving   Storage   Disposition

•  Policy Enforcement

•  Metadata, Classification

•  New Definitions and Terms









Accountability






Change Management

Measurement & Monitoring

P i d C fid i l

• S• T• M• C• S

& Readinesseadiness ess gement & Cp & Accoun

hhhhhhange aaaaaagement

•  Re-evaluate Data Quality Standards, Thresholds and Metrics

•  Data Availability requirements and monitoring

•  Data Profiling rules & processes

•  Monitoring of data movement and usage

•  Track security, privacy

•  Web metrics









Accountability






Change Management

Technology

•••

•

•

d Analysis rogress f issues mprovemeg

•  Integrating existing and Big Data Technologies, i.e. Master Data Management

•  Big Data Lifecycle Management   Data Compression & Archiving Requirements   Regulatory Retention Requirements for Big Data   Business Retention Requirements   Data Volumes & Cost

•  Metadata requirements

•  New Sources









Accountability






Change Management

Communication

&veenveeny

g P

ta Owncou

•  Extended Communication Plan, Awareness & Education

•  New Stakeholders •  Enhanced Goals, Priorities,

Concerns and Objectives


Big Data Governance & Technology Work Together

Big Data Strategy

Standardized Methods and Data Definitions (Metadata)

Roles and Responsibilities

Decision Rights, Arbiters and Escalation, Ownership & Accountability

Big Data Policies (Security, Privacy, Access, Retention)

Statistics / Analysis / Monitoring, reporting, Consumption

Filtering & Cleansing

Enrichment

Translation/Transforming

Run Algorithms

Data Processing Manipulation & Sorting

Data Analysis

Aggregate Results

Retain

Provide Guidance

Track Progress

Big Data Governance Big Data Technology

Create & Enforce Policies

Provide Feedback


Ensuring Success

•  Organizational leaders must start identifying and assessing opportunities

•  Leaders must understand the value in big data as well as how to unlock this value

•  Leverage existing governance capabilities

•  Determine both process and technical integration requirements

•  Take a phased and iterative approach

“Perspectives on “data” as a single, amorphous resource will have to give way to more granular optimization effort that recognizes the complex variations of information that,

collectively, paint a picture of a given customer audience.” Winterberry Group Whitepaper – Marketing Data Governance - July 2013


[ SECTION TITLE ]


[ QUESTIONS? ]


The First Step in EIM

Contact Info

Kelle O’Neal [email protected]

415-425-9661 www.firstsanfranciscopartners.com

@1stsanfrancisco

Date post:	05-Oct-2020
Category:	Documents
Upload:	others
View:	0 times
Download:	0 times

The First Step in EIM: Big Data & Big Data Governance · 2013. 11. 6. · • Data driven mind-set...

Documents