Maturing Enterprise Information Management: Extending the ... Information... · associated analytics, the basics are still needed. • Many companies today do not utilise or understand

© Innovation Value Institute 2013

Maturing Enterprise Information Management: Extending the IT CMF Conor O’Brien


What is Big Data?

Big data is a relative term describing a situation where the volume, velocity and variety of data exceed an organization’s storage or compute capacity for accurate and timely decision making. (SAS, “Big Data Meets Big Data Analytics”, White Paper)

2


How ‘Big Data’ is Different

Coming to terms with big data is prompting organizations to rethink their basic assumptions about the relationship between business and IT — and their respective roles.

Organizations that capitalize on big data stand apart from traditional data analysis environments in three key ways: • They pay attention to data flows as

opposed to stocks. • They rely on data scientists and

product and process developers rather than data analysts.

• They are moving analytics away from the IT function and into core business, operational and production functions.

3

Davenport, T. H., Barth, P., & Bean, R. (2012). How “ Big Data ” is Different. MIT Sloan Management Review, 54(1).


BUSINESS INTELLIGENCE AND ANALYTICS: FROM BIG DATA TO BIG IMPACT

• BI&A 2.0 some research from academia and industry • BI&A 3.0 is an emerging research opportunity • Phones and tablets 480 million vs. PCs 380 million • Although the number of PCs in use surpassed 1 billion in 2008, the

same article projected that the number of mobile connected devices would reach 10 billion in 2020.

(Economist 2011)

4

Chen, H., & Storey, V. C. (2012). BUSINESS INTELLIGENCE AND ANALYTICS: FROM BIG DATA TO BIG IMPACT. MIS Quarterly, 36(4), 1165–1188. Retrieved from http://www.misq.org/skin/frontend/default/misq/pdf/V36I4/SI_ChenIntroduction.pdf


BUSINESS INTELLIGENCE AND ANALYTICS: FROM BIG DATA TO BIG IMPACT -> Research Opportunities

Analytics in a Web 3.0 (mobile and sensor-based) environment using large scale and fluid mobile and sensor data requires location and context-aware techniques for: • collecting, • processing, • analysing and visualizing.

5


Unique research challenges: • Highly mobile • Location-aware • Person-centered • Context-relevant • Operations • Transactions • Mobile interface • Visualization • HCI (human–computer interaction)

design are also promising research areas.


BI & A 3.0

Business Intelligence and Analytics - Evolution and Capabilities

Key Characteristics Gartner BI Platforms Core Capabilities Gartner Hype Cycle

BI & A

1.0

DBMS-based, structured content •RDBMS and data warehousing •Dashboards and scoreboards •Data mining and statistical analysis

• Ad hoc query and search • Dashboards & scoreboards • OLAP • Interactive visualization • Predictive modelling & mining

• Column-based DBMS • In-memory DBMS • Real-time decision • Data mining

workbenches

BI & A

2.0

Web-based, unstructured content •Information retrieval and extraction •Opinion mining •Question answering •Web analytics and web intelligence •Social media analytics •Social network analysis •Spatial-temporal analysis

• Information semantic services

• Natural language question answering

• Content and text analysis

BI & A

3.0

Mobile and sensor based content •Location aware analysis •Person centric analysis •Context-relevant analysis •Mobile visualization and HCI

• Mobile BI

6



The Role of Ethics in Data Governance (1 of 2) 7

Goetz, M. (2013). The Role of Ethics in Data Governance, 2, 1–2 Information Management.

Security and privacy have always been at the core of data governance. Two stories in the news have recently exposed an aspect of data governance that muddies the water on our definition of data ownership and responsibility.

•After the tragedy at Sandy Hook Elementary School, the Journal News combined gun owner data with a map and released it to the public causing speculation and outcry that it provided criminals information to get the guns and put owners at risk.

•A more recent posting of a similar nature, an MIT graduate student creates an interactive map that lets you find individuals across the US and Canada to help people feel a part of something bigger.


The Role of Ethics in Data Governance (2 of 2) 8

• Technology is moving faster than policy and laws can be created to keep up with this change.

• The owners of data more often than not will sit outside your corporate walls.

• Data governance has to take into account not only the interests of the company, but also the interests of the data owners.

• Data stewards have to be the trusted custodians of the data.

• Companies have to consider policies that not only benefit the corporate welfare but also the interests of customer and partners or face reputational risk and potential loss of business.

Goetz, M. (2013). The Role of Ethics in Data Governance, 2, 1–2 Information Management.


We still need the basics

• While a lot of today’s hype is about “big data” and associated analytics, the basics are still needed.

• Many companies today do not utilise or understand what can be gleaned from their own in-house data.

• Many SMEs do not use the most rudimentary of statistical approaches or tools.

• Identifying specific relevant targets for business intelligence and analytics functions is an essential step towards value.

9


Mortgage Bankers’ Association (1 of 2) 10

Focus Area Description Why Important Artifacts

Data Quality Defines and enforces quality and integrity standards and tolerances.

Proactively manage the quality and integrity of the data. Must be able to certify accuracy and completeness of data.

• DQ Strategy Document

• DQ Implementation Plan

• Data Profiling Metrics Data Architecture

Establishes the future state vision and overall framework for development of business services that are aligned with strategic goals of the business.

Ensures that projects integrate with the overall data strategy. Eliminates silo-based solutions that meet tactical needs but lack strategic perspective. Over time, migrate from the current state “web” of interfaces and data feeds to the future state vision.

• Data interface model • Future state model • Data Architecture

roadmap

Data Management

Mortgage Bankers Association. (2008). Data Management Best Practices. Technology.


Mortgage Bankers’ Association (2 of 2) 11

Focus Area Description Why Important Artifacts

Metadata Defines how data is collected, labelled and stored through-out all applications and databases.

Provides data standardization, data inventory, and controls. Monitors the data flow as it relates to business processes. Manages and tracks critical data fields. Provides alignment to industry standards (MISMO).

• Metadata repository

• Business rules • Conceptual

Model • Logical Model • Data Dictionary • Project

Templates

Data Stewardship

Provides business definition and direction on the use and security of data.

Creates ownership by the business to define data and govern the use, security and dissemination of the data.

• Domain owner matrix

Data Management

Mortgage Bankers Association. (2008). Data Management Best Practices. Technology.


Running today’s Enterprise Information Management Workshop (Part A)

- What we ask of you! - Organize yourselves in groups of no more than 5-6 individuals. - Collectively brainstorm in your groups the following question:

- What are the key factors/features needed to support an effective Enterprise Information Management in 2013-2015?

- Record identified skills/competences on provided flip chart - Nominate a spokesperson to feedback to the wider group

- Feedback session to be followed by an overview of IVI’s approach to Enterprise Information Management

12


How does your feedback compare to IVI’s Approach to Enterprise Information Management? 13

Definition Enterprise Information Management (EIM) develops, establishes and manages operational systems to effectively gather, manage, disseminate, leverage and dispose of information artefacts used to manage the organization and its strategic and operational activities. EIM combines the strategic, operational and security aspects of information management with the capabilities to analyse and exploit information.


How does your feedback compare to IVI’s Approach to Enterprise Information Management? 14

In-Scope Out-of-scope People, processes and technologies for: •Information Management Strategy •Data and Information Governance •Standards, Policies, and Controls •Master Data Management and Meta-data Management •Information Quality and Security Management •Data storage decisions and information lifecycle management inclusive of information tracking •Performance measurement •Information analysis including exploratory data analysis and confirmative data analysis.

• Storage (see the Critical Capabilities Technical Infrastructure Management (TIM))

• Infrastructure (see the Critical Capability Enterprise Architecture Management (EAM))

• Application development (see the Critical Capability Solutions Delivery (SD))

• The cultural and organizational behaviour aspects of knowledge and information sharing see Knowledge Management (KM)

• Intellectual Property Management (IPM) is not addressed and may be covered in a new Critical Capability of the IT-CMF

• Performance management


Enterprise Information Management Categories and Capability Building Blocks (1 of 3)

15

Category Capability building block

Strategy and Organization

Information Management Strategy

Information Governance

Competences and Communities

Policies, Standards, and Controls

Standards and Policies

Controls

15



16


Information Management

Information Valuation

Master Data Management

Metadata Management

Information Quality

Information Lifecycle Management

Business Continuity Planning

Information Security




Business Intelligence and Analytics

Leadership

Preparation

Reporting

17


Running today’s Enterprise Information Management Workshop (Part B)

- What we ask of you! - Organize yourselves in groups of no more than 5-6 individuals. - Collectively brainstorm in your groups the following question:

- What key1 skills and competences would you expect to see as enterprise information management matures within an organization?

- Record identified concepts on provided flip chart - Nominate a spokesperson to feedback to the wider group

- Feedback session to be followed by an overview and discussion of IVI’s key practices, outcomes and key metrics

1.Linked to the key issues identified earlier

18


Enterprise Information Management (EIM) Summary of key practices, outcomes, and key metrics (1 of 3) 19

Maturity Key practices Outcomes Key metrics High

5

Optimising

Meet regularly to review and update policies, standards and controls. Information life-cycles are kept flexible and thus enable business process optimizations. Information is a platform for new business.

Legal and standards compliance reduces risk and enhances reputation. Lean flexible processes lead to increased value generation. Identification and exploitation of options.

Current review period and trends for: # Business opportunities

identified # Lifecycle and storage

platform metrics # Incident metrics # Availability # Errors, Defects, Tardiness,

# Fixes

4

Advanced

Develop multi-planning period information management strategies. Information revenue, storage and retrieval costs are actively managed. Data classifications and access controls are implemented in meta data.

Long term strategic goal focus delivers greater value in the long term. Investment decisions are simplified and value focused. The management of information artefacts is simplified allowing a value effort focus.

# Number of years covered in rolling strategy

# Data life-cycles with revenue and costs

# Security audit exceptions # Governance audit

exceptions %Staff fully trained for

assigned IM roles




3

Intermediate

Information management, business intelligences and analytics are aligned with the business. Meta data and master data management are used to drive information quality. Competent staff assigned to IM roles.

Information Management (IM) services meet the needs of the stakeholders. Data quality issues are being addressed at a business level and it’s getting better. Reliable professional service emerging.

# Training goals set by senior management

# Major business goals set for business intelligence and analytics

# Security violations and trends

2

Basic

Management leads the development of policies, standards, and controls that are business-wide focused. EIM roles are defined and training is provided for stewardship, quality and security.

Confidence in the EIM capability is growing as it evolves into a professional service. Training and segregation of roles enhances the security, availability, relevance, control and timeliness of information.

# Standard issues tracking (open, closed, trends, categorized).

# Systems with access controls # Training goal targets by role # Performance metrics (reports

on-time, data quality, system responsiveness)

# life-cycle exceptions




1

Initial

Local goals are set for access and availability. Specific analytics goals are set at function and department levels.

Local successes cannot be replicated at organisational level. Expertise is specific to local and department problem domains.

# Goals set by local managers.

# Goals set by senior managers.

# Databases with roles for access, life-cycle management and security


BI Leader Job Description

Job title: Director/manager/VP of business intelligence Reports to: Chief operations officer, chief strategy officer, chief information officer, chief financial officer (more popular in the past than the present) or some of the newer titles such as chief customer officer, chief data officer, or chief analytics officer. http://blogs.forrester.com/boris_evelson/13-02-13-bi_leader_job_description Boris Evelson on February 13, 2013

22

http://blogs.forrester.com/boris_evelson/13-02-13-bi_leader_job_description



Job description: The director/manager/VP of BI has primary responsibility for setting the strategy and vision and for managing the day-to-day tactical operations of the BI teams. He/she will be responsible for all strategic, tactical, operational, financial, human, and technical resource managerial responsibilities associated with the following BI and BI-related functional areas: •Data preparation (sourcing, acquisition, integration) •Data warehousing (we often recommend that the first two functional areas are managed separately by “data preparation” team(s)) •Reporting, analytics, data exploration •Information delivery (portals, mobile) •BI competency center or center of excellence (BICC or BI COE) •The director/manager/VP of BI will lead his/her teams by establishing and executing a vision for the delivery of information and analytics platforms and solutions to the business’s key stakeholders, including, internal staff, partners and clients. This position will be ultimately responsible for helping transform the company into a business that truly differentiates and competes on analytics. http://blogs.forrester.com/boris_evelson/13-02-13-bi_leader_job_description Boris Evelson on February 13, 2013

23



BI Leader Job Description Job requirements: A degree in computer science, mathematics, operations, management, or a related field is required. A minimum of X years of progressively responsible experience in a directly related area, during which both professional and management capabilities have been clearly demonstrated. Industry/domain skills: Extensive expertise in [insert industry- and domain- (finance, HR, sales, marketing, etc.) specific verbiage]. Technical skills: •Extensive expertise in [insert specific BI and related technical platforms]. •Extensive expertise in data modelling, both logical and physical. •Extensive experience in multidimensional data modelling, such as star schemas, snowflakes, denormalized models, handling “slow-changing” dimensions/attributes. •Experience in and understanding of a wide variety of analytical processes (governance, measurement, etc.). •Experience with agile software development. •A solid understanding of key BI trends.

http://blogs.forrester.com/boris_evelson/13-02-13-bi_leader_job_description Boris Evelson on February 13, 2013

24




General business skills: • Extensive experience interacting with C-level executives. • Excellent written and verbal communication skills. • Excellent presentation skills. • Experience managing large [global] complex BI projects and teams. • Proven ability to complete projects and achieve results in an ambiguous work

environment. • Proven strong leadership skills within the project team and in the business community. • Proven ability to establish and articulate a vision, set goals, develop and execute

strategies, and track and measure results. • Proven ability to build and motivate a team to achieve well communicated expectations. • Proven strong negotiating and consensus building abilities. • Proven skills to work effectively across internal functional areas in ambiguous situations.

http://blogs.forrester.com/boris_evelson/13-02-13-bi_leader_job_description Boris Evelson on February 13, 2013

25



Data Scientist Job Description

- Business Savvy - IT Capable - Statistician - Process Engineer or Designer - Data/Information Modeller and Part-time Architect The above are graduate level criteria and more

senior data scientists will need …

26


Thank You

The innovation Value Institute thanks you for your contribution to the workshop. We will take your comments and feedback and review our current position on Enterprise Information Management. The IT-Capability Maturity Framework will continue to evolve, improve, and act as a basis for improving the delivery of value based on member contributions and on-going research.

27


Limitation of Liability

©2013 Innovation Value Institute™. All rights reserved. The material contained herein may not be copied, photocopied, reproduced, translated, or reduced to any electronic medium or machine-readable form, in whole or in part, without prior written consent of the Innovation Value Institute, except in the manner described in the documentation. All other brand names, product names, and trademarks are copyright of their respective owners. While every reasonable precaution has been taken in the preparation of this document, the author and publishers assume no responsibility for errors or omissions, nor for uses made of the material contained herein and the decisions based upon such use. No warranties are made, express or implied, with regards to either the contents of this work, its merchantability, or fitness for a particular purpose. Neither the author nor the publishers shall be liable for direct, indirect, special, incidental, or consequential damages arising out of the use or the inability to use the contents of this text.


For more information contact: [email protected]

mailto:[email protected]

Documents

Maturing Enterprise Information Management: Extending the ... Information... · associated analytics, the basics are still needed. • Many companies today do not utilise or understand