The document discusses an enterprise information management (EIM) framework and big data readiness assessment. It provides an overview of key components of an EIM framework, including data governance, data integration, data lifecycle management, and maturity assessments of EIM disciplines and enablers. It then describes a big data readiness assessment that helps organizations address questions around their need for and ability to exploit big data by determining which foundational EIM capabilities must be established and what aspects need improvement before embarking on a big data initiative.

1. P A G E 1
Big Data Readiness
Assessment
V E R S I O N 1 . 2

2. P A G E 2
EIM goals and strategies are business-driven for
the entire enterprise, underpinned by guiding
principles supported by senior management
Roles, responsibilities, structures and
procedures to ensure that data assets are
under active stewardship
Processes, procedures and
policies to ensure data is fit for
purpose and monitored
Metadata capture, management,
& manipulation to place data in
business & technical context
Proactive planning for the information lifecycle
including from acquisition through manipulation,
access, & use to archiving & disposal
The identification of appropriate data
integration approach for business challenges
e.g. ETL, P2P, EII, DV, EII or EAI
Appropriate and fit for purpose Information
security processes & controls to manage &
provide authorization, authentication,
access & audit of information assets
Organise information to align with business &
technical goals using Enterprise, Conceptual,
Logical & Physical models
Management of Data Warehouses and creation
of actionable Business Intelligence to provide
intelligence and analytics for business benefit
Identification, management and delivery to
consuming applications of the core shared data
concepts required enterprise wide
Manage diverse data sources across the
organisation from transaction data
management, to data warehousing and
business intelligence, to Big Data analytics
Enterprise Information Management Framework
Development of realistic Information Management strategies to align the desired Information capabilities and services with business motivations and
strategies. The information initiatives can be accelerated by use of our Reference Architecture models to understand the capabilities, and typical functional
areas for each IM discipline under consideration (such as MDM, DQ, Data Integration etc.). Our Architecture Reference models contain the typical areas of
functionality & capabilities observed in each IM discipline. Our EIM framework has capability & maturity models for each of the IM disciplines together with
the typical processes and activities observed in mature organisational services for each.
Manage & exploit Big Data analytical
approaches to yield new actionable insights

3. P A G E 3
Big Data Readiness Assessment
1. IM & Enablers Maturity: The organizations IM maturity level is measured,
IM processes & technology capabilities & human resource skill sets
reviewed.
2. Data Governance: A strong governance program combined with
a metadata management policy will help lessen or mitigate risk
intrinsic in broadening the types of information accessed.
3. Data Sourcing & Access: Identify the big data sources required and the
business case for each.
4. Integration & Exploitation: Determine how to gain value from, interpret
and integrate the data. Big Data vs RDBMS will be assessed.
5. Data Lifecycle: Review the data retention period for each source, what
should be kept long term vs passed through to help in the next step.
6. Technology Enablement & Services: What portfolio of big data services
should be offered, & what are the most appropriate technology enablers
7. Transition: What is the roadmap & pragmatic transition path to accomplish
your Big Data vision?
New
Insights
Manage & exploit Big Data
analytical approaches to
yield new actionable insights
What does Big Data really imply, do you need it and are you ready to exploit it?
Our Big Data readiness assessment helps organisations address these
questions, determine which of the “little data” disciplines absolutely must be in
place before embarking on a Big Data initiative, and what other foundational
aspects must established for a project to succeed.

4. Information Management Maturity Assessment*
Our EIM Framework has methods, principles, roles & responsibilities, and Maturity assessment models for each of the IM disciplines. Current state maturity
is assessed, and a target state determined. Following a gap analysis we develop a framework for improving the IM capabilities and a prioritised realistic
transition plan.
Note*: IM maturity frequently varies across business areas & a first cut review assesses overall IM Maturity
2
1.5
2
1.5
1.5
2
1.5
1.5
1.5
2
4
4
4
3
44
3.5
4
3.5
4
0
1
2
3
4
5
IM Principles
Data
Governance
IM Planning
Data Quality
IM Lifecycle
Management
Data
Integration &
Access
Data Models
& Taxonomy
Metadata
Management
Master Data
Management
DW & BI
IM Maturity Assessment
Current
Target
1.5
1.5
1.521.5
1.5
3.5
3.5
4
3.5
3
3
0
1
2
3
4
5
People
Processes
Executive
Sponsorship /
Leadership
Technology
Compliance
Measurement
IM Enablers Maturity Assessment
Current
Target
What? Content
and Artifacts
CONTENT
FRAMEWORK
When and How?
Current and Target
Maturity
MATURITY MODEL
Who? Skills and
Roles
SKILLS
FRAMEWORK
IM Maturity Assessment
To mature the IM Practices within the
existing IT Processes, there is the
need to mature people and process
along with technology
Across all of the IM disciplines key
enablers exist whose maturity
impacts the success of an IM
initiative.
IM maturity is a assessed across the
core IM disciplines. Maturity also
includes the provision of technology
to support the IM area.

5. P / 5
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