Cost of information management

Cost of information management
http://www.strassmann.com/pubs/econ-polim.html
The constitution includes:


Goals statements that define the concepts of operation of the business and its supporting information systems in years that are beyond the current budget planning horizon. Goals are hardly ever attained completely, but provide a strong framework for the long-term direction of the organisation's information management.
eg: Voice, data, video and image information systems shall be inter-operable so that any authorised employee will be able to retrieve on a single display information from any source or location.



Specific objectives towards achieving the stated goals.
eg. By 199x all applications shall run beneath a standard corporate graphic interface in which every employee has received basic training.



Management policies that define and allocate responsibilities for information management. These should include some `parliamentary process' to allow staff involvement in policy setting. (All policies should be phrased so that compliance can be tested.)
eg A senior executive, reporting to ......, shall be the chief information officer of the enterprise, with responsibility for the alignment of information management plans and resources with the approved goals, principles, policies and objectives.



Policies for the design, implementation, staffing and financial control of information systems.
eg Information systems shall be developed and enhanced according to an enterprise-wide methodology.

All corporate data shall be entered into the Information System only once, at the point of initial origin; all subsequent uses of such data shall rely on copies of the original entry.



Planning and finance policies for information.
eg Business plans must show how information systems contribute to achieving committed operating results.

Data analytics maturity

Data Analytics
http://www.juiceanalytics.com/writing/5-phases-data-analytics-maturation-part-1/

5 Phases of Data Analytics Maturation: Part 1
By Ken Hilburn
December 8, 2008
Find more about: analytics


http://www.juiceanalytics.com/writing/data-analytics-maturation-part-2/

Data Analytics Maturation Phase 3: Bigger Static Reports
Answers to questions you don't know

analytics

tdwi che c k l i s t r e p or t: DATA RE QUIRE MENT S FOR ADV ANC ED ANA LY T I C S


Discover relationships. Whether advanced analytics is based on
data mining, statistics, artificial intelligence, or complex queries,
it can help you discover and quantify important relationships that
you may have been unaware of. These relationships can reveal
fraud, define customer segments, group products of affinity,
and link field conditions that lead to product failures. The newly
discovered relationships, in turn, help you reduce fraud and its
costs, target marketing campaigns more accurately, develop effective
merchandizing strategies, and improve product quality.

Anticipate the future. Predictive analytics can produce scores and
statistics through which you can predict the likelihood of various
outcomes of certain situations. For example, predictive models
quantify a customer’s proclivity to churn, thereby giving you an
opportunity to retain the customer. Predictive models can assist
with various types of forecasting. Likewise, predictive analytics can
quantify future risk for pragmatic applications in actuarial tables or
loan approvals.

analytic sandbox

TABLE 1.
Reporting and OLAP
Advanced Analytics, Both
Query-Based and Predictive
Business
Method
Performance management for
business entities, relative to
a business plan.
Develop new products,
customers, etc. Reduce cost,
risk, fraud.
Information
Purpose
Update known facts.
Quantify past performance.
Infer unknown facts and
relationships. Quantify
future probabilities.
Output Historical standard reports,
dashboards, metrics, KPIs,
cubes for OL AP, etc.
Predictive models, scores,
forecasts. Results of complex
queries. Insights.
Queries Known, simple queries that
are easily optimized.
Queries that become very complex
as they evolve via iteration.
Volume
per Query
Small (usually less than
a gigabyte).
Large (possibly terabytes

After all, an EDW can handle
both query-intense and predictive-scoring workloads, plus it can
manage the low-level, detailed data that advanced analytics often
requires.


Table II
Enterprise Data Warehouse Data Mart Analytic Database
Business Method Single version of the truth for
enterprise performance.
Single subject area(s) for applicationspecific
purposes.
Test bed for exploring change
and opportunity.
Optimization Multiple update speeds, high performance,
workload management, in-database analytics.
Regularly updated data for reporting,
performance management, and OL AP.
Unpredictable data sets about changing
markets, costs, customers, risks, etc.
Data Attributes High standards for production data, plus
inclusion of experimental data.
Carefully transformed, cleansed,
modeled, and audited.
Less cleansed and modeled. Often just
raw source data.
Data Models 3NF data model to model the enterprise
with views for application flexibility.
Relational models for reporting.
Multi-dimensional models for OL AP.
3NF of source data. Models demanded by
analytic tools. Predictive models and scores.
Data Lifecycle Permanent history with transient, elastic
logical marts.
Permanent history of enterprise performance. Data tends to be transient, as analytic
needs change.
Data Acquisition Well-governed process with the flexibility
for self-provisioning elastic logical marts.
Slow process due to data transformation,
cleansing, modeling, audit trail, etc.
Load data fast with little prep and start analysis
immediately, regardless of state of data.


Analytics processed within the EDW. This is what most users tell
TDWI they would like to do. The catch is that many of the analytic
tools based on data mining technologies require users to dump
analytic data into flat files with a specific record structure or a single
denormalized table


Predictive analytics. The data mining and statistical algorithms
of a predictive analytic tool typically demand a very specific
data structure, typically denormalized