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.
Wednesday, June 16, 2010
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
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
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
Friday, May 14, 2010
SharePoint overview
http://www.microsoft.com/everybodysbusiness/en/ca/products/sharepoint.aspx?CR_CC=100248412&WT.mc_id=SEARCH&WT.srch=1&CR_SCC=100248412
Top SharePoint features
Makes it easy for everyone to access current information, get real-time status updates and collaborate with co-workers
Improve workflow and help reduce shipping and travel costs by sharing all of your information through a single, universally accessible location
Connects seamlessly with Microsoft Office and third-party platforms, making it easy for users
Works with Microsoft Unified Communications CRM and ERP, so you eliminate some redundant solution costs
Top SharePoint features
Makes it easy for everyone to access current information, get real-time status updates and collaborate with co-workers
Improve workflow and help reduce shipping and travel costs by sharing all of your information through a single, universally accessible location
Connects seamlessly with Microsoft Office and third-party platforms, making it easy for users
Works with Microsoft Unified Communications CRM and ERP, so you eliminate some redundant solution costs
Wednesday, February 24, 2010
Categories
Categories under dataset
http://www.data.gov/catalog
Search "raw" data by single/multiple category
Skip to search by agency All Categories
Agriculture
Arts, Recreation, and Travel
Banking, Finance, and Insurance
Births, Deaths, Marriages, and Divorces
Business Enterprise
Construction and Housing
Education
Elections
Energy and Utilities
Federal Government Finances and Employment
Foreign Commerce and Aid
Geography and Environment
Health and Nutrition
Income, Expenditures, Poverty, and Wealth
Information and Communications
International Statistics
Labor Force, Employment, and Earnings
Law Enforcement, Courts, and Prisons
National Security and Veterans Affairs
Natural Resources
Other
Population
Prices
Science and Technology
Social Insurance and Human Services
State and Local Government Finances and Employment
Transportation
Wholesale and Retail Trade
http://www.data.gov/catalog
Search "raw" data by single/multiple category
Skip to search by agency All Categories
Agriculture
Arts, Recreation, and Travel
Banking, Finance, and Insurance
Births, Deaths, Marriages, and Divorces
Business Enterprise
Construction and Housing
Education
Elections
Energy and Utilities
Federal Government Finances and Employment
Foreign Commerce and Aid
Geography and Environment
Health and Nutrition
Income, Expenditures, Poverty, and Wealth
Information and Communications
International Statistics
Labor Force, Employment, and Earnings
Law Enforcement, Courts, and Prisons
National Security and Veterans Affairs
Natural Resources
Other
Population
Prices
Science and Technology
Social Insurance and Human Services
State and Local Government Finances and Employment
Transportation
Wholesale and Retail Trade
Data Policy
http://www.data.gov/datapolicy
Data Policy
Data Policy Statements
Public Information
Security
compliance with the required confidentiality, integrity, and availability controls mandated by Federal Information Processing Standard (FIPS) 199 as promulgated by the National Institute of Standards and Technology (NIST) and the associated NIST publications supporting the Certification and Accreditation (C&A) process. Submitting Agencies are required to follow NIST guidelines and OMB guidance (including C&A requirements).
Privacy
compliance with current privacy requirements including OMB guidance.
Privacy Impact Assessments or System of Records Notices (SORN) easily available on their websites.
Data Quality and Retention
All information accessed through Data.gov is subject to the Information Quality Act (P.L. 106-554). For all data accessed through Data.gov, each agency has confirmed that the data being provided through this site meets the agency's Information Quality Guidelines.
As the authoritative source of the information, submitting Departments/Agencies retain version control of datasets accessed through Data.gov in compliance with record retention requirements outlined by the National Archives and Records Administration (NARA).
Secondary Use
Data accessed through Data.gov do not, and should not, include controls over its end use. However, as the data owner or authoritative source for the data, the submitting Department or Agency must retain version control of datasets accessed. Once the data have been downloaded from the agency's site, the government cannot vouch for their quality and timeliness. Furthermore, the US Government cannot vouch for any analyses conducted with data retrieved from Data.gov.
Citing Data
The agency's preferred citation for each dataset is included in its metadata. Users should also cite the date that data were accessed or retrieved from Data.gov. Finally, users must clearly state that "Data.gov and the Federal Government cannot vouch for the data or analyses derived from these data after the data have been retrieved from Data.gov."
Public Participation
In support of the Transparency and Open Government Initiative, recommendations from individuals, groups and organizations regarding the presentation of data, data types, and metadata will contribute to the evolution of Data.gov.
Applicability of this Data Policy
Nothing in this Data Policy alters, or impedes the ability to carry out, the authorities of the Federal Departments and Agencies to perform their responsibilities under law and consistent with applicable legal authorities, appropriations, and presidential guidance, nor does this Data Policy limit the protection afforded any information by other provisions of law. This Data Policy is intended only to improve the internal management of information controlled by the Executive Branch of the Federal Government and it is not intended to, and does not, create any right or benefit, substantive or procedural, enforceable at law or in equity, by a party against the United States, its Departments, Agencies, or other entities, its officers, employees, or agents.
Data Policy
Data Policy Statements
Public Information
Security
compliance with the required confidentiality, integrity, and availability controls mandated by Federal Information Processing Standard (FIPS) 199 as promulgated by the National Institute of Standards and Technology (NIST) and the associated NIST publications supporting the Certification and Accreditation (C&A) process. Submitting Agencies are required to follow NIST guidelines and OMB guidance (including C&A requirements).
Privacy
compliance with current privacy requirements including OMB guidance.
Privacy Impact Assessments or System of Records Notices (SORN) easily available on their websites.
Data Quality and Retention
All information accessed through Data.gov is subject to the Information Quality Act (P.L. 106-554). For all data accessed through Data.gov, each agency has confirmed that the data being provided through this site meets the agency's Information Quality Guidelines.
As the authoritative source of the information, submitting Departments/Agencies retain version control of datasets accessed through Data.gov in compliance with record retention requirements outlined by the National Archives and Records Administration (NARA).
Secondary Use
Data accessed through Data.gov do not, and should not, include controls over its end use. However, as the data owner or authoritative source for the data, the submitting Department or Agency must retain version control of datasets accessed. Once the data have been downloaded from the agency's site, the government cannot vouch for their quality and timeliness. Furthermore, the US Government cannot vouch for any analyses conducted with data retrieved from Data.gov.
Citing Data
The agency's preferred citation for each dataset is included in its metadata. Users should also cite the date that data were accessed or retrieved from Data.gov. Finally, users must clearly state that "Data.gov and the Federal Government cannot vouch for the data or analyses derived from these data after the data have been retrieved from Data.gov."
Public Participation
In support of the Transparency and Open Government Initiative, recommendations from individuals, groups and organizations regarding the presentation of data, data types, and metadata will contribute to the evolution of Data.gov.
Applicability of this Data Policy
Nothing in this Data Policy alters, or impedes the ability to carry out, the authorities of the Federal Departments and Agencies to perform their responsibilities under law and consistent with applicable legal authorities, appropriations, and presidential guidance, nor does this Data Policy limit the protection afforded any information by other provisions of law. This Data Policy is intended only to improve the internal management of information controlled by the Executive Branch of the Federal Government and it is not intended to, and does not, create any right or benefit, substantive or procedural, enforceable at law or in equity, by a party against the United States, its Departments, Agencies, or other entities, its officers, employees, or agents.
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