goa enterprise data analytics strategic plan - …€¦ · · 2017-06-15goa enterprise data...
TRANSCRIPT
GoA Enterprise Data Analytics
Strategic Plan
Presentation to IM Aware
June 8th, 2017
Strategy
…now for something (a little bit)
different….
4
Start with an
issue
Identify Indicators
Who has
the data?
Data Consultations
Ministry 1, Department 1
Acquisition
Storage
Tools
Data Cleansing
Data
Transformation
Is the data Fit for use?
Data Agreements
Ministry 1, Department 1
Ministry 1, Department 1
Ministry ‘N’, Department ‘N
~1-6
months
~4 months ~ 4
months/dataset
~ 1 month / dataset ~2 months
Data
Identification
Process Repeats (Sustainable?)
Impact
Analysis
Options
Analysis
Validate
Assumption
Traditional
Analysis
Non
Traditional
Analysis
Descriptive/
Diagnostic
Predictive/
Prescriptive
Ministry 2, Department 1
Ministry ‘N’, Department ‘N’
Data
A
ug
men
tati
on
Current State
5
Initiation Data Discovery Data Acquisition
Analysis Decision making Preparation
Focus Area
Target State
Init
iati
on
Data
Dis
co
very
Data
Acq
uis
itio
n
An
aly
sis
Decis
ion
makin
g
Pre
para
tio
n
Focus Area
EDA Framework
7
• Who: the human capital (People) – a
skilled workforce, and data-confident
consumers.
• What: the tools and infrastructure
(Technology) to acquire, store and
analyze data.
• How: the methods of sharing data
(Process) – the ability of data
consumers to access and share data
appropriately.
• Where (everywhere it’s needed) and
When (whenever it’s needed) are
intrinsic to the nature of data analytics.
• Governed by standards and agreed to
business process and approvals.
Long Term (3 - 5 years)
Medium Term (18 months - 3 yrs)
Short Term (18 months)
Roadmap Stages
Fragmented
• Fragmented point
solution – ad hoc
• High level of
duplication
• Minimum
resource capacity
Optimized
• Integrated at
enterprise level
• Discoverable
across enterprise
• Scalable
analytics
infrastructure
• Interoperable
• Cross
government
analytics
Standardized
• Better integration
at a department
or sector level
• Data
Governance
established
• Some level of
interoperability
• Collaborative
analytics
Innovative
• Data centric
enterprise culture
• Optimized data
governance
• Actionable
analytics to
enhance value
potential
• Centre of
Excellence
established
• Data as a
Service model
Stage of Maturity
Tra
nsfo
rma
tio
nal V
alu
e
Opportunistic
• Integrated at a
program level
• Limited Data
sharing between
departments
• Limited
interoperability
• Functional Data
stewardship is
present
People & Process
Category
Short Term
(18 Months)
Medium Term
(18 months – 3 years)
Long Term
(3 – 5 years)
Ease of Data
Access
• Gradually easier to find and
retrieve data due to:
- Cataloging of data sources
- Strategic accumulation of
certain data sets for easy
access
• Ease of access to data
improving due to critical mass
of data housed in Data Lake
• A maturity of the data catalog
brings ease of access
• Data is ubiquitous
• GOA Data and analytics available
across GOA
• Access from a wide range of tools
and mobile devices, both within
GOA and without
Trustworthiness
of Data
• Data quality is spotty
• Specific initiatives focus on
certain data sets to highlight
quality issues
• Governance and processes are
gradually stood up to deal with
quality issues
• The source of Truth is often
ambiguous
• Specific data sets are highly
trusted – many data sources
remain untrusted
• Quality has increased due to
governance processes
• Metadata provides greater
insight into quality issues
• Source of Truth data is well
understood for certain GOA
Sectors
• Some GOA Sectors achieve
mature governance
• Most GOA Sectors have
achieved governance maturity to
facilitate data quality
• Data quality measures provide
tangible evidence of quality
• Source of Truth is well
understood
• Business context accompanies
the metadata to support greater
understanding of potential use
• Governance maturity advances
across GOA
Collaboration
• Pockets of analytics
collaboration, within Sectors
• Collaboration limited to specific
areas of business
• GOA analytics visualization tool
supports sharing of artifacts
• Within GOA Sectors,
collaboration of analytics
initiatives gains efficiencies
• Metadata and catalog
evolution supports data
dissemination across GOA
• High degree of analytics
collaboration across GOA
• Both data and analytics insights
are subject to collaboration efforts
People & Process
Category
Short Term
(18 Months)
Medium Term
(18 months – 3 years)
Long Term
(3 – 5 years)
Support of
Business Needs
• Visualization Tool provides
advancement in Descriptive
Analytics capabilities
• Most of the time and energy of
analytics is still invested in
accessing and wrangling data
• More analytics has matured to
Diagnostic and Predictive,
based on improvements in
data quality and
completeness of data
• Less time spent on data
wrangling; more time allowed
for data analysis
• Prescriptive Analytics is more
common in most GOA Sectors
• Policy needs are supported by
highly trusted evidence from
analytics
• Little time spent on data
wrangling
• Cost savings found in reduction
in data securing activities
Forward
Thinking
• Analytics activities are mostly
prescriptive in nature; forward
looking is limited by the data and
necessary investments in time
• More Diagnostic analytics
provides insights into forward
thinking
• Through Data Lake
intelligence and monitoring,
opportunities to direct efforts
to data that will yield
maximum business value
• Predictive Analytics and
Prescriptive Analytics provides
insight what the future may bring
• Understanding future needs of
data and analytics based on
advanced monitoring of Data
Lake access and usage
Key Considerations: Governance and Data
• Strategic IMT
– Capital Investment
– IMT Governance
• Enterprise Infrastructure
– IMT Standards
– IT Supports (AMS)
• Steering Committee - EDA Strategy
– Self-serve visualization - technology deployment
– Service management
• Others - Data Governance
– Data Providers (Ministries)
– Data Consumers (GoA, partners, citizens)
Short Term Medium Term Long Term
Oct-Mar Apr-Sep (17/18) Oct-Mar (17/18) Apr-Sep (2018/19) Oct-Mar (2018/19) Apr-Sep(2019/20) Oct-Mar(2019/20) Year 4 Year 5
Enterprise Data Analytics Platform – IMT Strategy Alignment
Architecture
Vision & Roadmap
Advanced Enterprise Analytics Capabilities (Open Analytics)
Self-Service - Visualization Tool
RFQ Operationalize
Rollout Self-Service - Visualization
Data Lake - for Visualization Data Lake - for Big Data Analytics Advanced Analytics Capabilities
Oversight & Governance
Architecture Oversight / Alignment with GOA Data Governance, Data Classification, etc
IRMS / AB Climate Change / FASD (Project)
Cybera PoC Projects
GOA Pilot Projects Data Projects Data Projects Data Projects Data Projects Data Projects
Rollout Visualization Services (by Use Case, and/or Dept./Sector)
Rollout EDAP for (By Dept./Sector)
Refine Architecture
Analytic Service Opportunities
Visualization Tool Procured
Service Model Deployed
EDA Platform - Capital IMT Project
Technology Pillar
Status: Draft
Data Capture & Preprocessing
Technology: Architecture Vision
Data Analyst
Data Lake Data
Consumption
Data
Sources
Government
Sources
GOA Portals
External Partners
Other Sources
Ingestion
Hub / Gateway
APIs
Batch / File
Database
Streaming
Big Data and Enterprise Analytics
Analytics & Data Science Presentation & Visualization
Targets
APIs
Database
Streaming
Batch / File
Government
Sources
GOA
Portals
External Partners
Other Sources Batch
Capture
Streaming
Capture
Storage
Data & Storage Management
Inventory,
Organization,
Optimization Providence
Monitoring,
Tuning Security
Consolidation
Automated Workflow & Self-service Preparation
Profiling,
Quality
Transformation Design,
Development
Semantic
Consistency
Metadata
Discovery
Metadata
Capture
Metadata
Catalog
Metadata
Enhancement
Search &
Distribution
PQR/RFP
Overview
• Enterprise Data Analytics (EDA) Platform Project is a
GoA priority Capital IMT project
– Enables implementation of EDA Strategic Plan
Technology pillar
• Two phases of EDA Platform Project :
– Self-Serve Data Visualization - develop and host interactive
visualizations (charts, graphs, dashboard, maps, etc.) to
improve understanding, interpretation, and use of data
– Open Analytics Technical Infrastructure – design and implement
a "data lake" and analytical tools to facilitate data sharing and
“big data” analytics
Self-Serve Visualization Project
• Enterprise – One Government Approach to meet the
needs of both individual users and the economy of
scale
• Open and fair procurement – focusing on the
capabilities required versus a tool specific
procurement
• Business agility – requirements driven by business
users, to develop and share interactive and dynamic
data visualizations across the enterprise
Visual Analytics
• 3-year enterprise-wide
license with option to
extend to 2023
• Allows for 1,000
creators, 500
concurrent users,
unlimited viewers
• In-house solution with
implementation target
July
C op yr i g h t © 2015 , SAS Ins t i t u te Inc . A l l
r i g h ts r eser v ed .
APPROACHABLE
ANALYTICS
DELIVERS A SINGLE SOLUTION FOR FASTER, SMARTER
DECISIONS
MOBILE
PREPARE
DATA EXPLORATION REPORTING
• Native iOS
and Android
applications
that delivers
interactive
reports
• Join data
from multiple
sources
• Create
calculated
and derived
columns
• Load data
• Perform ad-
hoc analysis
and data
discovery
• Apply
exploratory
analytics
• Create
dashboard
style reports
for web
• Build
interactive
storyboards
SAS ANALYTIC SERVER
ADMINISTRATION
• Monitor SAS
Analytic
server
• Load/unload
data
• Manage
security
MODELING
• Perform
exploratory data
analysis
• Conduct rapid
model
prototyping
• Create analytics
not just reports
Central Entry Point Integration Role-based Views
Rollout Plan
Procurement – SAS Visual Analytics
– Solution Validation – February 10th
– Workshop with Forrester – February 16th
– P.O./Contract Execution – March 24th
• HW Deployment – April to July*
– Setup the Enterprise Infrastructure
• Operations - Enterprise Licensing and Service Catalogue
– looking at different models to cover ongoing costs (license,
hardware/infrastructure/data hosting
– Service options: standard (access); +training; +advisory;
+data development/data science
– User sign-on and tool access
Data Accelerator: Proof of Concept
Projects
Capability Model
Purpose & Approach
Develop an understanding and capabilities of data
analytics technologies to help inform implementation
• Partnership with ministries
• Focus on exploratory analysis
• Leveraged open source technologies
• Leveraged skills from Cybera and the University of
Alberta
• Run in 2 week sprints
Consumer Programs – Data Dormancy
~ 1 million data
records over 5 years
Status of Women – Social Media Analysis
Public Fatality Enquiry- PDF Extraction
Transportation (Collision Data)
• collision location
• intersection type
• collision type
• day of week
• primary event
• road class
• total number of vehicles involved
• collision environmental condition
• collision severity
• collision surface condition
Technology & Skills
• R, r-shiny, plotly,
Python/Pandas/Numpy,
Tableau, Jupyter,
Github, Swift container,
matplotlib, Julia, JIRA,
Google Maps API
33
EDA Program and Services – the
Future
Opportunities
• Self-Serve Visualization
– exploration of data
– sustainable descriptive and diagnostic analysis
• Internal Data Discovery Portal
– Improved search/discovery of information products
• GoA “Data Lake”
– unlocking data assets
– secure access to GoA data
– linking of data
• Centres of Excellence/Communities of Practice
In summary…
We’ve reached the end of the show
Questions?
Thank you
POLICYWISE FOR CHILDREN & FAMILIES
Building Bridges: Connecting Social-Sector Data in Alberta
Jason Lau, PhD Director of Data Operations
June, 2017
POLICYWISE FOR CHILDREN & FAMILIES
PolicyWise for Children & Families
PolicyWise for Children & Families exists to improve child, family and community well-being by leading, creating, enabling and mobilizing
research and evaluation for evidence-informed policy and
practice.
About Us
POLICYWISE FOR CHILDREN & FAMILIES
Our Five Core Functions
• Undertake research, evaluation, communication, and knowledge mobilization
• Conduct, fund, and build policy-relevant research capacity
• Link, analyze, and manage data
• Manage resources and strategic relationships
• Measure and communicate our impact and value
POLICYWISE FOR CHILDREN & FAMILIES
Secondary Data Use
Beyond purpose it was collected for
• Administrative data
• Far larger dataset than for-purpose data collection, but not always the exact data point you wanted to get
Benefits
• Expand the use and value of existing data
• Reduces burden on clients/participants/individuals
• Gain systemic intelligence on performance, outcomes, and impact.
• Help quantify ROI for funders and planning purposes
• Potential for linkage and cross-disciplinary analysis with other existing datasets
POLICYWISE FOR CHILDREN & FAMILIES
Data Initiatives at PolicyWise
Child and Youth Data Lab (CYDL)
• Links and analyzes government administrative data
• For policy relevant research
• With a focus on children, youth and families in Alberta
Secondary Analysis to Generate Evidence (SAGE)
• Data repository for research, service delivery, and administrative data
• Value added service
• Goal: Link data to answer more complex question
POLICYWISE FOR CHILDREN & FAMILIES
Child and Youth Data Lab
Established in 2007, the initiative was intended to:
“Provide the GOA with a picture of the programs and services Alberta’s children and youth are accessing to see the patterns and potential relationships that may exist between government programs and services.”
POLICYWISE FOR CHILDREN & FAMILIES
CYDL Approach
Partnership with ministries
• Governance and Data sharing authority – Children First Act, Health Information Act
• Policy relevant and priority driven analysis
• Collaboration and involvement in research process (Iterative process)
• Integrated knowledge mobilization model
POLICYWISE FOR CHILDREN & FAMILIES
Experiences of Children and Youth: A Longitudinal Project
2 million+
Albertans 0 to 30 years of age
50 million+ service uses over 6 years (2005/06 to 2010/11)
20+ databases; more than 225 data elements
POLICYWISE FOR CHILDREN & FAMILIES
CYDL Data
Linkage
Process
MINISTRIES Extract and submit IRD, postal codes and ARD
HUB Translate postal codes to
dissemination areas
CYDL Link ARD and analyze
AIRS Link IRD and generate
record-linking file
Postal Codes
Record-Linking File
Dissem
inatio
n A
reas
POLICYWISE FOR CHILDREN & FAMILIES
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
All programs
Health
Child support services
Child intervention
Family Support for Children with Disabilities
Child care subsidy
Education
Corrections
Offence charges
Income support learners
Income support
Advanced Education
Assured Income for the Severely Handicapped
Persons with Developmental Disabilities
Court outcomes
Type of charge, charge history
Program and institution details
Household type, enrollment details
Client type, household type
Disability type, household indicators
Type and amount of support
Perinatal indicators, diagnoses, mental health status, high cost users
Reason for referral, type of services provided, history
Type of order, amount of order, reason for file closure
Type of disability, duration of services, file closure reason
Program type, reason for care, duration
Achievement, programs, high school completion
Gender, age, socio-economic status, region of residence, Indigenous status (if available)
Young adulthoodChildhood YouthEarly childhood
POLICYWISE FOR CHILDREN & FAMILIES
https://visualization.policywise.com/P2matrix
https://visualization.policywise.com/P2matrix/Video_tutorial.html
POLICYWISE FOR CHILDREN & FAMILIES
https://visualization.policywise.com/P2dashboard
POLICYWISE FOR CHILDREN & FAMILIES
The Power of the Longitudinal Project
Over-represented programs • In what programs are Child Support Services Dependents
over-represented?
Resilience and Transitions • What is the linkage rate between Child Intervention clients
and Post-secondary students?
Time trends • What types of services did Income Support clients receive in
the past?
POLICYWISE FOR CHILDREN & FAMILIES
Mental health service use
0%
5%
10%
15%
20%
2005/06 2010/11
Master's/PhD
Bachelor/Applied
Certificate/Diploma
University transfer
No credential
Percentage of students aged 18 to 25 who used a mental health service by credential type over time
POLICYWISE FOR CHILDREN & FAMILIES
15%
23%
30%
18%
32%
5%
9% 9%
Post-secondary students Income Support Income Support Learners Offences
Figure 6. Supports and Services used by AISH clients at some point between 2005/06 and 2010/11
AISH Rest of Population
Disability Supports
POLICYWISE FOR CHILDREN & FAMILIES
Publically Released Reports
Program and population profile and dashboard
• Post-secondary Education
• Criminal Offences
• Income Support
• Family Support for Children with Disabilities
• Fetal Alcohol Spectrum Disorder
• Mental Health Service Use
https://visualization.policywise.com/P2dashboard
POLICYWISE FOR CHILDREN & FAMILIES
http://www.edmontonjournal.com/health/province+pledges+million+mental+health+vulnerable+kids/9463608/story.html
Policy Impact
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Data Initiatives at PolicyWise
Child and Youth Data Lab (CYDL)
• Links and analyzes government administrative data
• For policy relevant research
• With a focus on children, youth and families in Alberta
Secondary Analysis to Generate Evidence (SAGE)
• Data repository for research, service delivery, and administrative data
• Value added service
• Goal: Link data to answer more complex question
POLICYWISE FOR CHILDREN & FAMILIES
Models of Secondary Use
CYDL is project-based – Demonstrated policy-relevance of linking GoA data
– Discrete data sets for specific years
– Very laborious
– Linked for specific analyses conducted by PolicyWise; cannot be accessed by other researchers
– Will be destroyed after modest retention period
Repository model
– Create sustained, linked datasets
– Ongoing data transfers and updates
– Accessible (with proper governance) to policy-makers and researchers
POLICYWISE FOR CHILDREN & FAMILIES
SAGE
Administrative Data
Research Data
Community Service Data
Sharing Accessing
Enable and support use to Inform Policy and Practice
SAGE (Secondary Analysis to Generate Evidence)
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Partnership between PolicyWise, Centre for Child Well-Being, and the following not-for-profits organizations: Calgary Food Bank, Families Matter, Calgary Counselling Centre, and South
West Community Resource Centre
Main Objectives: 1. Discovering/Establishing a common meaning
indicator/predictor of poverty 2. Explore opportunities for data sharing to gain intelligence
regarding service use patterns, service overlap between programs, and outcome trajectories of clients
POLICYWISE FOR CHILDREN & FAMILIES
Secondary Use by Design • Client/participant consents
• Informed about collection, use and disclosure
• Provisions for sharing and future use of data
• Incorporate secondary use in proposal and grants • Work with funders to allow for data
management and sharing resources
• Focus on capacity building
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Building a Path
Many Non-Profits lack the resources and expertise – Need to pave the way with clear advice, expertise and tools
– Identify the bright spots along the data-readiness spectrum to take leadership positions
– Data management and data quality are significant issues
– Work with funders to build capacity
Need clear value proposition and incentive
– Beyond reporting
– How can organizations fully participate in research, evaluation, service planning, etc. How can they contribute through data?
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Building a Path
Risk adversity – Protection of vulnerable populations
– Special considerations for Indigenous communities
– Risk should be managed, rather than eliminated; weighed against potential benefit
Ambiguity and uncertainty in boundaries – Distinction between research, evaluation or quality improvement
– Legal (privacy) and ethical boundaries
– But there’s no real legislative barrier in most cases
POLICYWISE FOR CHILDREN & FAMILIES
Legislative Context in Alberta
Non-Profits fall in a gap in privacy legislation – Health Information Act only applies to custodians (providers of
care, hospitals, ministry, etc.)
– Personal Information Protection Act mostly for commercial activity
– Freedom of Information and Protection of Privacy Act applies to public bodies (gov’t and gov’t-ish organizations)
Non-Profits fall in a gap in ethics oversight
– Research Ethics Boards apply to health research. Institutional boards apply to researchers with academic affiliations
– ARECCI partially fills the gap (advice only)
POLICYWISE FOR CHILDREN & FAMILIES
Legislative Context in Alberta
Lack of legislative framework a double-edge sword – Still need to take responsibility as good stewards of data
– Very situation-specific (legal relationships, funder relationships, population vulnerability)
– Need to develop relevant best-practices
Opportunities abound
– Need leadership, organizations to push the envelope
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MyCHILD
Partnership with AHS, Alberta Innovates, WCHRI, GoA – Developed in partnership with the MNCY SCN
– Focus on children with complex medical needs, and children with neurodevelopmental disorders
– Population draws significant resources from AHS and social sector ministries such as Children’s Services, Community and Social Services, Education, etc.
– Currently, limited exchange of intelligence between health and social sector services for these children.
– Integrate economics into analysis to tease out real system costs and potential for efficiencies and integration between services.
POLICYWISE FOR CHILDREN & FAMILIES
MyCHILD Alberta
~2% of kids: Medically
complex (but no neuro-developmental
problems)
~15% of kids: Neuro-
developmental problems (but not medically complex)
~1% of kids: Overlap (medically complex AND neuro-
developmental problems)
All kids
In terms of population
Kids with extra-ordinary needs
POLICYWISE FOR CHILDREN & FAMILIES
MyCHILD Alberta
All kids
In terms of cost
Kids with extra-
ordinary needs have
disproportionately
higher resource
usage, but are not
well-characterized
POLICYWISE FOR CHILDREN & FAMILIES
MyCHILD
Take advantage of anonymous linkage – HIA custodians and FOIP bodies disclosing de-identified data
– Work with OIPC and privacy experts to move towards repository model
– Inform aligned work on a health repository
Focus on context and content
– Address a focused need for program-relevant information
– Gain an early win for the value of bringing data together in a repository to inform policy
– Establish governance for future access
POLICYWISE FOR CHILDREN & FAMILIES
Building Bridges
Vastly different philosophy, perspectives, role in society – Government is influenced by politics, economics, etc., and has the
role of making policy
– NPFs have a broad range of influences (faith-based, volunteer, funding pressures)
But often have the same goals
– Improving lives through social service support
– Serving same or overlapping populations
Data is the point of convergence and collaboration
POLICYWISE FOR CHILDREN & FAMILIES
Gov’t N
FP
https://alberta.ca/information-management-branch.aspx Page 1 of 3 June 2017
Service Alberta Enterprise IM Update
June 2017
Standards Update
Two standards were approved in May.
The revised Data and Information Security Classification standard describes the
four data and information security classification levels used to classify all
Government of Alberta data and information assets. They are: Public, Protected
A, Protected B, and Protected C. This revision aligns with the Government of
Canada’s information security classification scheme, and enables better data and
information sharing practices across jurisdictions.
The Data Security in the Cloud standard defines the controls necessary to
protect Government of Alberta data and information in Cloud environments.
Policy Instruments for the Management of Information Project
Information Management Brach has launched a broad, strategic project to
identify, recommend, and develop policy instruments that will help enable the
GoA reach its vision for the management of information.
This Policy Instruments for the Management of Information project, will provide a
detailed road map of policy instruments to help the GoA achieve its information
management related goals. The Records Management Regulation, Data and
Information Management Policy, and all other associated policy instruments will
be considered.
Foundational work for this project comes from the Results Based Budgeting
initiative, as well as the information management policy consultation sessions
held last fall.
The project team will be engaging the community for feedback, and will provide
communication about the project as it progresses.
https://alberta.ca/information-management-branch.aspx Page 2 of 3 June 2017
Schedule Reengineering
The broader community involved in the Records Retention and Disposition
scheduling process met for a working session at the end of April to discuss future
state process models.
After review of feedback from the working group session and a discussion of the
benefits and risks of both schedule development process models, the Schedule
Reengineering Project steering committee has approved the centralized
approach.
The Schedule Reengineering Project working group and project team will begin
to build out a detailed implementation plan, which will be reviewed and approved
by the steering committee in July 2017.
IM Branch Website launch
The refreshed Information Management Branch website will be launched on
Tuesday, June 13.
The launch will be formally announced via email to various stakeholders
including the GoA IM community (e.g., IM Aware, Senior Records Officers, CIOs)
and federal-provincial-territorial IM groups we liaise with on a routine basis.
Announcements will also be published in Service Alberta’s ‘At Your Service’ and
the GoA online ‘Connector’ newsletters.
The refreshed site offers improved access to policies, guidance, tools, and
education material for anyone interested in information management.
For those familiar with the previous site, be aware that you will be automatically
redirected to the new site; please update your bookmarks accordingly.
Contact us at SA.InformationManagement if you have any questions.
Alberta Records Centre
The Alberta Records Centre (ARC) has calculated their annual statistics for
paper destruction for the 2016-17 fiscal year.
In accordance with good recordkeeping practices, the contents of 74,000 boxes
and 12,000 locked bins were destroyed.
https://alberta.ca/information-management-branch.aspx Page 3 of 3 June 2017
Through the ARC paper recycling program over the past year, the GoA’s
information management program saved 42,918 trees; enough energy for 758
homes; 35,995,901 litres of water; and 4,846 cubic metres of landfill space.
File Room Move
The Central Service Alberta File Room, previously located in the Seventh Street
Plaza Building, has moved to the Brownlee Building.
This file room is used for centralized management of accounts payable records.
As a reminder, Service Alberta offers centralized management of accounts
payable records as a service to all ministries. If your ministry is not signed up for
this service but would like to be, please contact either Jennette Frost or Geneva
Snow.
No Holds Barred Information Management Trivia
Answer Key
1. What are the three goals of the Government of Alberta Information Management
Strategy?
Build enterprise governance and accountability for information.
Transform information collection, access and use.
Maximize the potential for our information.
2. What does “born digital” mean?
“Born digital” refers to content created digitally or electronically, and without an
analogue original or equivalent.
Examples include digital documents, digital photographs, digital audio and video.
3. What Alberta University is named after an early supporter of Records Management?
Grant MacEwan.
In 1957, Richard Hall and Grant MacEwan put forward a motion to ensure that the
records of the value to the province were preserved - “to give consideration to a
vigorous program of collecting and preserving public and historical records and
museum records.” (Journals of the Legislative Assembly of the Province of Alberta
1957, 13th Leg., 3rd Sess., April 4, 1957 at 139).
4. Name three things you can find on the Government of Alberta Open Data Portal.
(any accurate response)
5. What are the four mandatory metadata elements as described in the Government of
Alberta Metadata Core Content Standard?
Creator: the business entity responsible for creating or compiling the original
content of an information resource
Date Created: The date, or date and time, on which the content of an information
resource is created or compiled.
Date Modified: The date, or date and time, on which the content of an information
resource is changed.
Title: The full and formal title given to an information resource.
https://alberta.ca/information-management-branch.aspx Page 2 of 3 June 2017
6. According to Municipal Affairs, how many Albertans held library cards in 2014?
1,261,267
7. What is the longest records period (not including permanent retention) in the
Government of Alberta?
Pursuant to subsections 50(1) and 63(1) of the Vital Statistics Act, birth records
must be retained for 120 years after the date of the birth.
8. What are the four levels of security described in the recently introduced Information
Security Classification Standard?
The four levels are:
o Public
Applies to information assets that will not result in injury to individuals,
governments or to private sector institutions; and financial loss will be
insignificant.
o Protected A
Applies to information assets that, if compromised, could cause injury to
an individual, organization or government.
o Protected B
Applies to information assets that, if compromised, could cause serious
injury to an individual, organization or government.
o Protected C
Applies to information assets that, if compromised, could cause
extremely grave injury to an individual, organization or government.
9. When was the first records retention schedule adopted in Canada? What organization
introduced it?
1890.
The Post Office Department.
10. How much of the world’s data is analyzed and used?
A 2012 study, The Digital Universe in 2020, found that less than 0.5% of all data is
used for analysis.
“IDC estimates that by 2020, as much as 33% of the digital universe will contain
information that might be valuable if analyzed, compared with 25% today. This
https://alberta.ca/information-management-branch.aspx Page 3 of 3 June 2017
untapped value could be found in patterns in social media usage, correlations in
scientific data from discrete studies, medical information intersected with
sociological data, faces in security footage, and so on. However, even with a
generous estimate, the amount of information in the digital universe that is "tagged"
accounts for only about 3% of the digital universe in 2012, and that which is
analyzed is half a percent of the digital universe.”