goa enterprise data analytics strategic plan - …€¦ · · 2017-06-15goa enterprise data...

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 ‘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 ‘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 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


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


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


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


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.”


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


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


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


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


https://visualization.policywise.com/P2matrix

https://visualization.policywise.com/P2matrix/Video_tutorial.html



https://visualization.policywise.com/P2dashboard




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?


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


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


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






http://www.edmontonjournal.com/health/province+pledges+million+mental+health+vulnerable+kids/9463608/story.html

Policy Impact




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


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


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)


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


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


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?


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


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)


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


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.


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


MyCHILD Alberta

All kids

In terms of cost

Kids with extra-

ordinary needs have

disproportionately

higher resource

usage, but are not

well-characterized


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


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


Gov’t N

FP


[email protected]

[email protected] 780 408 8732

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


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.



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://natlib.govt.nz/files/reports/research-borndigital2017-report.pdf


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://www.emc.com/collateral/analyst-reports/idc-the-digital-universe-in-2020.pdf

https://www.theguardian.com/news/datablog/2012/dec/19/big-data-study-digital-universe-global-volume


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.”


goa enterprise data analytics strategic plan - …€¦ · · 2017-06-15goa enterprise data...

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