QQ slide presentation of 12 with note in the note section , 5 references, apa7  d make your recommendations to the organization. . ( use the Analytics Stra

QQ slide presentation of 12 with note in the note section , 5 references, apa7
 d make your recommendations to the organization. . ( use the Analytics Stra

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QQ slide presentation of 12 with note in the note section , 5 references, apa7
 d make your recommendations to the organization. . ( use the Analytics Strategy Template to check your work.)
10. Technology & Infrastructure (Be as detailed as possible and use the Analytics Strategy Template to check your work.)

Is the current technological system sufficient to implement your proposed plan?
Are there any issues with the infrastructure that need to be addressed?
How will the data be managed?
What is the quality control process?
How will data be integrated into the larger organization?
Consider the economics and costs of the new system

11. Future State

Address any gaps that you have discovered.
Make recommendations to the organization to address the gaps.
What reformation needs to take place in order to deliver better healthcare?
What changes do you expect to take place if the system is implemented?

12. Communication Plan

Describe your plans for communicating the data analysis and plan to all the stakeholders.
Including a graph or chart may help you present your findings.
Explain the connection between your findings, your proposal, and executing the organization’s strategy.

This is the conclusion so you need to make your recommendations to the healthcare system or organization. Stakeholders
and users
A stakeholder is
a person (or
group of
persons) that
impacted by,
users of, or
otherwise have
a concern (or
interest in) the
and deployment
of analytical
throughout the

developing an
strategy, it is
important to
understand what
each of the
likely analytics
stakeholders will
require, and
approaches to
ensure they are
getting what
they need.



Use cases

♦ Clearly identify who the key stakeholders are in the analytics
strategy. Major stakeholder groups in healthcare include:
♦ Patient – The person whose health and healthcare

experience we’re trying to improve with the use of analytics
♦ Sponsor – The person(s) who supports and provides

financial resources for the development and implementation
of the analytics infrastructure

♦ Influencer – A person who holders considerable influence
over the support of analytics initiatives.

♦ User – A person in the organization who accesses analytical
tools, or uses the output of analytical tools, to support
decision making and to drive action.

♦ To what extent are the stakeholders currently engaged in
developing the strategy and/or the use of analytics within the
healthcare organization?

♦ Reference Appendix A for a stakeholder analysis form you can
use to capture key details.

♦ Where are stakeholders located within the organization? (attach
an organization chart, if available and applicable.)

♦ Obtaining as much information as possible about the possible
uses of analytics will help to: identify any gaps in analytics
capabilities, and reduce the likelihood that critical analytics
needs will be missed.

♦ Apply analytics use cases to help identify:
♦ what data elements are most important,
♦ what indicators will be necessary to calculate, and
♦ what types of usability factors (such as dashboards, alerts,

and mobile access) need to be considered.
♦ decisions for which analytics insight is required
♦ actions that get triggered by analytics indicators
♦ risks that analytics identify and/or help to mitigate
♦ what key processes need to be monitored and/or improved
♦ what indicators are required to monitor quality and

♦ Use the Analytics Use Case document available in the

downloads section of to
document key analytics use cases from your stakeholders.

♦ Provide a summary of the most important (or highest priority)
use cases in the strategy document.

♦ Other considerations to document, where appropriate, include:
♦ Who is using the existing analytics tools within the


♦ What is the level of stakeholders’ analytical sophistication
(For example, do the “dabble” in Excel, or are they expert

♦ How are analytics tools being used? (For example, are
analytics being used primarily for operations, or research, or
are analytics being used at the clinical point-of-care?)

♦ What business, quality, and/or clinical questions are being
answered? Perhaps, more importantly, which ones are NOT
being answered due to limitations in current analytic

♦ How can stakeholders’ use of (or access to) data, analytics,
and overall business and clinical insight be improved?

Process and
An analytics
strategy must
address: how to
determine which
data is most
necessary for
quality and
how the data is
managed and its
quality assured,
and how data
links back to
processes for


Data sources

Data quality

governance &

♦ Data is the raw material of analytics, and data and processes
are very closely linked. It is the business processes and clinical
workflows that generate data, and these same processes and
workflows must be well understood to provide critical context to
data for analysis.

♦ What are the sources of data in the organization? What
operational and clinical systems contribute to source data?

♦ Are all necessary data sources available for analytics? Are
these data sources linked (where possible) and integrated into a
data store suitable for analytics such as an Enterprise Data
Warehouse (EDW)?

♦ What data is necessary from source to develop analytics that
address key business issues?

♦ How good is the quality of available data?
♦ What are possible/likely sources of data quality issues?

♦ How well do the source systems enable high-quality data
input (i.e., through extensive input validation, etc.)

♦ What gaps in data exist? Do gaps in data exist because data is
not collected in source systems, or because it’s not integrated
into accessible data stores?

♦ Is there a culture of strong data stewardship and governance
within your organization? What policies and procedures exist for
governing data?

♦ Who is responsible for:
♦ Monitoring and evaluating data quality, identifying issues,

and making appropriate recommendations to fix data quality

♦ Ensuring that any modifications to data storage and
management are in line with accepted policies and

♦ Ensuring that data is used properly and that it is accessible
to those who need to use it.


Data model

Gap Analysis

♦ Helping to establish enterprise-wide standards for data
quality and usage.

♦ Most quality improvement methodologies monitor progress and
evaluate performance and outcomes using indicators based on
process data.

♦ This requires a strong alignment between key business
processes and the data that measures those processes.

♦ As part of the analytics strategy, you should consider:
♦ if and how current business processes are documented, and
♦ how data items are mapped to these documented business

♦ whether any formal business process management (BPM)

tools are utilized to catalogue processes, workflows, and
their associated data points.

♦ Data modelling helps to identify the many sources of data, and
understand how interconnected data is. The model can also
highlight the potential uses of data within a healthcare
organization by showing connections between data sets that
may exist beyond department or program boundaries.

♦ If available, attach a high-level data model illustrating key
information sources within your healthcare organization.

♦ Clearly outline the gaps in data sources, and in how data is
managed within the organization. Prioritize all gaps in the
understanding, acquisition, and management of both data and
processes knowledge so high-priority gaps can be addressed
quickly. For example, if your organization does not have a
formal data model, you might consider having a data architect
construct a “current state” data model, then examine
opportunities for enhancements in the data model.

Tools and

Analytics needs

Application types

♦ Analytical tools must meet the requirements of analysts building
analytics solutions/applications, and the end-users who will rely
on the resultant information and insight.

♦ It is important to align analytics tools, methods, and capabilities
♦ Business and quality questions that need to answered
♦ Relevant quality goals and Key Performance Indicators

♦ Data available
♦ Stakeholder requirements and analytical sophistication
♦ Appropriate statistical analyses
♦ Tools/software available

♦ Conduct an inventory of existing analytics tools to determine if:

Gap analysis

♦ Capability is missing that will be required (for example, tools
that enable predictive modelling)

♦ Existing capabilities exist that may not be widely known (for
example, pockets of expertise in the use of specialized tools
for simulation, and other advanced analytics)

♦ Different types of analytics tools that may be in use within the
organization include:
♦ Statistical – Used for deeper statistical analysis not available

in “standard” business intelligence or reporting packages
♦ Data visualization – Used for developing interactive, dynamic

data visualizations that aid with analysis
♦ Data profiling – Helps to understand and improve the quality

of an organization’s data.
♦ Data mining -Analysis of large data sets to uncover unknown

or unsuspected relationships.
♦ Text mining – Analysis of unstructured, text-based data to

extract high-quality information.
♦ OLAP -Allows analysts to interactively explore data by

drilling-down, rolling up, or “slicing and dicing” data.

♦ Do the analytics teams in the organization have all the
necessary tools to accomplish the jobs they are tasked with?

♦ What tools are required that would be more appropriate or more
efficient for the types of analytics required?

♦ Identify viable best-of-breed vendor solutions that meet
requirements; custom-build from scratch if necessary (or if
participating in research).

Team and
PEOPLE are, by
far, the most
developing an



Gap analysis

♦ An analytics strategy must consider:
♦ What kinds of people (and the skills they bring) are

♦ How to attract the best analytical talent
♦ How to retain the analytic talent within your HCO

♦ Different resource management models exist for analytics:
♦ “centralized” analytics office (most or all analysts reside in a

central office providing)
♦ “distributed” analytics resources (most or all analysts reside

within departments and/or programs)
♦ “virtual” center of excellence / competency center

(many/most analysts reside within various departments
and/or programs, but a core or “home base” exists to
provide a common set of standards, training, and tools
regardless when they sit within the organization).

♦ Do we have enough of the right types of people?
♦ Where do analytics professionals reside?
♦ To whom do they report?

Future state

♦ What support is available for analysts? What support do they

♦ I.e., single voice of a distributed analyst group
♦ How are they trained, and what training opportunities are

♦ What are the standard hiring and performance requirements?
♦ What key skills are required and need to be added to the

analytics team? What types of professionals can best provide
these skills to the team?

♦ What training or skills development needs do the team have?
♦ Is the team adequately staffed with the right people?

♦ Once the strategic goals are addressed, what will the analytics
team look like with respect to:
♦ Number of resources adequate to do the job
♦ Composition (types of people based on education

background and related work experience)
♦ Roles (Are the right people doing the right time of work? Are

people going to be more generalists and taking on multiple
types of roles, or will people be more specialized)

♦ How can the organizational structure (relating to location and
support of analytical professionals) be structured (or improved)
to better meet the professional needs of analysts, and better
meet the analytical needs of the organization?

Technology &
Ideally, the
analytical needs
of an
organization and
requirements to
achieve those
needs will figure
prominently in
analytics and IT

Current state



♦ Document currently available technology and infrastructure that
support analytics. This includes application and database

♦ More importantly, document how well current infrastructure
(networks, servers, and storage) copes with analytic demand,
and describe any performance issues or other gaps that may be
limiting analytic capability and usefulness. Examples of things to
consider include:
♦ Overall performance (how long do reports and other queries

take to run)
♦ Reliability (how often do downtimes of infrastructure occur)

♦ What current data management systems are in place, and how
well are the systems meeting current demand for analytics. Data
management systems to consider include:
♦ Data Warehouses (DW)
♦ Operational Data Stores (ODS)
♦ Data Marts (DM)
♦ General storage and backup

Gap analysis

♦ What systems are in place to enable data integration? Are these
systems sufficient for current (and anticipated future)
♦ Extraction / Load / Transformation (ETL)
♦ Data Quality (DQ) (cleansing, profiling, management)
♦ Service Oriented Architecture (SOA)
♦ Business Event Monitoring (BEM)
♦ Complex Event Processing (CEP)
♦ Business Process Management (BPM)
♦ Business Rules Engine (BRE)
♦ Enterprise Information Integration (EII)

♦ What gaps exist in the current technology and infrastructure that
constrain analytics within the organization. Considerations
♦ Are reports, queries, and other tools taking too long to

♦ Are data integration (i.e., ETL) services validated for data

quality, and optimized for best performance?
♦ Do servers have sufficient memory?
♦ Are server and/or network reliability issues causing

downtimes or other issues that are affecting the ability to
make accurate and timely decisions in the organization?

1. Future State Gap analysis

Next Steps

♦ Summarize all the gaps identified in previous

♦ Prioritize the gaps based on importance/impact to
the organization and the effort required to resolve
the gap and/or mitigated risks associated with the
♦ Aim for high-impact and low to medium effort

projects initially to achieve early “wins” and build
on success.

♦ Use the Gap Analysis Form available in the
downloads section of to help construct
your strategy’s gap analysis.

♦ What options have been considered for strategic
change and on what basis have the decisions been
made on which to progress?

♦ What are your organization’s priorities for addressing
identified gaps?

♦ Who is responsible for undertaking work to address
specific gaps? Will this take away from “regular”
work and responsibilities? How will this impact
overall performance of the analytics and IT teams?

♦ What timeframes is the work expected to be
performed in?
♦ One to three years is a typical timeframe to

consider. Planning too far ahead leads to limited
returns because of changing technology and
changing needs of the healthcare organization.

♦ When all important gaps have been addressed, will
the strategic vision described earlier be achieved?

♦ How do we expect the key performance indicators
and outcomes to change as we implement ?

Note: This section should NOT include a detailed
project plan to achieve each of the identified strategic
priorities. Once the overall strategy is approved,
detailed project plans (where necessary) can be drawn

2. Communication

Who will this strategy
be communicated to?

How will this strategy
be communicated to

♦ Plan for communicating the content of the strategy,
and ongoing progress, to all stakeholder groups
(taking into account differing needs/ expectations/
levels of involvement)

♦ How do stakeholders expect or prefer to be notified
when this strategy document is released (or
updated)? Different preferences may include:
♦ Full document format
♦ Abridged / executive summary
♦ Slide stack (i.e., Powerpoint) file
♦ In-person overview
♦ Presentation

♦ How often does each stakeholder (or stakeholder
group) need to be notified of updates to the

♦ In all likelihood, multiple approaches will need to be
employed to ensure that all stakeholders are kept
appraised of the analytics strategy release and
update cycle.

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