Contents

Appendix - Data Strategy

Appendix - Data Strategy

Templates

These are forms and templates associated with building a data strategy. This streamlined approach to drafting a data strategy was derived from several sources1,2,3,4,5. It is highly recommended that you collect as much data as possible in the course of developing a data strategy.

Data Strategy Assessment - Participant Template

This document is a guide to collect key information points for the construction and drafting of an institution’s data strategy.

Participation in this data collection process will be voluntary and participants must give their consent to the researcher so that their inputs on the following questions may be ethically included in the data strategy document.

The researcher may collect this data through any number of methods (e.g., interviews, open conversations, focus groups, response form, etc.) so long as the participants feel comfortable and able to submit their most complete answers to the questions. Considerations include selection of interview environment, time of day, or other factors that may optimize or enhance the interaction between researcher and participant.

The researcher may refer to the Interviewer Template for additional information on clarity and context of the questions during the collection process with the participant.

Questions

Organization

  • What are the institution’s mission, values, and vision statement?

  • What are the institution’s strategic objectives and strategic scope?

  • What are the institution’s strengths, weaknesses, and distinct competitive advantage in terms of product ease of use and range, price, distribution, marketing, service, or processes?

  • What is the institution’s product portfolio performance (by product/market segment)?

  • Where are the root causes of difference between results and stated objectives (using gap analysis)?

  • Who are the customers for the products?

  • What political, economic, social, technological, and environmental opportunities and threats does the institution need to consider?

People

  • Who are the key data leaders?

  • What are significant stakeholders saying about the institution’s data analytics and data teams?

  • Which internal leaders are more likely to use data analytics and make data-driven decisions?

  • Why do internal consumers use data?

  • What is the type and amount of work within the institution compared to available talent supply and demand?

  • Is talent aligned with business objectives, stakeholders, and customer needs identified earlier?

  • How well does the organization retain and engage people in teams that support the data lifecycle?

  • Do we have the right learning and development opportunities to build new capabilities and strengthen existing skills?

Technology

  • What are the current and proposed data architectures for the data lifecycle?

  • What is the capability and capacity to store and process data?

  • What languages and applications do people use in each stage of the data lifecycle?

  • What are the institution’s latest developments and trends in data technology?

  • Who are our technology partners?

Processes

  • How well are data project planned and prioritized?

  • How effectively are the benefits of work tracked?

  • How strong is the capability to deliver?

  • How efficient are ETL processes?

  • How rigorous is development lifecycle management?

  • How easy is it to deploy data products/services to customers?

  • How thoroughly do we review and revalidate outputs and processes?

Data Assets

  • How well is data governed?

  • How well is current data integrated?

  • How effective is master data management (MDM)?

  • Are data quality assurance and data quality control successful in ensuring data is “fit” for its purpose?

  • Is data structured and persisted for appropriate use cases such as (your institution’s data scenarios)?

  • Does the institution have unique data only it can exploit?

Data Strategy Assessment - Collection Form

Add columns for as many participants as you have.

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What are the institution’s mission, values, and vision statement?

What are the institution’s strategic objectives and strategic scope?

What are the institution’s strengths, weaknesses, and distinct competitive advantage in terms of product ease of use and range, price, distribution, marketing, service, or processes?

What is the institution’s product portfolio performance (by product/market segment)?

Where are the root causes of difference between results and stated objectives (using gap analysis)?

Who are the customers for the products?

What political, economic, social, technological, and environmental opportunities and threats does the institution need to consider?

Who are the key data leaders?

What are significant stakeholders saying about the institution’s data analytics and data teams?

Which internal leaders are more likely to use data analytics and make data-driven decisions?

Why do internal consumers use data?

What is the type and amount of work within the institution compared to available talent supply and demand?

Is talent aligned with business objectives, stakeholders, and customer needs identified earlier?

How well does the organization retain and engage people in teams that support the data lifecycle?

Do we have the right learning and development opportunities to build new capabilities and strengthen existing skills?

What are the current and proposed data architectures for the data lifecycle?

What is the capability and capacity to store and process data?

What languages and applications do people use in each stage of the data lifecycle?

What are the institution’s latest developments and trends in data technology?

Who are our technology partners?

How well are data projects planned and prioritized?

How effectively are the benefits of work tracked?

How strong is the capability to deliver:

How efficient are ETL processes?

How rigorous is development lifecycle management?

How easy is it to deploy data products/services to customers?

How thoroughly do we review and revalidate outputs and processes?

How well is data governed?

How well is current data integrated?

How effective is master data management (MDM)?

Are data quality assurance and data quality control successful in ensuring data is “fit” for its purpose?

Is data structured and persisted for appropriate use cases such as (your institution’s data scenarios):

Does the institution have unique data only it can exploit?

Data Strategy Assessment - Interviewer Template

Background

Keeping up with the rapidly evolving data technology landscapes requires great efforts on the part of organizations. Data strategy is at the core of any successful data operations action plan2. Data operations itself is a recent concept built upon several past information technology management (ITM) and data management (DM) efforts; popularized by Andy Palmer in 20155. In order for data operations to enable data management practices and optimizations, it must be situated within a vision and plan that make organizational transformation of data practices achievable. This requires an organization to cyclically audit and assess their current needs and future desires for data technologies that hierarchical align with upper-level institutions’ visions, missions, and goals. As a result, defining and integrating a strategy for data operations is at the center of every data practice and each business requirement.

Rationale

In order to enable and persist research efforts in alignment with customer and stakeholder requirements, institutions must have a clear path from vision to action plan for their data operations and data management practices. This path is necessary to scope both personnel and data architectures for annual budgets and contract requirements processes. As a result, this scope must be reviewed and adapted persistently to ensure that an institution is mobilizing its resources to maximize its outputs and return on investment (ROI) in alignment its business strategies.

The ciuTshi data strategy process is derived and adapted from several sources to capture the current state of data operations and necessary evolutions that must take place in the coming fiscal years. This approach leverages situational analysis in order to garner a full-spectrum understanding of the data environment for institutional customers and stakeholders. This understanding will guide acquisition and deprecation of data architectures and assets for each fiscal year while accounting for potential challenges and innovative opportunities. This allows leadership to submit improved business requirements per project; broadening external engagement through more specific data operations language and data knowledge areas. This specificity opens dialogues that are more applicable to stakeholders and foster requirements that are rapidly achievable and deliverable to customers seeking improved contributions within their associated communities or practice.

Objectives

  • Perform data strategy reviews every two years to enable adaption and evolution of data practices

  • Align data operations to enable business strategies

  • Enable data operations to improve data products and services

  • Share lessons learned on data operations successes and challenges with customers and stakeholders

Method - Situational Analysis

Situational analysis is a method for assessment of current states for any number of environment factors2. As derived from Harvinder Atwal’s Data Ops works2,5, situational analysis will focus on five key areas:

  • Organization - These questions focus on common and tacit knowledge associated with data operations’ status, paradigms, and challenges.

  • People - These questions garner understanding on:

    • Leadership - How decision-makers use data products, services, and analytics.

    • Teams - Understanding roles, resource allocation, and personnel skills/retention.

  • Technology - These questions focus on current and planned data technology capabilities.

  • Processes - These questions capture the levels of data engineering, data analysis, and data science maturity.

  • Data Assets - These questions garner understanding of how the institution manages data, including accessibility and improvability.

Note: Situational analysis should not seek to create solutions or objectives in the course of data collection and analysis. Its purpose is to make planning a strategy more robust; not to be the plan itself. This requires strict observation by the researcher for any cognitive biases or logical fallacies that may be presented by a particular question or data point.

Situational analysis can be paired with gap analysis to ensure institutional alignment of the data strategy with upper-level missions, goals and key performance indicators (KPI). The data strategy will then be:

  • Defined and disseminated to leadership and the data team to ensure understanding and compliance.

  • Implement the action plans and record metrics to ensure the implementation stays within data strategy scope and timeline

  • Monitor and adapt the action plans as needed, ensuring shifts are reflected in the proceeding data strategy documents

What follows are the participant template questions and additional details to assist the researcher in collecting data points and expanding the participant inputs beyond the initial questions.

Questions
Organization

  • What are the institution’s mission, values, and vision statement?

  • What are the institution’s strategic objectives and strategic scope?

    • What are the key performance indicators (KPI) the organization cares about?

    • What are the trends?

  • What are the institution’s strengths, weaknesses, and distinct competitive advantage in terms of product ease of use and range, price, distribution, marketing, service, or processes?

    • Who are the leading competitors?

      • How do they compete against the institution?

  • What is the institution’s product portfolio performance (by product/market segment)?

    • How is each evolving?

    • What is the growth strategy?

  • Where are the root causes of difference between results and stated objectives (using gap analysis)?

    • Lack of skills?

    • Wrong structure?

    • Wrong system?

    • Other?

  • Who are the customers for the products?

    • How do they differ from ideal customers?

    • What are the customer needs?

    • Are they satisfied?

    • What are future customer needs?

      • How does the institution identify them?

    • How do customers find the institution?

      • What are their pain points in dealing with the institution?

    • How does the institution continually improve relationships with customers?

    • Are the institution’s customers loyal and/or happy?

    • How do the institution’s customers see us?

  • What political, economic, social, technological, and environmental opportunities and threats does the institution need to consider?

People - Leadership

  • Who are the key data leaders?

    • At what stage of the “data-driven organization” buy-in process are they?

  • What are significant stakeholders saying about the institution’s data analytics and data teams?

  • Which internal leaders are more likely to use data analytics and make data-driven decisions?

    • Who should be the ideal customers for data analytics be?

  • Why do internal consumers use data?

    • What are their needs?

    • Are requirements fulfilled?

    • What are their future needs?

People - Teams

  • What is the type and amount of work within the institution compared to available talent supply and demand?

    • Against each data role?

    • Rough measure of time allocated to:

      • Stakeholder management

      • Development

      • Migration

      • Data cleansing

      • Modeling

      • Monitoring

      • Measurement and reporting

      • Process improvement

  • Is talent aligned with business objectives, stakeholders, and customer needs identified earlier?

    • If not, where are the significant gaps?

  • How well does the organization retain and engage people in teams that support the data lifecycle?

    • What are the turnover rates?

    • Why do people leave?

    • What are levels of job satisfaction and engagement?

    • How well are we growing data talent?

    • What are the growth paths and changeover points for people?

  • Do we have the right learning and development opportunities to build new capabilities and strengthen existing skills?

Technology

  • What are the current and proposed data architectures for the data lifecycle?

  • What is the capability and capacity to store and process data?

    • Is there access to scalable computing resources such as:

      • Virtual machines (VMs)?

      • Platform-as-a-Service (PaaS)?

      • Container management?

      • Other?

    • Is there the ability to process streaming data?

    • Is scalable storage?

    • What capability to store non-structured or non-relational data?

  • What languages and applications do people use in each stage of the data lifecycle?

  • What are the institution’s latest developments and trends in data technology?

    • Which are we currently pursuing?

  • Who are our technology partners?

    • What are their strengths and weaknesses?

Processes

  • How well are data project planned and prioritized?

    • How aligned are plans to the organization’s objectives, stakeholders, and customer needs?

  • How effectively are the benefits of work tracked?

    • How efficient is the feedback loop to new opportunities?

    • How good is the ability of data teams to sell their work?

    • How well do data teams drive business and customer change?

  • How strong is the capability to deliver?

    • Data preparation?

    • Data visualizations?

    • Data models?

    • Analytics?

    • How fast can we validate data models?

    • Are models explainable?

    • Do we have the necessary range of access to algorithms/SMEs?

  • How efficient are ETL processes?

    • What types of data and code testing occur?

    • What is the capability to schedule processes?

  • How rigorous is development lifecycle management?

    • Is there effective:

      • Revision control?

      • Configuration management?

      • Quality assurance?

      • Release and deployment management?

      • Operations monitoring?

      • Knowledge management?

      • Collaboration?

  • How easy is it to deploy data products/services to customers?

    • How long to generate and update reporting?

    • How easy is it to launch experiments?

    • How quick is it to update existing data products?

  • How thoroughly do we review and revalidate outputs and processes?

    • How often do we reuse existing developments?

Data Assets

  • How well is data governed?

    • How sensitive data secured and privacy protected?

    • Is non-sensitive data easily accessible?

    • Do we have the right data owners and data stewards?

  • How well is current data integrated?

    • How easy is it to integrate new data?

    • Can data lineage and data provenance be tracked across the data lifecycle?

    • How quickly can data be provisioned to end users?

  • How effective is master data management (MDM)?

    • How well is reference data maintained?

    • How well is metadata managed so data can be easy to locate and understand?

  • Are data quality assurance and data quality control successful in ensuring data is “fit” for its purpose?

    • Is data integrity maintained?

  • Is data structured and persisted for appropriate use cases such as (your institution’s data scenarios)?

    • Relational model?

    • Flat tables?

    • Cubes?

    • Files?

    • Streams?

    • Is there the ability to process streaming data?

  • Does the institution have unique data only it can exploit?

    • What data are we not capturing or storing that we should?

    • What are the useful external data sources we are not using?

Sponsorship

The organization’s data manager role and their chain of command are the advocates for data-driven practices and the adoption of those practices with stakeholders and customers.

Data Strategy Assessment - Report Template

Introduction

This document describes the general data strategy report template. The vision, mission, and objectives that guide the data strategy will be outlined. These points will be followed by the planned operational model and a description of the necessary roles and responsibilities. Data operations will then be contextualized within the evolving state of institutional data architectures, culminating in the roadmap which broadly describes the incremental realization of the data strategy for the biennial report period.

Note: More detailed, architecture-specific versions of this report may be drafted within other secure systems as required.

Operational Model

The operational model section covers the organization and team considerations for the data strategy period of performance. A primary consideration is that data operations must consolidate essential data governance challenges for improved data strategy definition and planning for the biennial report period.

Vision

The vision statement for the data strategy must encapsulate the necessary future state of the institution’s data infrastructure for enhanced data asset management. This will support the broader vision, but from a technology and information systems perspective. The vision statement must also point to key groups or people whom benefit from, or are determinant of, the vision’s implementation.

Mission

The mission statement for the data strategy establishes the current tasks undertaken by the institution’s data professionals in support the data mission. This fulfillment model generally encompasses support of projects through collaborative efforts from ITM, data architecture, and data asset management personnel. Much like the vision, the mission will shift with the institution’s project requirements and annual mission statement changes.

Objectives

The scope and scale of the data strategy mission covers several key objectives that must be addressed within the biennial report period. These must weave together the scales of data infrastructure with the need of data asset utilization across systems where metadata, metrics, and parity are critical to data operations practices.

Organizational Operations

The organizational operations section will establish alignment between data operations and the institution’s mission, priorities, and goals. This section will improved clarity on how the operational model will aid alignment of data efforts toward overall organizational success.

People

The people section covers the data personnel considerations for the data strategy biennial period of performance.

Roles and Responsibility

This section should list key roles and subordinate roles essential to guidance and completion of data strategy objectives. Each role will include a list of primary responsibilities which distinguish the roles within the context of the proceeding data strategy sections. This clarification on what roles achieve in the scope of mission, vision, and goals helps create a lexicon which reduces conflict between data teams while encouraging maximum collaboration between essential data personnel.

Talent and Skills

This section will list essential talents and skills seen as critical or essential for the completion of objectives during the biennial data strategy period. This may include hardware, software, organizational, educational, interpersonal, and any other category of skill or talent needed to achieve positive results toward the mission, vision, and institutional priorities.

Data Operations

The data operations section covers the data asset process considerations for the data strategy period of performance.

Data Governance

This section covers shifts in data governance as a core element of the data operations standard for guiding data management efficiencies. Shifts may cover changes to requirements processes, task management system practices, version control guidance, quality assurance practices, security updates, and revised ethics for data asset utilization. This has direct implications for the biennial strategy’s handling of data availability, data access, and resource prioritization.

Data Lifecycle Management

This section covers the direct system-level considerations for the biennial data strategy. Data lifecycle management is the engine of data operations, buffered by data governance to allow data engineering personnel to execute requirements with increased efficiency. This covers changes to data storage, modeling, analytics, data integration, metadata, and cataloging of data assets from raw state to end-point delivery.

Trust and Security

The biennial data strategy should enable a strong link between operations and security. This includes bridging the gaps in trusted systems and personnel with the data infrastructure accesses that perpetuate efficient data asset delivery. Institutional and community factors should be accounted for as regulations and license agreements for both systems and assets can shift the security and trust paradigm for data operations customers and stakeholders.

KPIs and Metrics

KPIs and metrics will be a significant part of the data strategy as they help track outputs, outcomes, and value generation per process for internal customers and external stakeholders. This tracking makes future planning efforts simpler and more achievable as the vision increasingly integrates data requirements and task management practices.

Data Architecture

The data architecture section covers the data technology and data asset considerations for the data strategy period of performance. Data assets depend on data architectures ability to facilitate assets per requirements to customer end-points. A large part of this relationship are the tools and technologies that support their management and governance within the organization’s information systems.

Technology and Tools

This section covers essential considerations for maintenance and shifts in tools and technologies for data management. This includes software and programming languages essential to process and regulate data assets across systems. This focus ensures that the data strategy accounts for timelines to upgrades and license technologies in a manner that achieves continuity of data operations within and between strategy periods.

Architecture

This section covers essential considerations for cloud and on-prem data architecture resources. The data strategy requires careful observation of data architecture resources, their maintenance, and the metrics that dictate how these systems should shift between biennial planning periods. The manner at which these choices drive sustainable parity between data architectures on different information systems is at the core of this sections purpose. This consideration allows data asset governance and management to be more easily maintained and for portability of practice to customers and stakeholders.

Data Operations and Management Connections

This section covers the overlap between tools, architectures, and practices in data operations for asset management. The focus here should be on the management and security of data asset externalities in data operations. These elements can be subdivided into two initial areas of concern: lineage of acquisition and processing to mastered internal consumer data mart; export and delivery of practices and services to external stakeholders.

Internal Customer Data Mart

With data management buffered by data governance within broader data operation practices, data architecture should at a minimum facilitate internal access to search of data assets. This access should be permissable to expected organization security and access levels of the assets and systems. Once accesses are established, this section should define the manner in which internal customers can request support and additional dimensionality for project associated data services. Commonly, this service layer will center on data catalog and requirements elements of data operations.

Transition of Products and Services to External Stakeholder

With clearly defined delivery and deprecation standards obtained through the requirements practices, this section should focus on packaging practices for data assets and services from appropriately secure systems to external stakeholders as part of project completion. This includes additional features of quality assurance and integration that must pass both organization and customer standards for final requirement fulfillment. The data strategy should account for any critical gaps in this set of practices to ensure essential revision and adoption of data operations practices within evolving data architectures.

Roadmap

This roadmap covers the cumulative path of actions to be taken during the biennial data strategy period to realize the data operations’ requirements outlined in above sections.

Action Plans toward Vision and Objectives

This section lays out the two year timeline for all elements of the data strategy. Though this plan may change per organizational shifts, the action plan must be pursued to its fullest extent possible. Each of the above sections will have their own set of action plans making tasking per roles more simple and equitable for the array of personnel skills available. This consideration for delegation across sections and personnel should be seen as an opportunity to review and revise both plans and ideation towards essential integration to the institution’s vision and objectives for the expected years encompassed by the data strategy.

The organization may shift the format and systems through which action plans are conceived and executed. The core of the action plans utilization within the data strategy is in the consistent accountancy of critical shifts to any element of data asset management and governance. These considerations permit leadership to enable valuable feedback to data professionals regarding the strategy and the ultimate value delivered to the business model and the institutional array of customers and stakeholders.

Rubric

The ciuTshi metamodel for metadata contains a set of baseline criteria. This can be adjusted based on the specific language or model metadata requirements. Benchmarks and metrics are flexible elements that can guide and enrich the metadata model for the institution’s specific metadata needs.

  • Benchmark is the expected suitability measure or criteria for the metadata element.

    • essential elements are metric elements of information needed to ensure data retained in done so for measurable reason(s).

    • non-essential elements are elements that may not be relevant to the raw data asset of the institution in charge of the data asset.

    • recommended elements are recommended in cases where the raw data asset has set conditions upon it utilization or complexities in its interpretation.

  • Metrics is an extensible array of quantitative and qualitative features associated with the data asset element and can be augmented to suit an institutions metric requirements. weight is the only default feature in metrics

    • weight by default is set to 1 for each metadata element.

    • field_category has three variables: direct; indirect; and mixed measures.

      • Direct measures are first-hand data points collected by the research.

      • Indirect measures are second-hand sources used by the researcher.

      • Mixed measures are a combination of direct and indirect.

For more information, refer to the content management document.

field_name

category

definition

benchmark

metrics

ds_year

strategy

year the data strategy was published

essential

[‘weight’:1]

ds_participant

strategy

number of participants involved in the data strategy research

essential

[‘weight’:1]

ds_researcher

strategy

number of researchers involved in the data strategy research

essential

[‘weight’:1]

ds_dates

strategy

the timeframe in which the research was conducted

essential

[‘weight’:1]

ds_location

strategy

the settings in which the research was conducted including times, locations, and other environmental and ethnographic factors

essential

[‘weight’:1]

ds_methods

strategy

the research methodologies used to conduct the data strategy research per section and sub-section

essential

[‘weight’:1]

ds_model

strategy

the analytic models used on the research data

essential

[‘weight’:1]

ds_outcomes

strategy

description of research outcomes not covered in the final data strategy document

essential

[‘weight’:1]

ds_docs

strategy

location of the data strategy documentation

essential

[‘weight’:1]

ds_organization

strategy

Data points that capture common and tacit knowledge associated with data operations status, paradigms, and challenges

Obtain and analyze all data points to enable an action plan that establishes and enriches institutional data strategy alignment with upper-level organizations and stakeholder expectations (at least 70% compliance). Need to obtain and code data points from 100% of technology leadership with each covering at least 80% of the section topics; 100% cumulative coverage on section topics

[‘weight’:1, ‘field_category’:’direct’]

ds_people

strategy

Data points that capture how decision-makers use data products, services, and analytics and understanding data team roles, resource allocation, and personnel skills/retention

Obtain and code all data points to guide 100% of leadership and data team personnel towards the data strategy to enable data operations action plan coverage and inclusion per role. Need to obtain and code data points from 100% of technology leadership with each covering at least 80% of the section topics; 100% cumulative coverage on section topics

[‘weight’:1, ‘field_category’:’direct’]

ds_technology

strategy

Data points that focus on current and planned data technology capabilities

Obtain and analyze all data points to establish 100% of the current data technology architecture, enable planning on acquisition of enhanced/replacement tool (50% or more; budget dependent), and deprecation of obsolete and under-performant data technologies (80% or more, operations dependent). Need to obtain and code data points from 100% of technology leadership with each covering at least 80% of the section topics; 100% cumulative coverage on section topics

[‘weight’:1, ‘field_category’:’direct’]

ds_processes

strategy

Data points that capture the levels of data engineering, data analysis, and data science maturity

Obtain and analyze all data points to establish 100% of documented processes and to plan documentation of at least 80% of undocumented processes for each section (e.g., data engineering, data analysis, data science) in order to enhance content management, data management, and data project strategy integration. Need to obtain and code data points from 100% of technology leadership with each covering at least 70% of the section topics; Obtain and analyze 100% of the data from existing data systems associated process topic areas to cover the remaining 30% of section topics; 100% cumulative coverage on section topics

[‘weight’:1, ‘field_category’:’mixed’]

ds_assets

strategy

Data points that capture understanding of how the institution manages data, including accessibility and improvability

Obtain and analyze all data points to establish 100% of data management practices pertaining to data assets to guide data lifecycle management practices and improved integration with data technologies and processes. Need tobtain and code data points from 100% of technology leadership with each covering at least 70% of the section topics; Obtain and analyze 100% of the data from existing data systems associated process topic areas to cover the remaining 30% of section topics; 100% cumulative coverage on section topics

[‘weight’:1, ‘field_category’:’mixed’]

References

Number

Reference

1

Henderson. D., Earley, S., Sebastian-Coleman, L., Sykora, E., Smith, E. (Eds.). (2017). DAMA-DMBOK: Data management body of knowledge (2nd Ed.). Basking Ridge, NJ: Technics Publications.

2

Atwal, H. (2020). Practical dataops: Delivering agile data science at scale. UK: Apress.

3

Ladley, J. (2019). Data governance: How to design, deploy, and sustain an effective data governance program (2nd ed.). San Diego, CA: Academic Press.

4

Berkun, S. (2008). Making things happen: Mastering project management. Sebastopol, CA: O’Reilly Media Inc.

5

Atwal, H. (May 18, 2018). DataOps: Nine steps to transform your data science impact Strata Lon…. Slideshare.Net. Retrieved August 2, 2021, from https://www.slideshare.net/harveysa/dataops-nine-steps-to-transform-your-data-science-impact-strata-london-may-18

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Appendix - Scenario