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Analytics Strategy Playbook Template

This analytics playbook template was built for enterprise analytics and business intelligence leaders and teams. This template is designed for cross industry application and can be customized to fit your specific organization.

Author –

Ryan Goodman⁠

DataToolsPro.com⁠

⁠

How to Use this Template

Inventory and Rank: A top to bottom inventory of your organization’s strategy components. You can use the included assessment template to rank the “Level of Existence” for each component

Document Playbook - Use the Playbook Wiki to document each component of your analytics strategy. Link to other documents, portals, applications allowing the wiki to serve as a hub.

Survey and Re-Assess - On a quarterly or bi-annual basis, survey your business information consumers and re-assess both existence and execution of your analytics strategy.

1. Objectives

1.1 Analytics Purpose and Goals

To succeed, we will define our analytics playbook based on "people, data, process, technology". Here is an example how I define purpose and goals:

PEOPLE

Advance and level-set skills for data influenced decision making

Facilitate accountability across lines of business with reporting and analytics

Perpetuate hard work / great attitude (reaching our goal is going to be tough work!)

DATA

Data that is “just in time”, and readily available

Information assets that are discoverable and deployed to the right information consumers

PROCESS

Predictable, fast decision support

Set actionable goals and KPIs

Prioritize data quality and completeness

Standardization for scoping and knowledge retention.

TECHNOLOGY

Drive best practices for information management on top of our proprietary platform

Minimize existing technical debt while building a technology platform ready to scale

Apply practical and impactful use of AI in the form of traditional machine learning

Data and analytics are driving innovation and supporting AI augmentation

1.2 Current Analytics Goals and History

What are the top goals for advancing analytics initiatives?

1.3 Analytics Playbook Objectives

Without a strategy in place, it is extremely difficult to scale your analytics effort. How will you measure success? How do you recognize the analytics is having a positive impact on your business. These are the tough questions to answer in this section.

1.4 Inventory Analytics Content

Your inventory should point to resources that support your analytics objective. A metric / KPI dictionary, portals where analytics are delivered to the business. Data warehouse docs, knowledge base, project management, source control, etc.

1.5 Team and Roles

Who owns the playbook, who are business leaders, regional teams and leads, key strategic vendors

1.6 Business Anecdotes / Personas

We use anecdotes to help explain the perspective and needs for those individuals who use data to make decisions. Anecdotes highlight previous experiences from individuals that should point to previous success and failures, to help guide future developments.

2. Analytics and AI Drivers

2.1 Summary of Business Challenges

This should be comprehensive and can use our business initiative tracker, categorized by line of business, operating unit, and even leveling within the organization (executive to front-line).

2.1.1 People Challenges

2.1.2 Data Challenges

2.1.3 Process Challenges

2.1.4 Technology Challenges

2.2 Envisioned To-Be State

Define what success looks like per line of business. Some common goals like consistency in measurement, improved change management, confidence in data, accountability, availability of data.

2.2.1 People

2.1.2 Data

2.1.3 Process

2.2.4 Technology

2.2.5 AI

How is your organization evolving to support AI; inclusive of machine learning, speech/image recognition and generative AI.

2.2.2 Analytics Process/ Change Management

Key guidelines for prioritizing, scoping, and delivering analytics projects.

2.3 Priorities and Alignment

What are the top strategic organizational initiatives and goals for the year, quarter that analytics will support.

2.3.1 Risks and Challenges?

What are key operational challenges that could impact your success to use data and analytics to support strategic organizational initiatives?

2.4 Analytics Projects and Initiatives

What are key projects and initiatives to support your strategic organizational initiatives?

3. Business Case

3.1 Analytics Value Statement

This section will highlight the qualitative value that your organization receives from analytics. This should help drive requests for investment in people and technology.

3.2 Business Feedback Loop

Gathering quantitative and qualitative feedback is critical to demonstrate you’re meeting the organizational objectives. I recommend a quarterly or semi-annual survey of all business data and analytics consumers to gather insights and ensure data and analytics is working to support business objectives.

4. Information Structure & Technology

4.1 Information Categories

For the purposes of the playbook, an Information Category is a broad collection of related business questions that are aligned to data sources and structures. The purpose of defining information categories is to create focus on decision support across lines of business.

4.1.1 Primary Taxonomies

Taxonomies are specific categorization and segmentation of information in your business.

4.1.2 Use Cases

Use cases explain how data is transformed into information and how that information is consumed. For example, operational reporting, cohort analysis, historical trending are flavors of descriptive analytics. Similarly for predictive analytics you have a wide range of use cases. In addition to explaining use cases, sharing where those techniques are applied helps justify further investment.

4.2 Architecture and Standards

Provide guidance to the analytics architecture. This section should be kept simple to explain high level architecture design and patterns. Sub-sections can explain in greater detail how your analytics architecture is designed and the various components as shown below

4.2.1 Business Applications

4.2.2 Data Staging and Data Lake

4.2.3 Data Warehouse

4.2.4 Data Pipelines and ETL

4.2.5 Artificial Intelligence / ML

4.2.6 Development Operations

4.3 Analytics and AI Applications

Analytics apps should highlight analytics platforms, portals, and consumption modes. Similar to architecture this section will include sub-sections to fully explain your analytics apps current state.

Organization & Implementation

5.1 Governance Structure

Governance is a critical component to any analytics strategy and often blends traditional IT governance processes with added scope for analytics. In the analytics strategy playbook. There are a number of sub-sections required to fully explain your governance process.

IMAGE SOURCE: Gartner

5.1.1 Data Governance

5.1.2 Master Data Management

5.1.3 Metadata Management

5.1.4 Data Security / Trust Models

5.2 Analytics Center of Excellence

Who is responsible for harmonizing, managing, and distributing knowledge. Who and how are you pushing to level up your organizational analytics abilities.

5.3 Roadmap and Milestones

Define a quarterly or annual roadmap for how you will improve and advance your abilities to transform data into knowledge? What initiatives and investments are planned and what is the over arching goals.

5.4 Measurements – KPIs

How and where do you store a KPIs and metrics?

5.5 Education / Training

What are you doing to educate and train analytics specialists, IT, and business information workers, and information consumers? Explain the programs, tools, and why they are important.