Do you want to develop a data strategy but are just not sure where to start? Don’t get caught in an endless cycle that leads nowhere. The key is to start with a data strategy assessment so that you can build a data strategy roadmap to success.

In this blog, we outline what a data strategy assessment is, how to approach it in three steps, and how it ultimately leads to a data strategy roadmap. We also provide examples of successful data strategy assessments.

Successful, thriving companies all have one thing in common: they can make sense of their data and strategically use it to transform and drive their business forward. But how do they do it? It’s simple: they have a defined data strategy that acts as the foundation of their data and analytics practices.

A good strategy is more than just data and technology — it is a defined plan that outlines the people, processes, technology, and data that your organization needs to accomplish your data and analytics goals. A good data strategy answers exactly:

  • what you need to use data more effectively;
  • what processes are required to ensure the data is high quality and accessible;
  • what technology will enable the storage, sharing, and analysis of data; and
  • what data is required, where it’s sourced from, and whether it’s of good quality?

Before you can answer any of these questions and develop a successful data strategy, you should start with a data strategy assessment.

Download: Data Strategy Assessment Template

In this blog, we cover:

What is a Data Strategy Assessment?

A data strategy assessment is an in-depth evaluation of various factors within your organization that affect the quality of your analytics and your ability to make data-driven decisions. During an assessment, you review where you are today, map out where you’d like to go, and develop a plan for how to get there.

The goal: at the end of an assessment, you will have a defined data analytics strategy and a customized, step-by-step roadmap that defines how to implement it. The  data strategy roadmap outlines all the steps that need to happen and when, so you can be confident that you are tackling projects in the right order and realizing quick wins right out the gate.

How to Approach a Data Strategy Assessment in 3 Steps

There are three steps to a successful data strategy assessment, including identifying goals and challenges, assessing and capturing the current state, and finally, designing a proposed future state. Armed with this information, you will have what you need to create a roadmap for a successful data strategy.

Three blue colored boxes aligned horizontally represent three steps to a data strategy assessment: identify business goals and challenges, assess and capture current state, design proposed future state. Above each box includes a white icon and number for the step.

The three-step approach to assess your data and analytics allows you to understand business goals, capture current state, and design a future state that enables long-term success.

Step One: Identify Business Goals and Challenges

Interview IT and business stakeholders to get a complete understanding of your business goals, current roadblocks, and specific uses cases where data can support your goals. During these interviews, discuss and identify:

  • What are you trying to accomplish (what are the ‘big rocks’ you are trying to move)? By understanding your business objectives, you can prioritize technical projects and move from the cycle of treading water to making progress toward your goals.
  • What are the current data roadblocks, limitations, and challenges? Documenting this information will allow you to develop a plan to tailor a solution that will address the issues, as well as allocate enough resources to doing so.
  • What will drive data and analytics projects? The goal here is to understand the most valuable business areas that can be improved with data and analytics, and how data can support your business goals within specific use cases.

Step Two: Assess and Capture Current State

Get a better understanding of where you are today by assessing your analytics maturity and examining your current environment.

During this assessment, discuss and identify:

  • What tools, technologies, and systems are currently in use? A complete inventory will provide insight into your current technical landscape to identify how well existing tools are leveraged or where there may be gaps.
  • What data sources exist and how are they currently being used? A deep dive into your data sources and infrastructure will provide visibility into what information is available and how it is currently being analyzed to identify opportunities to better leverage existing data.
  • What are the current frustrations or limitations with your existing data and analytics tools and processes? Gathering feedback from IT, SMEs, and business users will help you uncover any pain points or roadblocks within your current environment.
  • What issues have been identified through data profiling activities? This will help assess data quality and data integration issues.
  • What skills or expertise are currently lacking on your team? An assessment of current skills within your organization will inform training or hiring needs.
  • What barriers exist to adopting advanced techniques (i.e. Generative AI)? An AI -readiness assessment sets expectations.

Summarize all of this into a current state overview that details where insufficiencies exist within your technologies and competencies, makes clear the need for a new solution, and serves as a benchmark against which progress will be measured.

Step Three: Design Proposed Future State

After you have a firm grasp of your goals, challenges, and current environment, you can design the proposed future state — outlining the people, processes, technologies, and data you need to reach your goals. During the design process, you should:

  • Identify KPIs and key metrics
  • Create a prioritized backlog of use cases
  • Design future-state data architecture along with specific technology recommendations, which can span any area within data management and analytics
  • Conduct a thorough tool selection process where — for each layer in the data lifecycle — you evaluate the technologies relevant to your organization against custom criteria based on your specific priorities, environment, and parameters
  • Design dashboard wireframes
  • Build conceptual data models
  • Create data flow diagrams
  • Build a bus matrix
  • Define the roles and responsibilities required to be successful with the recommended future-state architecture and the organizational structure and business processes needed to accomplish goals (including training needed, org charts, job descriptions)
  • Define the data governance approach, processes and committee structure, roles and responsibilities

With this information, you can run each item from the future state documentation through an evaluation process based on expected business impact and technical feasibility. This allows you to plot the proposed future state on a prioritization matrix and group everything into projects to determine a logical sequencing of activities.

This approach allows you to plan projects in the most economical and efficient way (e.g., you might combine actions from different quadrants if they are based around a common data entity); plus, it helps identify the high feasibility/high value projects that should kick off the initiative so that you immediately start getting value from your solution.

Graphic illustrates a Prioritization Matrix used during a Data Strategy Assessment to the identify high feasibility/high value projects that should kick off data and analytics initiatives.

A prioritization matrix helps identify the high feasibility/high value projects that should kick off data and analytics initiatives.

How to Use a Data Strategy Assessment to Create a Roadmap

All of the understandings and output from the first three steps are then used to create a data strategy roadmap. The data strategy roadmap is your North Star: it includes a plan, schedule, and costs for how to implement the recommended future state. It prioritizes efforts and identifies quick wins so you can start seeing value quickly, but also includes a long-term plan to increase your analytics maturity.

The data strategy roadmap includes:

  • The business case for the data strategy and activities in the roadmap
  • An agile schedule, starting with what should happen first, complete with timing for all future phases
  • Estimated time, costs, and effort needed to carry out the solution

As soon as you develop your data strategy roadmap, you can start executing the plan because the deliverables outlined in the data strategy assessment will be actionable and practical.

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Examples of Data Strategy Assessments 

We have conducted data strategy assessments for hundreds of clients through the years — each has found value in different, but meaningful ways. Below are some examples of data strategy assessments that have led to successful data strategy roadmaps that enable a better future with data.

Data Strategy Enables Healthcare Provider to Unify Insights After Mergers & Acquisitions

A large healthcare provider came to us facing a familiar challenge — they were delivering care based on insights from disparate data inherited from multiple acquisitions, lacking a unified view across lines of business. With services now spanning multiple divisions, managing disconnected systems and siloed information posed barriers to optimizing operations and continuously improving patient outcomes.

Through a data strategy assessment, we identified how to modernize the provider’s architecture with Snowflake, Fivetran, and dbt, leading to:

  • Streamlined data integration and maintenance across systems from multiple acquisitions
  • Actionable insights from a single, unified view of patient and workforce data
  • Continuous delivery of excellent care as the organization expands its reach, empowered by the strategic decisions made possible through a cohesive data strategy

Data Strategy Roadmap Enables Cielo to Take Valuable Data Products to Market

Cielo, the world’s largest talent acquisition partner, has goals to continue growth by serving new markets and acquiring additional companies. To do this successfully, Cielo needed to develop a data strategy for a more effective internal use of data, as well as to create a path toward data monetization.

After reviewing Cielo’s current technology stack and business processes, we:

  • Developed a data strategy roadmap that outlined the path to a modern data architecture.
  • Clearly defined roles for their data engineering and data analytics teams.
  • Identified use cases for data monetization.

CareQuest Institute Makes Equitable Change in Oral Health Care with Data-Driven Mission Delivery

CareQuest Institute for Oral Health is a national nonprofit organization that focuses on creating a more accessible, equitable, and integrated oral health system. The organization had recently separated from one of their affiliates and had an urgent need to become technologically independent. At the same time, they wanted to automate and advance their data practices so that they could better serve the community.

After completing a data strategy assessment of CareQuest’s technology and business processes, we:

  • Developed a data strategy roadmap for modern cloud technology, a new architecture stack, a path to migrate and ingest data sources, and consolidation to one analytics platform.
  • Made staffing recommendations and outlined a plan to hire and train employees.

CareQuest now has full independence of their data, IT, and security with a future-proof solution that will grow with them.

Fitness Platform iFit Shapes Up Data for Analysis and Growth

iFit is a leading global provider of interactive connected fitness technology, offering a unified fitness experience across the home, gym, and outdoors. A recent surge in consumer interest created more opportunities and increased data collection, prompting the company to seek outside investment for growth and product expansion. However, to achieve these goals, iFit needed to analyze customer data to better understand their needs and improve their overall experience.

At the end of the data strategy assessment, iFit had:

  • A data strategy roadmap to integrate data from multiple systems into a cloud-based repository to allow for unified data analysis and simple addition of new data sources.
  • Best practices for breaking out of the cycle of endlessly fighting fires and instead achieving more efficient, agile data and analytics processes that allowed for progress toward goals.

This plan provides iFit the basis to be more agile in their market, grow their member base, and improve customer satisfaction.

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Christina Salmi Christina leads the Data Strategy Service Line, helping our customers to think and act strategically about data and analytics.
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