How to Build a Data Strategy Roadmap

Once you have a data strategy in place, it’s crucial to develop a roadmap and plan for implementation. This will ensure that everyone involved understands the steps necessary to make your data-driven enterprise a reality. It will also help keep you on track and accountable for reaching your goals. 

Getting Started with your Data Strategy Roadmap 

A roadmap is a step-by-step guide to what needs to be done to achieve your desired business goals. A good roadmap should include: 

  • Timelines – When are you going to start implementing each phase or solution? What milestones do you need to hit first? 
  • Goals – What do you want to accomplish by the end of this project? Are these realistic, and what value will be delivered in each implementation phase? 
  • Objectives – How will you know if you’ve accomplished your goals? These may include “increase revenue” or “reduce costs.” 
  • Resources required for implementation:  
    • People: Who will be responsible for each step of the process? 
    • Financial: What is the budget for each phase of the implementation? 
    • Technology: What technology is aligned to the future state reference architecture? 
  • Deliverables – What steps must be completed before moving forward with your next goal?  

Developing a data strategy roadmap can ensure that your data strategy is achievable and provides tangible results. 

Why Do You Need a Data Strategy Roadmap? 

If you don’t have a clear picture of where you’re headed, then it’s almost impossible to get to your final destination. Developing a data strategy helps break down the big picture into manageable chunks. With a roadmap in hand, you’ll always know how far along you are and whether or not you’re on track. 

When planning a data strategy, it’s essential to think through all the different aspects of the implementation. If you try the monolithic approach, you will run out of steam before you get started. Before you begin any data-related projects, it’s helpful to create a detailed plan that outlines everything from the project’s scope to its cost.   

By mapping out the entire process, you'll avoid wasting time, money, and resources. You can also use this information to gauge the feasibility of new ideas and figure out which ones to pursue first. 

A data strategy roadmap will allow you to communicate what you expect to achieve to stakeholders and management. It will also provide an easy reference point for each future data initiative.  

You can use roadmaps to visualize your strategy and keep the entire organization focused on your goals. Implementing a modern data & analytics platform will transform your organization's data-driven decision process and can help turn big data from a buzzword into a powerful business asset. A strong understanding of your organization's goals and the resources required to reach them are just two critical components to getting started. 

Some other Data Strategy Roadmap components include: 

  1. The Business Case – Define the business case for a data strategy. 
  2. Data Governance Plan – Develop an effective governance structure for managing the data assets. 
  3. Data Management Plan – Identify the resources needed to manage the data. 
  4. Data Quality Assurance Plan – Define best practices to keep data standards high for accurate reporting.   
  5. Data Analytics Plan – Establish a process for analyzing data to support decision-making. 
Business Case 

Defining the business case for a modern data platform is one of the essential parts of a data strategy roadmap. Your business case should include the following elements: 

  • What problem does a modern data platform solve? 
  • Will there be any additional costs associated with the data platform? 
  • What is the total cost of ownership (TCO)? 
  • What is the return on investment (ROI) expected? 
  • What is the timeframe for deployment? 
  • Do we have enough budget to complete the project? 
Data Governance Plan  

A solid data governance plan is a foundation for a robust data strategy. You'll want to establish a framework for managing the data assets within your organization. In addition, you'll need to define roles and responsibilities, identify who owns what data, determine where the data resides, and create policies and procedures for accessing and using the data. 

A good data governance plan should include the following elements: 

  • Roles and responsibilities – Describe who will access the data, how they will access it, and who will oversee their activities. 
  • Ownership of data assets – Determine who owns each piece of data and how they will be used. 
  • Location of data – Specify where the data will reside and describe its accessibility. 
  • Policies and procedures – Create policies and procedures for accessing the data. 
  • Access control – Designate which users will have access to the data based on their role. 
Data Management Plan 

Once you've defined the scope of your data strategy and determined the types of data you will use, you'll need to decide how you will manage the data. The data management plan describes the tools and processes that will be used to manage the data. Some key considerations include the following. 

  • Resources needed to manage data – List the resources required to manage the data. These may include hardware, software, people, and training. 
  • Types of data – Identify the different types of data you will be storing. For example, structured data such as financial records and unstructured data like emails and text documents. 
  • Storage options – Choose the storage option that best fits your needs. Cloud computing options include private, public, hybrid, and community clouds. 
  • Security measures – Define the level of security needed to protect the data. 
Data Quality Assurance Plan 

The data quality assurance plan defines the processes and systems used to ensure that the data meets your standards. A data quality assurance plan includes the following components.  

  • Identification of standards – Describe the standards that must be met before the data can be released. 
  • Definition of metrics – Define the metrics that are used to measure the success of the data quality program. 
  • Methodology for testing data – Outline the process for testing data. 
  • Reporting of results – Report the results of the tests. 
  • Monitoring processes – Monitor the progress of the data quality program and report back to stakeholders. 
Data Analytics Plan 

The data analytics plan details the analytical techniques used to analyze the data. An analytics plan includes the following elements. 

  • Process and approach – Your analytical process from prioritization, and guided navigation, to enabling self-serve analytics. 
  • Data preparation – Describe the preparation steps performed before analyzing the data. 
  • Use cases – Define the scenarios that will drive the analytical techniques. 
  • Business rules and data models – Describe the business rules and data models applied to the data. 
  • Presentation – Describe the analytics presentation and how it will be used both internally and externally. 

In Summary 

A data strategy is a plan that outlines how enterprises will use data to achieve specific business objectives. It provides a clear sense of direction and sets expectations. Creating a data strategy roadmap is a valuable tool to help execute on the strategy’s implementation.  

Having these expectations laid out in a roadmap brings the entire organization along for the journey. This is important for many reasons, but mostly it’s so data consumers can fully grasp the cultural transformation needed to be a data-informed organization.    

This article provides practical guidance on how to get started with a data strategy roadmap, and as you can see, there are many aspects to consider when developing it. At Data Ideology, we can help you put together a pragmatic data strategy roadmap that allows for a smooth implementation of your data strategy and avoids bumps along the way.

Written by Mike Sargo


Co-Founder & Chief Data and Analytics Officer at Data Ideology


Mike Sargo is Chief Data and Analytics Officer and Co-Founder of Data Ideology with over 18 years of experience leading, architecting, implementing, and delivering enterprise analytics, business intelligence, and enterprise data management solutions.

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