Developing a Data Strategy Roadmap in 7 Steps
If we look at today’s businesses, we can see that data has become an invaluable asset. Even though the value of information is commonly acknowledged, unclosing that value is often difficult due to the large volume of data and the difficulties associated with gathering, organizing, and activating the data. It’s therefore vital for businesses to develop a comprehensive data strategy roadmap.
The capacity to exploit data insights is a significant aspect in defining the leaders and laggards in every industry in our rapidly expanding digital environment. Formulating a data strategy roadmap can assist firms in overcoming these obstacles while also gaining access to the value of their data while preserving resources.
In this article, we’ll discuss the importance of data strategy roadmaps. Moreover, we’ll also go into the steps you need to follow to develop a data strategy roadmap.
Why is Data Strategy Roadmap important?
A data strategy roadmap is a step-by-step plan for achieving a data strategy’s objectives. It depicts the phases and interactions of each step: conceptualize, evaluate, analyze, implement, and govern.
It explains how your company will attain, integrate, secure, store, manage, monitor, analyze, operationalize, and monetize data through various methods, services, procedures, and structures.
It also comprises a set of key performance indicators that can be used to track progress. When put together, an agile data strategy roadmap directs the development of a flexible and scalable data strategy that balances long-term planning with short-term projects.
How to develop a Substantial Data Strategy Road Map?
Every goal you establish should be accompanied by a strategy for achieving it. These plans should be detailed and include information such as who owns the goal, what procedure and technology will be used, how much it will cost, how long it will take, and the expected outcome.
These seven steps will assist you in creating a roadmap for a modernized data infrastructure that can develop quickly and continually to overcome any threat or capitalize on any competitive opportunity.
These plans should also be adaptable, so you may amend them if something doesn’t work out as planned or if circumstances change. As your initiatives proceed, you should analyze them regularly to see what’s working and what’s not.
Aligning the Vision with your Goals
How you put your data should align with the organization’s goals. You should discern based on the vision where your data should fit. At this stage, you should be in a position to answer the question, “Why am I developing this data strategy roadmap?”
Your data strategy roadmap should be driven by the need to improve visibility or monetize data while creating new products. Your data should be organized efficiently. It’s crucial to get clarity to be sure about the problem you are about to solve before proceeding.
Appointing the Right Tools for your Roadmap
At the next stage, you should be able to select the right and adequate tools and a platform established on your vision.
First, you should infer which platform you will be using. Some organizations use a single platform, while some benefit from multiple data warehouses.
Choosing tools is also crucial as you must be careful not to get into a situation where you must use multiple one-off tools.
Making a Catalog of Existing Data Assets
Estimate the value and complexity of the data included in your sources and give them a score. Then another thing you should do is make a list of current master data management systems.
Prepare a list of prevailing information logistic configurations, such as stores, data lakes, vaults, or warehouses. Make a list of your top three data strengths and weaknesses, along with a brief description.
Discuss your discovery with your team and convert the data to Visio to strengthen your mental model while creating something more permanent and sharable.
Adopting Database Management
Having a database framework as a fundamental element is crucial while developing a data strategy roadmap.
Your approach should have the initial setup powered by business intelligence and configuration.
Before setting the roadmap, you should gather knowledge of which datasets need certification or who owns the process.
Illustrating Roles and Responsibilities
After selecting your business objectives and tools, you should look for the proficiency required to execute your strategy.
You should know if your requirements include a centralized data analytics team or a unit. If you cannot hire a full-time data team, you should fill the gaps while figuring out how to deal with the requirements by training internal resources or hiring specialists.
Generating a Training Plan
Once you’ve defined roles and responsibilities, you’ll know the skillsets needed to support your data strategy.
A training strategy can assist you in developing internal capacities and competencies to fill in the gaps with existing resources.
Envisioning your Roadmap
Take the above components and arrange them on a chart to visually represent your data approach.
While your data strategy roadmap should include a vision for the next two to three years, you should also have targets for each year.
The first-year plan should include the most information, such as prioritized initiatives, business domains, risks and mitigation techniques, and expected outcomes.
Data Strategy Roadmap: Next Steps
Given how crucial data has become and will continue to be to businesses, developing a practical data strategy roadmap is now more critical than ever. The data strategy roadmap is the outline where you can see the intentions for achieving your ultimate long-term and small short-term goals.
Including the elements above in your data strategy roadmap will enable you to achieve modernization, achievement, and better business results. You risk losing sight of your objectives along the way if you don’t create a well-designed roadmap for how you’ll use data to help you achieve your goals.
It is unlikely you will identify a unified approach to data and its use across your company. With data-centric capacities becoming a new essential, people who haven’t yet modernized their data infrastructure risk endangering their bottom line and industry stance.