A data management roadmap is a strategic plan that outlines the steps and activities necessary to effectively manage data within an organization. It provides a structured approach to ensure data is collected, stored, processed, and used in a consistent, reliable, and secure manner. Our seven steps provide a proven approach to delivering a business-first data management roadmap and bridge the gap between your data strategy and data management practices.
The first step in defin
ing a data strategy is to understand your business strategy. Your business strategy outlines your goals and objectives, your target audience, your value proposition, and your competitive landscape.
Identify key business goals and objectives.
Goals are the outcomes you intend to achieve, whereas objectives are the specific actions and measurable steps that you need to take to achieve a goal.
For example, a business goal may be:
- To Increase the sale pipeline
Objectives to support that may be:
- Increase the conversion rate of leads
- Increase outbound call quantity per sales rep
Understanding your business strategy will help you identify the data you need to achieve your goals and make better decisions.
The details of a strategy will differ from business to business, however, the goals of most data strategies should be:
- To ensure that IT projects are aligned with business needs and focused on business priorities through data excellence and information management
- To Increase operational efficiency by identifying and addressing key business process failures that impact data integrity
- To reduce data-related risk through pragmatic information risk management.
The Information Value Management methodology presents a systemic approach to achieving this.
Because the big strategic data priorities can take some time to implement and deliver value, your data management roadmap should also identify 1-3 data quick wins. These are fast, relatively inexpensive ways for you to add value and demonstrate return on investment from data.
For example, you might do some customer churn analysis, to help prevent or reduce customer turnover, or do some data cleansing if you know that data quality is a problem
Once you understand your business strategy, you need to identify what data capabilities are required to support the business objectives.
For each of the data priorities/use cases that you've identified, you need to work through the following considerations: Think about what data you need to achieve your goals and where that data will come from.
This includes:
- Do you need structured or unstructured data or a combination of the two?
- Can you achieve your goal with internal data alone, or do you need to supplement your company data with external data?
- Do you already have or can you quickly access the data you need?
If not, you need to set up a way to collect the appropriate data. What data collection method will you use?
For example, for the goal "Increase conversion rate" we may:
- Identify key metrics that must be delivered in the form of a new BI capability
- Identify the need to enhance lead data quality - validation of email addresses and/or telephone numbers from an external source
- Identify the need to enhance customer segmentation and predictive analytics
- etc
Linking data management capabilities to business objectives allows us to prioritise investment where it will bring the biggest return
Data brings great rewards, but it can also be a serious liability if you don't pay proper attention to data governance.
This step encourages you to think about data quality, data security, privacy and ownership issues, transparency, and ethical data use.
Key considerations include:
- Who is responsible for ensuring the data is accurate, complete, and up to date?
- If you're accessing someone else's data, could you lose access to it?
- What permissions do you need to be able to gather and use the data?
- How can you minimize data where possible?
Once you have identified your data needs, you need to define your data architecture. Having decided how you want to use data, and what data you'll need, the next step is to identify the technology and infrastructure implications of those decisions.
This means looking at your hardware and software needs for processing (analyzing) data to extract insights, which may include machine learning or deep learning technology, and communicating insights from data, including reporting and data visualization.
You should also consider how your data architecture will support your business strategy and how it will scale as your data needs grow.
A gap assessment and roadmap that identifies and prioritises missing enterprise information management capabilities will help to ensure an actionable strategy.
Often, the main stumbling block for companies wanting to get more out of data is the lack of data skills and knowledge.
Therefore, this is a critical part of your data strategy. Ask yourself: If not, can you train in-house staff or do you need to hire new talent? If you're looking at external skills, will you partner with a data provider or is there potential to acquire a company?
Leadership awareness is another important part of this. Your leadership team needs to understand why data is important and how data can help the business achieve its objectives. Ideally, this culture of data will filter throughout the whole company, so that everyone at every level is aware of the power of data.
Making a plan is one thing; delivering it is another. So this second last step is about making sure your data strategy becomes a reality.
This includes: How will you implement your plan? What are the key activities that need to happen next? Establish conservative project boundaries, make estimates, finish defining metrics, and produce a roadmap.
We typically recommend the following approach:
- Start immediately or within three months: Quick wins and firefighting. Prioritise those urgent issues that will take away attention from the broader plan, or that can deliver value quickly whilst delivering the vision
- Start within 3 to 6 months: Identify those missing capabilities that support more than one business objective, or that are otherwise important, and begin implementation. An implementation may be limited to doing relevant research and building a business case and roadmap for the following cycle, or it may involve a technology deployment, or organisational change, or both.
- Park for next cycle: Some gaps may be identified for capabilities that either may not be priorities or for which there is no capacity. These would typically be parked for review in the next cycle of the strategy. After a year business priorities may have shifted and some of these may now be priorities
Remember that a data management roadmap should be flexible and adaptable to evolving technologies, business needs, and regulatory requirements. Regularly review and update the roadmap to ensure it remains relevant and aligned with the organization's strategic objectives.
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