Make better business decisions and increase conversion rates.
There are four critical areas for planning your data strategy:
- Strategy and Culture.
- People and Skills.
- Technology and Tools.
- Methodology and Process.
You can also use these scores as a benchmark to reassess your business throughout the delivery of your strategy, and to quantify progress.
Map your data strategy against your overall business goals using Objectives and Key Results (OKRs) goal-setting framework to get measurable results.
OKRs use a hierarchical structure with objectives and key results at the company, team, and personal levels.
- Company objective: Customers love our product.
- Key results: NPS score increases to 30 by the end of Q3. Customer lifetime value (LTV) increases by 15% by the end of the year.
- Product team objective: Build features our customer want.
- Key result: 65% of customers use new product features at least once a week by the end of Q3.
- Product manager objective: Evaluate new feature ideas**.**
- Key result: Test four new feature ideas and identify which ones customers use most.
Example OKRs goal-setting structure.
Create a goal tree with data requirements to map your data strategy against your overall business goals at the company, team, and personal level.
A goal tree is a document that shows the C-suite how data ties to the business value they want to achieve. Add the data requirements by working through each objective and key result with the relevant team and asking:
- What data do they need to achieve and measure what is written?
- Which key performance indicators will measure their performance?
When writing your data requirements, you can ask yourself:
- What data do you need to inspire ideas and hypotheses?
- What data do you need to validate ideas?
- What data do you need to report on key results?
Work with your tech team to catalog what your current technical capabilities, tech stack, skills, and culture, can achieve from your list of requirements in the goal tree.
Rate the following six factors by importance:
- Data security and governance activities should occur throughout the roadmap. Specific activities to comply with security or governance should be the highest priority.
- Front-load activities that are easiest to implement and tied to the biggest wins. Consider any costs involved weighted against the business value associated with the requirements. Review and mine your existing analytics setup for valuable data.
- Dependencies. Understand what has to happen first for something else to happen later. For example, check the reliability of the data before you start personalization.
- Factor in staff availability and consider other internal projects that might conflict with your roadmap.
- Consider the budget process at your company. For example, ask yourself, Do you have to wait until next April when budgets get renewed?
Use the insight model developed by Gartner, to show how activities in your roadmap move your business toward data maturity.
Gather hindsight, insight, and foresight observations by asking yourself:
- What happened?
- Why did it happen?
- How can we make it happen?
Request that your CEO and managers ask teams to present data-based insights to reinforce the correct use of data. Help others learn from your own work by modelling how to make decisions using data. You can also share insights on your business and customers in weekly company newsletters.
For example, Airbnb created an internal data university with a curriculum tailored to their tool stack and business cases. As a result, they saw weekly active users of internal data science tools rise from 30% to 45%, and 500 employees had taken at least one class. Add training and key hires into your roadmap to ensure people know how to use data.
Plan out internal processes for handling new requests and questions going forward within the strategy you have created.
Requirements will change. For example, someone will need to track additional metrics, or a new social media network will get popular, and suddenly you have a new source of data to factor in.
When a team shops for a new tool, create a checklist of things the tech team needs, such as an open API or other ways to support automated data export and import.