In our first blog post, we took you through the levels of data maturity. We equipped you with the know-how to take your business along the data journey and showed you how closely integrated data maturity is with building data culture.
Now, we’re going to dive deeper into the final destination of your data maturity journey: becoming a data-transformed company.
In this post, we’ll guide you through the main steps and milestones of building maturity including how to establish a strong data foundation, integrate your processes, and embed good data habits.
Phase 1: Establish a data foundation
Focus: Technology and data management
1. Scope your data analytics needs
The first step is to identify the analytics needs of each team. These could include the metrics and KPIs they need to track, customer journeys they need to cover, or areas of the platform they are working on.
It's also important to identify the business operations and outcomes they are trying to improve–you can then establish baseline metrics across all products and use cases. Use this lean analytics plan framework to quickly map these.
2. Scope what your tech stack needs
The second step is to determine your tech stack needs. This includes:
Web and mobile dashboards and reports
Integrations (such as business intelligence, customer journey engagement, and customer success management)
3. Identify and enable data governance
Your data governors will be responsible for implementing an initial data governance plan and updating it as needed over time.
For more information on how to do this, read this recommended article.
4. Build out your change management plans
To facilitate the adoption of your analytics system, it's important to put in place basic change management guidelines for the data governance team to enforce and optimize over time. This will ensure that as your business grows, your data can too without any problems.
For more information on how to drive this process in your organization, check out these best practices.
Phase 2: Integrate your process
Focus: Building confidence and capacity
1. Educate your team on your growth model
Train your team members to understand how the customer journey is connected to your desired business outcomes. For example, which flows and events drive conversion? What are your KPIs when it comes to engagement and retention?
Once your team has mastered your growth model, they’ll be empowered to ask the right questions.
You can do this through training courses, 1:1 working sessions, during office hours, and ongoing enablement.
2. Enforce best practices through templates and briefs
Data trust: By governing event and property definitions, you can ensure your teams and stakeholders are all using the right metrics.
Communication: Build dashboard templates for easy comparability across different features, which can then be used by different teams on a practitioner and executive level.
Roadmap and GTM planning: Integrate the use of behavioral data in product briefs and go-to-market campaigns. Enforce the usage of baseline measurement and desired lift before greenlighting any plans.
3. Review post-launch results and present learnings
Make it a habit to gather all stakeholders post-launch to review successes, failures, and learnings.
Feel free to schedule the meeting date in advance of your launch and specify the date in the original brief. Make sure to include in the calendar invite the different data points and learnings each stakeholder is expected to present.
One way to complete a post-mortem review is by creating an after-action report. Learn more about after-action reports here.
Phase 3: Embed good habits
Focus: Build a data culture
1. Add data analytics to your onboarding program
Don’t wait to integrate new employees into your data-driven culture. Include an overview of your data processes, tools, and expectations during onboarding.
Show new employees how they can weave the tools they have at their disposal into their daily, weekly, monthly, and quarterly routines. As new employees continue to ramp up, make sure they understand your business model and growth levers.
2. Schedule training refreshers
Prime your organization for wider data adoption by continuously updating your training and advancing the level of self-serve analysis.
3. Regularly share learnings and celebrate wins
Share and cross-pollinate new learnings, frameworks, and methodologies. You can do this by emphasizing wins where data-driven decisions saved the day. Or sharing stories where data unlocked additional growth.
It is important to talk about how others can use similar data-driven methodologies in their own routines.
Collecting behavioral data is one thing, but actually enabling your company to be fully data-transformed is quite another.
Being a data-transformed company means you’re operating at the height of data maturity: it’s embedded in how you approach, discuss, and present work. It drives how you onboard new people, how you strategize what to build and why, how you share your wins and losses, and how you ultimately measure success.