Author: Steve Lock
Building a data analytics strategy from scratch can be daunting. However, if you follow the steps outlined in this article, you’ll be able to break things down into five key areas that will make your life much easier.
1. Business Objectives
Firstly, any data analytics strategy must align closely with business goals, otherwise your program will be dead in the water before it’s even started!
This should include aligning on KPIs as an essential first step. Ideal KPIs should include 3-5 primary metrics that are typically lag indicators and 5-8 secondary/supporting metrics that are often lead indicators (although there is no one-size-fits-all formula).
Common metrics include revenue, pipeline, new customer acquisition, churn rate, qualified leads, customer feedback, conversion rates, costs and eCommerce metrics, if relevant. These are just scratching the surface. The key is that all KPIs should be highly tailored to the business, with a strategy that’s laser-focused on measuring and improving the numbers that matter.
Once you’re comfortable with the main metrics, you should also consider key dimensions that are needed i.e. how you’d want to ‘slice’ the data.
This would typically include date, fiscal quarter, acquisition channel, campaign, geography and any other key views you want to see.
We’re big fans of prototyping and recommend that as early as possible, you start to document and plan the reports and dashboards likely to be required. Even if they’re just initial ideas, it’s typically the fastest and most productive use of time at early stages of data projects.
2. Auditing
This step is one of the most important on the list. It’s also a huge area with lots of complexity, so I could easily write ten posts on this topic. Here’s an overview of priority audit areas to cover and research further:
- Maturity assessment
- Data access and exploration/EDA
- Data quality
- People
- Processes
- Technology stack e.g. databases and data visualization tools
- Current capabilities
You need to identify your current state as clearly as possible, as it’s the only way to design a strategy for how to move forward and prioritize everything you need to do to hit your goals.
3. Define Goals
The next step should include defining the goals you’re aiming for. Not all businesses should be striving for the same data capabilities as Google or Meta. For many businesses, achieving high-quality data, automated reporting and dashboarding capabilities for all their key data sources is a fantastic start.
Below is our reproduction of a chart created by Analytics Activation on Medium. This helpful visual maps analytics capabilities at different stages of maturity.
As an example, you may decide that the Advanced Stage is a great fit for your business, but investment in AI and decision-making systems are beyond your budget.
4. Roadmap
Once you’ve defined key KPIs, ensured alignment with the business, assessed your current state, and defined a goal for the program, the next key step will be to document and communicate a clear roadmap.
For best results, focus on key deliverables such as reports and dashboards (or data applications for more advanced projects), then work backwards on all the key milestones that need to be met.
Training and enablement should also be included, as user adoption is critical for success. Often you’re better off with a more basic deliverable that is well adopted than building something amazing that nobody uses!
5. Operationalize
All of the above areas can be aggressively delivered as part of a series of projects, however you also need to think about how your data analytics program will work day-to-day. Good data projects are not ‘one and done’ and require careful thought to ensure you’re seeing continuous improvement.
This should include standing meetings, gathering feedback from end users, ensuring there is a clear process to submit requests and report bugs with SLAs and regular reviews.
Depending on the technology stack you’ve implemented you will ideally be able to monitor and analyze usage data on the data products you’ve delivered.
PS. Ready to dig deeper into crafting your own data analytics strategy? Choosing the right analytics tools is key. Check out Steve’s top tips on How to Pick the Right Tools for Data Visualization and Reporting.
