Building Your Minimum Viable Data
The concept of minimum viable data is relatively new, while most entrepreneurs are more familiar with the term “minimum viable product”, it is important to know how to work towards having minimum viable data as a startup. Building minimum viable data is the procedure of intentionally designing processes that collect and store as little customer or user data as possible to be successful and grow, in simpler words, building minimum viable data is the process of gathering the smallest and most important user data to cause the positive growth of your startup.
The idea behind minimum viable data has to do with the manner at which startups look at data as something they need to gather as much as they can, for possible future use. The concept of minimum viable data suggests that it is healthier for a startup to have lesser data, because having less data would boost growth and support the long-term health of your startup, and also reduce long term risks. For starters, having too much data leads to a couple of hidden costs such as storage costs, unknown risks, maintenance and technical debt, the best reason to have as little data as possible is because it’s the right thing to do, even from a user’s point of view, a user would rather you having as little information about them as possible.
As an entrepreneur, building your startup with minimum viable data makes the job of scaling or upgrading easier for you than having so much data all over the place, it saves time and energy because even external personnel will understand your business much easier when you use minimum viable data. Below are 6 basic steps to building your minimum viable data:
1. Decide what data you need and why
Answering the why question helps you filter the data you require to the barest minimum, you find out that a lot of data you ask for isn’t as necessary as you think.
2.Test to make sure it is enough data
It’s important to be sure that you have as much data as you need, too little or too much can be a problem. You might have to experiment with the data available to you and monitor the results to determine if you need more or fewer data.
3. Do a data audit to understand where your data lives (always ask partners and service providers)
Such service providers might include mail engines such as Mailchimp, where you store your email lists.
4. Create a data policy with playbooks and assigned roles. Make sure this includes a contingency plan for data breaches or crisis.
5. Design all your systems to comply with your data policy.
6.Re-evaluate twice to tweak and make improvements
Step 6 shows that having minimum viable data is not an end state, it is something you keep working towards as a startup and keeps your data in check, making sure you are using the best data methods for your startup.
