You may have undergone a data reset before without ever realizing it. The aftermath can be tricky – and scary – if you’re unprepared for what to expect, what to do, and what to avoid.

A data reset happens when:

• You’ve overhauled your data collection system
• You’ve cleared out inaccurately collected data
• You’ve fixed broken processes
• You’ve redefined your KPIs

Whatever the reason, all of your data on key insights and trends – the data you use to inform every major business decision – is back to square one.

Why is this such a critical time for your business?

Because until you have enough data volume, your analytics will be an estimated guess.

Don’t Make Key Decisions During This Time

Once numbers start pouring in, resist the temptation to change course.

It’s crucial to understand that until you have sufficient data volume, the insights gained may be inconclusive and merely noise.

Remember the reason you needed the data reset in the first place: To start collecting more accurate data so that you can make better business decisions.

So take a breath, be patient.

What to Do Instead

Start by measuring key performance indicators (KPIs) that can be tracked almost immediately. Activity volumes and all point-in-time data will be enough to manage day-to-day operations effectively.

For the key insights and trends, you’ll need to give things a bit more time.

Typically, two sales cycles is enough for most metrics.

This is the data you’ll use to diagnose problems and get ahead of issues while they are in their beginning stages.

But until that data is sufficient, you’ll have to follow established best practices to guide your decision making.

The Importance of Proactive Data Processes

This data-limbo is one of the main reasons we advocate for setting proper data practices sooner than later.

Every month that goes by without:

• Clearing out zombie pipeline
• Optimizing your tech stack
• Defining your metrics

…is another month of wasted data.

Investing the time upfront saves you from significant effort and unnecessary setbacks in the long run.

In Conclusion

Patience and adherence to best practices when analyzing data contributes to better decision making – and better decision making affects everything from sales to customer retention.

TL;DR

• Until you have enough volume (two sales cycles) your data is going to be inconclusive
• Avoid the temptation to change tack until you have enough information.
• Measure what you can while your dataset bakes
  • Activity Volume and point-in-time data
  • Key insights and trends need more time
• Follow best practices in the meantime