When Shortcuts Slow You Down

Understandably, most business owners want to move fast. They want hockey-stick growth and better reporting for data-driven decisions yesterday.

But here’s the paradox:

The faster you try to move without a solid foundation, the more time (and money) you waste fixing problems later.

I see this all the time with businesses rushing to scale. They expect quick delivery of dashboards and immediate insights from their data.

But they haven’t laid the groundwork.

Their goals aren’t defined, their processes aren’t consistent, and their operations are being held together by a patchwork of quick fixes that’s producing messy and incomplete data.

If you try analyzing your data or building dashboards on top of shaky infrastructure like this, you’re going to have a bad time.

You can’t generate meaningful insights from bad data.

And you’re inadvertently creating bottlenecks because you’ll have to go back and fix what you built later.

Think of it like building a house. If you lay the foundation improperly, it might look fine for a while.

But sooner or later, cracks start forming and the whole structure becomes unstable.

Business and analytics work the same way. What feels like a quick win ends up turning into a long-term nightmare.

Slow Down to Speed Up

At JS Analytics, the first thing we do with every client is a data audit. We perform a comprehensive review of all your data as well as the processes that are producing that data.

These are the top four issues we consistently find driving shaky analytics foundations:

1. Lack of common identifiers for entities between systems. Lead, job, and payment data in three separate tools? You need some way to tie customer data from one tool to their data in the others.

2. Multiple sources of truth for the same data. Every data point should have a single source of truth. When you do need to store the same data in multiple places, ensure you have reconciliation processes in place to prevent discrepancies.

3. Lack of, or failure to adhere to, standardized processes. Sales team documenting prospect objections differently in your CRM? That’ll make it awfully hard to evaluate that data later.

4. Incomplete data collection or failing to collect key data points. No timestamp for job start/end means you can’t track average service time. Collecting only a customer’s most recent job rather than all historical jobs makes it hard to measure repeat customer rates.

Yes, building a strong foundation takes time. But it’s time well spent. Because once your foundation is solid, everything else moves faster – without the constant backtracking and patchwork fixes.

So next time you’re tempted to take a shortcut, ask yourself: Is this saving me time now and in the future… or will it create bigger problems for me later?

Thanks for reading!
Josh

P.S. To learn more about JS Analytics and what we do, check out our website here.

If you’re interested in becoming a client of JS Analytics, feel free to grab time on my calendar here.