Eight Buckets, Four Rs: Why Most of Your Data is Useless (And What To Do About It)

Every company I talk to says the same thing: we have a lot of data and don’t know where to start turning it into something useful. It’s business chaos!

They’re right.

But here’s the truth: most of that data isn’t doing you a bit of good.

If it doesn’t pass what I call the Four Rs test, it’s just noise. And noise kills ROI.

So, how do you make sense of it all? You start by putting it into the right buckets - and then you stress-test it against the Four Rs.

This gives you a baseline to build your business around that will outlast your competitors.

The Eight Buckets of Data

I’ve found that every company - no matter the size or industry - has the same eight categories of data. Once you sort things into these buckets, the chaos starts to clear.

1. Customer. This is the heart of your business: account details, firmographics, product usage data. For a SaaS company, customer data might mean login frequency and feature adoption. For a manufacturer, it might mean which plants, regions, or product lines a customer buys from. If you can’t quickly answer “Who is our customer and how do they use us?” you’re already behind.

   
   

2. Sales. Opportunities, pipeline, contacts, cross-sell/upsell activity. This is where a lot of companies over-collect. I’ve seen Salesforce pages with 150 fields on an opportunity record - and sales reps don’t use 80% of them. That’s not sales data; that’s friction.

   
   

3. Quote. I break this out separately because of the complexity. In industries like manufacturing, a single quote can touch thousands of SKUs. In software, it might pull together license counts, modules, and service terms. Either way, quotes carry nuance that can drive pricing strategy, margin analysis, and customer satisfaction - so they deserve their own attention.

   
   

4. Purchase. This is the “what they actually bought” category. Orders, line items, bundles. The most telling metric here isn’t what was purchased - it’s the gap between quotes and actual purchases. If your quotes always look one way, but purchases tell a different story, that’s insight waiting to happen.

   
   

5. Invoice. Accounts payable and receivable. Payment history. Outstanding balances. It’s easy to overlook this as “just finance data,” but it can reveal buying patterns, credit risks, and even which customers are most likely to churn.

   
   

6. Service. Tickets, support chats, escalation logs, self-service centers, help documentation usage. If half your tickets are about the same three issues, that’s a product or training problem, not just a service workload.

   
   

7. Marketing. Ads, campaigns, website analytics, event responses, nurture sequences. The challenge here is fragmentation: your Google Ads data is in one silo, your CRM campaigns in another, and your web analytics in a third. Bucket them together and you start to see the full go-to-market picture.

   
   

8. Communications. Emails, Slack, Teams, SharePoint, Google Drive. This is the wild west of unstructured data. It’s messy, but it often holds gold: customer complaints buried in an email thread, or tribal knowledge stored in a shared drive that never makes it into a system of record.

Every business has these eight buckets. You may call them something slightly different, but they’re there and there’s eight.

Once you map your data into them, you can start asking better questions. And now we transition to the real test of what’s truly helpful.

Related: I run through this and much more in this webinar!

The Four Rs Test

Now, not all data in those buckets is created equal.

To figure out what’s valuable and what’s just clutter, run it through the Four Rs:

Relevant

Does this data answer a real business question?

• Good example: churn data that shows which customer segments are at highest risk.

• Bad example: a custom field in Salesforce nobody can explain but “it was there when I got here.”

Reliable

Is it complete, consistent, and trusted?

• Good: invoice data that reconciles perfectly with your ERP.

• Bad: pipeline reports where the close date fields are always wrong, so leadership ignores the dashboard and asks for manual updates.

If executives don’t trust the data, it doesn’t matter how much you have - it’s worthless!

Revealing

Does it uncover patterns or trends you can act on?

• Good: support chat analysis showing 50% of inquiries are already answered in your help docs - a signal to add automation or better deflection.

• Bad: a weekly report that shows “number of logins” without any context. Trend lines with no actionable insight are just decoration.

Reusable

Can you use this data again and again in the next 6-12 months and even 3-5 years?

• Good: product usage telemetry that feeds customer health scoring, renewal forecasting, and upsell targeting.

• Bad: a one-off campaign list that’s never cleaned, never refreshed, and clogs your CRM.

The reusable step is one that catches a lot of people off-guard because there’s a natural assumption that any data passing through the first three Rs will qualify as reusable. You’d be surprised how much doesn’t!

Because if it’s useful but only once, it might be noise a year from now. But if re-thought, it could also be quite valuable moving forward.

Any “Good” Data Must Pass 3+ Rs

To be honest, data that passes all four Rs is rare. In one client workshop I did, we looked at a small sample of data - just an initial pass of Account, Contact, and Opportunity data.

Only 1/39 fields met all four Rs. This is normal.

However, 16/39 passed at least three. That’s a solid foundation to work from, but it also highlights how 59% of that data was simply noise.

To be valid data, it needs to pass 3 out of 4 Rs.

If something passes only two, you need to dig into it and determine why it's valid - and why it isn't. Then, you should make a determination on whether you can improve the data to hit at least 3 Rs or if you need to get rid of it.

If it's 1 R or less, it's a candidate to get rid of. Combine it elsewhere, totally delete it, etc. - but don’t just let it sit there and create noise.

Noise is expensive, costing you storage, integration, staff time, and most importantly, decision-making clarity!

Why This Matters

When you run your eight buckets through the Four Rs, the fog lifts. Suddenly, vague questions like “Why are we losing customers?” become targeted:

• Customer data shows who is leaving.

• Service data shows what issues they had.

• Purchase and invoice data shows when buying slowed down.

• Sales data shows how opportunities slipped.

Now you’re not guessing. You’re building knowledge that can drive ROI.

And notice: this whole process is platform-agnostic. Salesforce, Azure, NetSuite, Informatica - those are activators.

The strategy comes first.

Tools only matter once you’ve done the work to bucket and test your data.

Test my framework

If you’re nodding your head, then you’re wondering “okay, what’s the first step we need to take?”

Check your opportunities

I believe everyone has a problem. You might not … so let’s test it.

Go into your CRM and open up a recent sales opportunity.

Now, look at each field and run it through the four Rs. How many are actually valid (and pass 3+ of the 4 Rs)?

I’m willing to bet it’s less than half.

If not, come tell me why I’m wrong over on LinkedIn!

Categorize

While you’re on that Opportunity page, take each field and categorize it into the eight buckets listed above. (Hint: they do not all go to Sales!)

This makes you think - now you’re starting to flex your brain into a data-first mindset :)

Extra credit: think of how your CRM talks to your ERP and/or marketing stack

How do you want to classify the data that’s moving between systems?

Where do fields in your CRM go, and how do they come into this system from others?

Now, you’ve started applying the data-first framework.

Your Takeaway

I’ve seen this across industries, and the pattern is always the same. Companies are data rich but information poor. The ones that break out of that cycle don’t do it by buying another tool.

They do it by putting their data into the right buckets, running it through the Four Rs, and stripping away the noise.

Because at the end of the day, having more data isn’t an advantage.

Knowing what data to trust and focusing only on that is. When you're ready to build your winning data strategy, we're here to help.