Most people building dashboards start in the wrong place. They worry about charts, colours, and layouts — when the real work happens long before any of that.

Hi, I'm Mike Fox, host of this podcast, "Lone Wolf Unleashed." In this episode, I walk you through what actually needs to happen before a dashboard can tell you anything useful. Drawing on a real client scenario — a growing business moving from a file-based system to a CRM — I break down the data foundations you need in place first.

We cover the sales pipeline as the most logical starting point, why tracking inquiry-to-answer time can expose problems you didn't know you had, and how a shorter sales cycle directly improves your cash flow. I also explain why starting manually — before you automate anything — is the right call, and how to keep your data ecosystem simple enough to actually maintain.

If you've been thinking about dashboards, a new CRM, or just getting better visibility over what's happening in your business, this one lays the groundwork.

📥 Resources and tools mentioned: lonewolfunleashed.com/resources

Timestamped summary:

0:00 — Why visuals are the last thing to worry about

1:38 — Dashboards start with data

2:29 — Sales pipeline metrics

4:51 — Tracking DocuSign and sales cycle completion

6:20 — Why you should start manually before automating

7:30 — Connecting systems: APIs and AI agents

9:00 — Keep your data ecosystem simple

10:53 — Dashboard filters, date ranges, and wrap-up

Mentioned in this episode:

This podcast is part of the Podknows Podcasting ICN Network

You might also like...

Check out the "Websites Made Simple" podcast with Holly Christie at https://websitesmadesimple.co.uk/

So when we think about the components or the taxonomy of a dashboard,

we're talking about different pieces of data that we're trying to turn into information.

What does that mean? Well, data is the raw stuff that sits in a database somewhere.

Information is something that can be utilised to make decisions.

When we are making decisions, we need to make sure that data is in a position for us to use.

That data comes from your processes — the things generating your inputs and outputs.

Those inputs are going somewhere. We want to make sure they go into a database.

There are three different systems here that we're going to start to build for his dashboards.

And I think the first one is the most logical place — and that's the sales pipeline.

Different businesses have different setups, different levels of complexity.

For example, my business is a little bit more complex than his because my sales cycle is typically longer.

you get an inquiry, you assess whether you can help, and you turn them into a customer.

It's that fast. So having these types of sales stages isn't quite appropriate in that case.

But basically, do we know the time from first inquiry to inquiry answer? That's number one.

That's two pieces of information we need to track. One dimension: do we know when the inquiry came in?

Now, if you don't have a CRM, that's going to be really difficult. You'll have to track it manually.

But in a CRM, if you've got a form embedded on your website that's attached to your CRM,

your CRM will know exactly the time and date that inquiry comes in.

When you go to answer the inquiry, there's another timestamp that needs to be managed.

It's not simply sending a return email and going "yep, job done." Is that timestamp captured within your system?

What the dashboard is going to do is pick up both of those things and calculate: inquiry answer time minus inquiry time.

And that is going to give you the average time for your inquiries.

Now, something to think about — do you want the average? Do you want the median? Do you want it per inquiry?

You might also want a second dimension over that, which is your date ranges.

In Q1, was our inquiry answering time higher or lower than the previous quarter?

And you can track that over time. The challenge when we build these systems is we don't know initially,

when going from a manual system to an automated one, which measures are going to be best to track.

let's say you want to know when a document comes back from a client.

In this case, forms would go out and come back signed through something like DocuSign.

And that is the moment when they become a customer.

So if you're tracking your overall sales cycle — from first inquiry through to signing as a client —

that's a critical thing. Because if you can get that number down as quickly as possible,

that can fundamentally change your cash flow, because you get paid sooner.

If you've got a large pipeline with a low sales cycle and your delivery processes are set up well,

you get paid faster, you get paid more, you make more money. It's that simple.

The challenge and complexity is a little higher now because we've got this timestamp captured somewhere —

in DocuSign at the time of signing — and we need to track that in the CRM.

Now we're talking about how we get that from one place to another.

My initial recommendation: if your volumes are relatively low, you need to do it manually.

Why? Because starting manually, you really get to know the intricacies of what works and what doesn't.

What the different rules of behaviour of the system are supposed to be.

If we try to automate this from the first moment, we're going to be making assumptions

about what you are doing and what the systems are doing that may be incorrect.

When you go to implement, it's those assumptions that really kick you in the backside.

Things start to go wrong. "Oh, we didn't account for this."

And then there's additional time spent trying to fix it. It's frustrating for both parties.

From my side, I have to deal with more support, and I may not get paid my delivery fee until it's fixed.

On the client side, it's frustrating because you've paid for a system that isn't working.

So what we want to make sure of is that when you're doing it manually,

we can really pick up those types of requirements and edge cases really, really early.

Connecting the two systems then becomes a requirement. There are multiple ways to do this.

The number one thing I'd explore is looking at DocuSign's API documentation —

whether or not the CRM and DocuSign can talk to each other.

That's done through what they call an API — the thing that connects both systems

so we can push data from one place to another.

If the CRM has a field set up to track the form return, we can pull that from DocuSign and put it in the CRM.

The second option, if that's not available, is that DocuSign sends an email when the form is signed.

So your customer gets notified by email. What you could do here

is have an AI agent sit on top of the email server — like an MCP server —

which would then punch the date and time of that email into that record in the database.

So that's another option. And there are many, many other options out there.

The third option might be to simply hire someone to manage the information.

But the point is: you can't track that information if it's not sitting in the database.

What I really like to recommend to smaller customers is to make your ecosystem as simple as possible.

Large organisations have things like data lakes — really complex environments to manage.

What we want to make sure of is that our databases sit within a simple ecosystem —

easy to understand, and easy to get the information out that we're really seeking.

If you're a small player, I highly recommend getting your information managed in the same place.

That way we're not having to deal with all these different integrations.

Just remember — every document is a surface you need to maintain. The same applies to your data.

If you're not maintaining your data and information, those things will break over time.

It's just like anything in your house. Right now I have a retaining wall coming down into my shed.

It's not terribly bad at the moment, but each day it's going to get worse.

It's really important that we keep on top of maintaining those things —

and make it really simple for us to do so. That's why I bought a house with not a big yard.

Easy to maintain. Doesn't take as much time. The same applies for your business.

So once we get that information into the right spots — dates, timestamps, dollars, number of cases —

whatever it is you're trying to track in the database, that's when we can start to see it on a dashboard.

Now, some standard dashboard functionality: chart types, maybe a colour scheme —

that's up to you to configure. The other thing is your typical date ranges.

What sort of date ranges do you want to track? I use a time tracking tool called Clockify.

There's reporting and dashboards in there — which projects I've spent time on, how much time,

and over certain date ranges. It changes those pieces of information based on the parameters I set.

It's also important to note down what other filters or parameters you need to have access to.

Maybe you only want to track it quarterly — well, put in a quarterly filter on your date range.

Maybe you need to track it every day. That's just something you need to decide.

Make sure you put down those types of requirements, because if you're handing this off to someone else —

and I do work with specialists who deal specifically with data reporting and dashboards —

it's important to hand them the specifications so they can go and build it.

And the same applies if you're going to implement it yourself.

So that's going to do us for today. I really appreciate your time.

You could have been doing a million things, but you decided to hang out with me

and learn about how to put together the data that needs to go on your dashboard.

Make sure to go and check out my website — lonewolfunleashed.com/resources.

There are some really useful resources there about building up business systems.

One of the most popular ones at the moment is my guide to building your first AI agent.

You can also sign up for my newsletter. Right now I have a series going on setting up Claude

and using it the way I'm using it. I'm finding that I'm saving weeks —

it's really fundamentally changing my business model. Charging by time is not really feasible anymore.

It's really changing the game for how service businesses can utilise AI to expedite the heavy admin work.

Thanks for joining me today, and I'll see you next week.