From Data Chaos to Business Insights
- Andy Boettcher
- Aug 29
- 4 min read
Updated: Nov 11
Every company is generating more data than it knows what to do with. CRM records, ERP transactions, spreadsheets, chat logs, emails, web analytics ... it’s endless.
But the real issue isn’t the amount of data. It’s how much of it is actually useful.
Most organizations are drowning in unhelpful data. They don’t have too much data. They have too much that doesn’t drive decisions.
They’re sitting on dashboards, buried in Salesforce fields, and still starving for actual knowledge. The result isn’t sharper decisions, but stress, overwhelm, and wasted investment.
Here’s how I look at it: every company has data. Most have some information.
Very few get to insights and almost no one gets to true knowledge. How do we turn data noise into clarity?
This is the pyramid that forms how we approach data strategy: Data → Information → Insight → Knowledge.
The higher you climb, the more value you unlock. But too many organizations get stuck at the bottom, usually without realizing it.
The Trap: Data Rich, Information Poor
I’ll give you an example: about a year ago, a manufacturing client came to me asking to pull order history into Salesforce. It made sense and was a super-straightforward request until they wanted more:service history, bill of materials, accounting data, all of this in one place.
Their instinct was they should bring it all into one system and create a one-stop shop for data.
That’s not quite it.
I’ve seen companies using 150+ data points on every sales opportunity. Usually, those sales teams used maybe 20 of them. That’s 86% of data that wasn’t driving towards anything useful!
Every dollar has to prove ROI. Replicating data everywhere doesn’t just add cost, it creates noise.
Noise ≠ Insights.
An Additional Human Cost: Stress
Data bloat isn’t just technical. It weighs on people.
Executives look at years of collected data and think, “I should be able to answer this question.” But the answer isn’t there. That gap creates stress, erodes trust in systems, and frustrates teams.
The way out isn’t to “boil the ocean.” It’s to start small. One win leads to another: a simple insight that improves retention or a clearer trend that shapes customer strategy.
Those wins stack. Suddenly, you’re building real knowledge!
It’s tempting to dive in and start optimizing for these individual wins, and you do need to show wins when you’re tackling your data - and truly, your business - strategy challenge.
However, there’s a process I’ve used time and again that makes this far more straightforward and avoids the all-too-common mistake of thinking on a per-system basis.
A Smarter Path to Data Strategy
So how do you actually do this? I’ll briefly touch on five key components, but there’s so much to each of these.
Categorize
Every company has eight categories of data:
Customer
Sales
Quote
Purchase
Invoice
Service
Marketing
Communications
Start by bucketing your data by category - not by individual system.
This allows you to best understand what types of data live where, but also to begin thinking through who actually needs each type. Your data also includes three types:
Structured: records in systems like CRM or ERP
Semi-structured: spreadsheets, forms, or exports
Unstructured: documents, emails, chats, call recordings
The last two are where chaos typically reigns, often hidden in plain sight. When you see this data as parts of a whole, now you start to calm chaos so you can uncover insights.
Run it through the Four Rs.
For each category, ask if your data is…
Relevant - does this data answer a business question and have a clear purpose?
Reliable - simply put, is what you’re seeing the full picture and is it something you inherently trust?
Revealing - does it show trends that make sense and could be acted upon?
Reusable - is this data something you can use again and again over the next months and years?
You may be surprised at how much data doesn’t pass the 4 Rs test. This is a good thing! It’s helping you separate what’s useful from what’s noise.
Focus on ROI.
If an initiative doesn’t either drive revenue or reduce cost, it’s not worth it.
Period.
Bucket your data that’s passed the 4 Rs into one of these two ROI designations. Anything that doesn’t fit isn’t worth your time.
Let technology activate, not dictate.
Salesforce, Azure, NetSuite, MuleSoft, Hubspot - any technology is an activator.
The strategy comes first. Tools should serve it, not define it. So do NOT let what platforms you’re using today change how you approach the data.
Plan for what’s next.
Data strategy isn’t just about solving today’s problem. It has to prepare you for what’s coming six, 12, 18 months from now.
That includes AI, though its so much more.
Related: Stop buying into these AI myths
A Data-First Framework
This is why we bring out data-first framework into every engagement. It’s quick, it’s agile, and it grows with your business. Every problem we look at, we run through the same lens:
What’s the data? What can I learn from it?
What’s the information? What do I know?
What insights are missing? What do I NOT know?
How do we move toward knowledge? What can we learn?
We’re not chasing endless executive workshops.
We’re chasing clarity, outcomes, and ROI. Even if we don’t hit the top of the pyramid on day one (and most can’t), we set the path to get there.
We start with the right questions, define the true business needs, and then align your data to your business strategy. Because ultimately, they’re one and the same.
Only then does this distill into a real action plan that drives your processes, people, and technology.
Remember: Knowledge > Insights > Data
Having data isn’t the advantage. Turning it into knowledge is. This is the crux of our data-first framework here at DoubleTrack, and if you want to see how it works for you, let me know.
Or, dive into it yourself in our Data Chaos webinar - it's ungated, no email required!
Because it's how you get out of chaos and into ROI. And that’s the work I love doing with our clients today!
