There once was a room full of silent executives...
Early in my BI consulting career, I vividly remember presenting a final review of a Sales Dashboard I had built to a room full of executives and department leaders at an athletic equipment manufacturer. I found myself panicking: Why it was so quiet? Why no one was asking questions? It seemed strange because over the past three months, everyone had been outspoken. But this time was different.
When the walkthrough was done, they all applauded, stood up, and started shaking hands and congratulating each other on a job well done. As they walked out and congratulated me, it hit me: They all, at one point or another, had been involved in building the Sales Dashboard. The BI process started right from the beginning, not just when the dashboard was about to go live.
The early months: discovering what the data can tell you
During the previous few months, the entire team had started to really dive into their data, and they were beginning to gain valuable insights into how their systems actually worked and were set up vs. how they thought they were – perception vs reality.
This is where the BI process started.
Reviewing existing sales reports
We started by looking closely at the sales reports they were already using. This helped us see what data was available, where there were gaps, and what metrics might need tweaking. It gave the team something to work from and helped clarify what questions BI should answer.
Understanding data sources and flow
Next, we dug into where the data was coming from, both inside and outside the company. Figuring out the origins and how the data moved through systems helped shine a light on blind spots or gaps that could hurt the data’s usefulness.
Integration and transformation steps
The data didn’t just arrive ready to use. It went through ELT, and integration processes. Understanding each step in the ETL process for their particular workload helped us make sure the data was accurate and consistent, helping avoid surprises down the road.
Storage and data update frequency
We also looked at where the data was stored, usually in data warehouses or lakes, and how often it was updated. This gave us a sense of how fresh the data would be and how quickly the teams could act on it.
Moving toward dashboard design
Only after these steps did we start designing the dashboard itself. It was important that the dashboard reflected what the data could really support, avoiding flashy visuals that didn’t solve any real problems.
Making BI work where it counts
If there’s one thing experienced BI folks know, it’s that setting up the right technology is only half the battle. For BI leaders, it’s never just about rolling out dashboards. Success means running a living BI system where data pipelines, analytics, and people all work together.
One major challenge is making BI part of how the company makes decisions day to day. That means dealing with a lot of messy, real-world complexities beyond just dashboards and models.
It isn’t just hyperbole. You have to get different stakeholders aligned, even when they have different ideas about what “trusted data” means. You need to manage data silos and governance so data stays solid, but users still have the freedom to explore and self-serve. You have to balance moving fast, trying new things, and maintaining controls so data remains correct and compliant. Sometimes, you have to figure out a way to persuade everyone to move more slowly now so that they can move more quickly later.
And the questions users ask? They don’t stay the same, anyway. As people get comfortable with data, they dig deeper and push for more detailed answers. That means teams have to keep working together to surface and solve fresh problems. This isn’t a drop-it-and-run situation.
Get the right people involved early
When it came to data collection and integration, it was essential to include the data warehouse team. But it was just as important to bring in the sales report stakeholders, or end users. Usually, these users aren’t interested in the technical backend details, but by participating, they learned critically important things like:
- How often data is updated and how available it is
- The mix of internal and external data sources
- What happens with data inconsistencies
- How reference info like employee details and stock levels fit in
Designing dashboards around real questions and needs
When we moved into data analysis and front-end reporting and visualization design, we brought the warehouse team back in. They learned about the questions sales and leaders ask all the time, and that helped them to build data infrastructure that corresponded to reality. Those questions were things like:
- Is the data fresh during a sales call?
- Can we confirm product availability for promised delivery?
- What seasonal trends should we watch?
- How do order volumes vary by time and day?
- What does the demand forecast say?
- How can new sales staff ramp up faster using data?
- Where do margins really stand?
How the dashboard became part of daily workflows
What started off looking like static charts became living, breathing insights woven into daily work. Sales teams could check inventory live during calls instead of guessing. Finance spotted margin shifts early and acted fast. Operations saw demand spikes coming and adjusted plans. The dashboard became a proactive decision-making hub, moving the company from firefighting to planning ahead.
What comes next: predictive analytics, push alerts, and staying engaged
Getting that dashboard live wasn’t the finish line. It was only the beginning. Once a company builds BI muscle, they start moving toward predictive analytics, alerts that push info in real time, and forecasting. The questions change from what happened to what’s next and what should we do.
Predictive models and alerts let teams forecast sales, spot risks, and act faster. But this level requires keeping stakeholders deeply involved, early and often. There are new challenges like alert overload, wrong assumptions baked into models, misunderstood results, and the risk of trusting black box tools without question.
To keep it working, teams need constant tuning, validation, and open communication. Automation is great, especially for the ‘boring stuff’, but human oversight and judgement are and will remain key.
A note on change
If your team can at times seem reluctant to engage early, or to adapt a new tool, here’s a great article by my colleage Mike McAlpine to help you overcome this hurdle: 6 Change Management Strategies to Boost User Adoption Success.
How DI Squared Helps
Ready to make BI work beyond dashboards? Partner with DI Squared to turn your data into a dynamic business asset that drives smarter decisions and sustained competitive advantage. Schedule your free data strategy session today.
