5 Main mistakes in Dashboard

🧩 1. Show too much data (Too much data)

Problem: Analysts often show everything, sales, product details, channels, customers, etc., but executives want the Dashboard "summary for decision," not "raw data to analyze."

📌 Having too much information, yes, that's always good. It can take a long time to understand the Dashboard.

The solution:

▫️ focus on 3-5 key KPIs and summarize key Insights on the home page.

▫️ in-depth detail section (Drill-down), then put it on the secondary page.

🧩 2. No clear goal (No Clear Business Question)

The problem: Dashboards are designed according to the data they contain, not business questions such as "want to see sales," but not "to measure what?"

📌 Understanding the roles and functions of the people we make Dashboard will help design elements and KPIs in Dashboard well.

The solution:

▫️ start by asking the executive, "What do you want to make?"

And design the Dashboard to answer that problem directly.

🧩 3. No Comparative Context (No Context or Benchmark)

Problem: Show a single number, such as "50 million sales."

But there is no data to interpret "good or bad?" compared to the target, the month before, or the year before.

📌 The comparison allows Dashboard users to know what words proceed.

The solution:

▫️ always compare, like "+ 8% from the previous month" or "92% of the target."

🧩 4. Poor Visualization Design

Problem: Use many kinds of graphs, too many colors, or out of sequence, and executives have to "interpret themselves" to waste time because they don't know where to focus.

The solution:

▫️ use Data Storytelling.

Data Sequence from Summary → Details Put color to communicate

🧩 5. Does not describe Insight or Actionable Point.

Problem: Dashboard tells "what happened" but doesn't tell "why" and "what should be done next."

The solution:

▫️ add an Insight block or a Dashboard end summary, e.g.

"Sales cut in North because stock lacks."

To make a Dashboard, we may need to improve the role of the user, for example, some use it to make decisions, some use it for operations. Each function affects the details of the Dashboard differently.

# data aanalyst # Data ascience # Self-improvement # Data engineer # Data line work