Most companies want to be “data-driven,” but many fall into the same traps. These mistakes waste time, distort insights, and slow down decision-making.Here are the ten most common issues — and how to fix each one in a simple and practical way.

1. Collecting Too Much Data Without a Real Purpose

Many companies gather every piece of data possible “just in case.” The result: huge files, scattered systems, and no clear idea of what actually matters.To fix it:Decide what business problem you want to solve before collecting anything.Ask: “What decision will this data support?”If it doesn’t support a clear decision or KPI, you probably don’t need it.

2. Poor Data Quality (Duplicates, Missing Values, Wrong Formats)

Bad data leads to bad decisions. Errors spread across dashboards, reports, and models. This is one of the most well-documented problems in analytics. For example, researchers in CleanML found that poor data quality directly reduces accuracy and reliability in machine-learning outcomes.To fix it:Build a simple cleaning checklist:
  • remove duplicates
  • check formats
  • standardize values
  • fix missing or incorrect entries
You can automate parts of this with Excel, Power BI, SQL, or Python.

3. Storing Data in Silos Across Different Teams

 Marketing has its data. Sales has another. Operations keeps its own spreadsheets. Teams don’t see the full picture — so decisions are based on partial information.To fix it:
  • Centralize your data.
  • Use one shared location (a data warehouse, OneLake, shared SQL database, or BI layer).
  • Give teams controlled access, but keep the data in one place.

4. Manual Reporting That Takes Hours Every Week

Employees spend hours copying numbers, updating slides, or fixing spreadsheets. This leads to errors, outdated reports, and wasted time.To fix it:Automate repetitive reporting tasks using tools like Power BI, Power Automate, or scheduled SQL queries. Once automated, you only check results instead of rebuilding the report.

5. No Data Governance or Clear Ownership

 Nobody knows who owns the data, who updates it, or who is responsible for accuracy. This creates messy pipelines, outdated dashboards, and slow decision-making.To fix it:Assign ownership. One person or team manages quality, documentation, and updates. Governance doesn’t need to be complicated — clarity alone solves most issues.

6. Relying Only on Averages (Ignoring Distribution & Context)

Companies often summarize data with a single number — the average — even when it hides important details.Descriptive statistics research shows that understanding variability (range, percentiles, standard deviation) provides a more accurate picture than the mean alone.To fix it:Use distributions, segments, and descriptive statistics.Look at:
  • median
  • percentiles
  • standard deviation
  • clusters or groups
This gives a clearer view than a single number.

7. No Data Dictionary or Documentation

Different teams use different definitions for “customer,” “order,” or “conversion.” This creates confusion and inconsistent reporting.To fix it:
  • Create a simple data dictionary.
  • Define your metrics and share them internally.
  • It can be one shared page — the impact is huge.

8. Jumping to Dashboards Before Understanding the Data

Teams rush to build dashboards or visuals without checking data accuracy or context. This leads to pretty dashboards that don’t tell the truth.To fix it:Always perform exploratory analysis first.Check:
  • distributions
  • Outliers
  • missing values
  • Relationships
Only after that should you start building dashboards.

9. Using Too Many Tools Instead of a Clear Workflow

Companies keep adding tools: Excel, CRM systems, BI tools, Notion, Google Sheets…Data becomes scattered, duplicated, and inconsistent.To fix it:Choose a simple, structured workflow.For example:
  • SQL for storage
  • Power BI for dashboards
  • One shared place for raw and cleaned data
Fewer tools = fewer errors.

10. No Training for Employees Working With Data

Teams are asked to “use data” — but are not trained in the skills needed to clean, understand, or communicate it. This leads to slow work, wrong interpretations, and frustration.To fix it:Provide structured training in:
  • data literacy
  • Excel
  • Power BI
  • SQL
  • descriptive statistics
  • Storytelling
This builds a stronger culture and reduces mistakes long-term.

How the IMP Diploma Helps You Fix These Mistakes (CTA)

Most of these problems come from one thing: teams don’t have the right skills or the right data workflow.The Data Analysis & Business Intelligence Diploma from IMP gives your employees the skills they need to work with data correctly from the start. They learn:
  • how to clean and prepare data
  • how to analyze it using Excel, Power BI, and SQL
  • how to understand descriptive statistics
  • how to build dashboards
  • how to create data stories that drive decisions
If you want your team to stop repeating the same mistakes — this training helps them build a proper foundation.Contact IMP to get details about schedules, registration, and group training options.