Slow is smooth and smooth is fast. This means a deliberate, precise approach will lead to greater speed and efficiency and minimising errors & mistakes.
It’s tempting to jump straight into analysing data for insights and answers quickly. However, it is important not to skip straight into the weeds, and instead first gain an understanding of:
- the overall data structure
- the underlying questions being asked
- cleaning the data for analysis
With this backbone behind you, analysis can be run with the confidence actionable insights will be generated (not hunting in the dark) with limited errors.
The Data Structure
Questions we would ask when setting up our data analysis would include:
- What systems is the data currently in? Accounting? CRMs? Excel?
- How can the data be accessed? API? Excel export? SQL database?
- How frequently is the data updated, and who “owns” it internally?
- Are there known issues or gaps in the data?
- How do the different systems relate to each other?
- What pieces are leading indicators (such as number of meetings held) vs lagging reporting (invoices issued)?
- How are dates stored in the dataset? (it’s amazing how many different ways dates can be stored!)
This early mapping exercise gives us a clear view of the terrain. What’s reliable, what needs fixing, and what might be missing entirely.
Clarifying the Question
It’s surprising how often analysis gets bogged down because the real question wasn’t defined up front. Taking the time to ask “what decision will this analysis support?” avoids producing pretty charts that don’t provide any real insights or actually help anyone.
We also dig into:
- What are the key metrics or outcomes the client cares about?
- Are we measuring performance, diagnosing a problem, or forecasting the future?
- How often will this need to be looked at? Once off? Monthly? Weekly?
- Who is the audience for the final output, and how will they use it?
When the questions are clear, the analysis becomes targeted and meaningful.
Cleaning & Preparing the Data
Only once we understand the structure and purpose do we move into cleaning. This step isn’t glamorous, but it’s where most accuracy problems get solved. Cleaning typically involves:
- Standardising formats (dates, currencies, etc)
- Handling missing values (only partially filled in CRM data anyone?)
- Removing duplicates (same person, multiple email addresses?)
- Aligning naming conventions across datasets (“John Smith” VS “Smith, John“)
- Checking for outliers or anomalies
- Ensuring consistency across time periods
Good cleaning transforms raw, messy data into something trustworthy. Now the insights rest on a solid foundation.
Bringing It All Together
By approaching analysis with intention (understanding the structure, clarifying the questions, and pre-cleaning) we set ourselves up for fast, confident analysis. The slow upfront steps actually accelerate the end reporting. They reduce rework, avoid misinterpretation, and ensure insights are both accurate and actionable.
In short: start slow, finish fast