If you’ve just inherited a monster CSV, or your boss dropped 10 million records on your desk, you’re probably wondering how to get this data into FastStats without losing your mind (or wrecking the database). This guide is for anyone who needs to bring large datasets into Apteco FastStats, clean them up, and actually trust the results.
No sales fluff—just what you need to know to get the job done, avoid common headaches, and not waste time on things that won’t matter.
Step 1: Get Your Data Ready Before Importing
Don’t skip this. Start by looking at your raw files before you even open FastStats. It’s way faster to fix obvious problems now than after you’ve imported.
- Check file format: FastStats likes CSV, TXT, or Excel. Stick to CSV for anything big—Excel gets flaky with large files.
- File size: Anything over a few million rows? Consider splitting the file or using FastStats’ database connectors instead of a flat file.
- Encoding: UTF-8 saves a lot of pain with special characters.
- Obvious junk: Open the file in a text editor (not Excel—Excel hides problems). Look for:
- Weird headers or extra rows
- Incomplete lines
- Strange delimiters (commas, tabs, etc.)
Pro Tip: If you can, ask for data extracts in the format you want. Getting clean data from the source beats hours of cleanup later.
Step 2: Set Up a Data Import in FastStats
FastStats isn’t hard to use, but it’s picky about the setup. Here’s how to get your data in:
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Open FastStats Designer
You need Designer, not just the Explorer, to set up imports. -
Create a New Data Import Project
- Go to ‘Data’ > ‘Import Data’ > ‘New Import Project’.
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Name it something you’ll remember. Don’t use today’s date—you’ll forget why you imported it in a week.
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Choose Data Source
- For big files, pick ‘Text/CSV File’.
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For massive data (think millions of records), use ODBC or direct-to-database import if you can. It’s faster and more reliable.
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Map Fields
- FastStats will try to guess your columns. It’s usually wrong for anything complicated.
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Go through each field:
- Check data types (text, number, date). If you get this wrong, you’ll have import errors or, worse, dirty data you don’t spot until later.
- Rename fields to something clear. “Field1” isn’t helpful six months from now.
- Set key fields correctly—the unique identifier for each record.
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Set Import Options
- Turn on deduplication if you know you have repeats (see Step 4).
- Set up field-specific import rules if you know, for example, that certain fields will always be blank or need fixed values.
What works:
- CSVs with clean headers and consistent data types import smoothly.
- ODBC is great if you’re pulling from SQL Server or similar.
What doesn’t:
- Messy Excel files with merged cells, subtotals, or random formatting. Save as CSV and clean first.
- Files over 2GB as a single CSV—FastStats can choke, and you’ll lose hours.
Step 3: Preview and Validate the Import
Don’t just hit “Go” and walk away. Always preview the import.
- Use the ‘Preview’ button in Designer to check the first 100 or 1000 rows.
- Look for:
- Misaligned columns (e.g., address data under phone number)
- Unexpected blanks or nulls
- Obvious outliers (e.g., birth year 1899)
If you see weirdness, stop and fix the source file. Trying to clean up inside FastStats is much harder after you’ve imported garbage.
Pro Tip: If you’re importing a test chunk first, use an actual slice of production data, not a “toy” dataset. You want to catch real-world issues.
Step 4: Clean and Deduplicate Your Data
Once your data is in, the real work starts. FastStats can’t magically know what “clean” means for your business, but it has decent tools for basic hygiene.
Basic Cleaning Tasks
- Deduplication:
- Use the ‘Deduplicate’ wizard in Designer.
- Set match rules—don’t just dedupe on name or email, or you’ll miss duplicates with typos.
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Check results; false positives are common. Always review a sample.
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Standardize Fields:
- Fix inconsistent values (“NY”, “N.Y.”, “New York”).
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Use FastStats’ ‘Recoding’ tools or, honestly, do a lot of this in Excel or Python before import if possible.
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Handle Nulls/Blanks:
- Replace empty fields with a standard value (“Unknown” or 0), if your analysis needs it.
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Be careful not to add fake data that’ll skew results.
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Remove Obvious Outliers:
- Filter out records with impossible dates, phone numbers, or amounts.
What to Ignore (for Now)
- Minor typos: Don’t spend hours fixing every spelling error in free text fields unless it matters.
- Extra fields: If you imported fields you don’t need, just ignore them. Don’t waste time deleting unless there’s a security risk.
Step 5: Index and Optimize for Performance
Large datasets can make FastStats crawl, especially if you’re doing a lot of queries or selections.
- Set up indexes on commonly used fields (customer ID, email, postcode).
- Partition data if you have time—split by year, region, or another logical chunk.
- Limit what you import: If you don’t need 10 years of history, don’t import it. Smaller datasets are easier to manage and clean.
What actually helps:
- Investing a bit of time up front to think about how you’ll use the data. If you know you’ll never filter by “favorite color,” don’t bother indexing it.
What doesn’t:
- Over-indexing. Too many indexes can slow down imports and don’t help much for fields you rarely use.
Step 6: Validate, Test, and Document
Now’s the time to check your work. It’s boring but critical.
- Run some counts: Does the number of imported records match your source? If not, find out why.
- Test queries: Pull a few random records and check them against the source. Look for missing or garbled data.
- Document what you did: Write down import settings, cleaning rules, and any weird quirks for next time. Future You will thank Present You.
Pro Tips for Not Losing Your Sanity
- Automate what you can: Set up repeatable import jobs for regular data feeds. Manual imports are error-prone.
- Don’t trust one-off scripts: If you’re cleaning data outside FastStats, keep a copy of your scripts for transparency.
- Backups. Always. Before and after big imports.
When to Ask for Help (and When Not To)
- Do ask for help if:
- The import is failing and the logs make no sense.
- You’re seeing weird performance issues after import.
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You need to connect to a new data source (e.g., a database you’re not familiar with).
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Don’t bother asking (yet) if:
- The problem is a blank field or minor formatting error. These are almost always source data issues.
Wrapping Up: Keep It Simple, Iterate Often
Importing and cleaning big datasets in FastStats isn’t magic, but it rewards a methodical, no-nonsense approach. Don’t get lost in the weeds—clean just enough to get started, document what you did, and come back to refine as you learn more. Most headaches come from trying to do everything at once. Start small, check your work, and build from there.
And remember: FastStats is a tool, not a miracle worker. Garbage in, garbage out—so spend your time where it matters.