The CleanSmartLabs Blog

Nobody wakes up excited to clean data.

But here you are. Maybe a spreadsheet just broke your pivot table. Maybe you're staring at 12,000 rows wondering how many are duplicates. Maybe someone just asked for "clean customer data by Friday" like that's a simple request.


We get it. Data cleaning is the work that has to happen before the work that actually matters. It's unglamorous, time-consuming, and weirdly satisfying when you finally get it right.


This blog is about making that process less painful. We write about the stuff that actually causes problems—duplicate records, formatting disasters, missing values, outliers that wreck your averages—and how to fix them. Sometimes we'll get technical. Sometimes we'll just commiserate.


No thought leadership fluff. No "data is the new oil" nonsense. Just practical advice for people who have real datasets and real deadlines.

Data processing concept: glowing server transferring data to a shipping label and box.
By William Flaiz January 27, 2026
Stop losing packages to overzealous standardization. Learn how to normalize addresses without dropping apartment numbers, breaking international formats, or creating returns.
Abstract graphic of data transformation: cubes funnel into a glowing, hexagonal structure.
By William Flaiz January 26, 2026
Step-by-step guide to cleaning customer data in your CRM. Find duplicates, fix formatting, fill gaps without losing critical records. Practical tips inside.
Data processing visualization: data flows from “Detect,” “Filter,” and “Standardize” to a data sheet with dates, one marked as complete.
By William Flaiz January 21, 2026
Excel turned your dates into five-digit numbers again. Here's how to fix the damage and prevent it from happening next time.
Data flow illustration with Shopify, Salesforce, and HubSpot integrated, leading to a verified user profile.
By William Flaiz January 14, 2026
How to merge customer records from Shopify, Salesforce, and HubSpot into one clean dataset. Field mapping examples and identity resolution tips.
Scientific diagram: Particles passing through a funnel, with a laser beam hitting a hexagonal target labeled
By William Flaiz January 7, 2026
Build a 0-100 Clarity Score to measure data quality. Covers completeness, consistency, duplicates, anomalies—plus a scorecard template.
Digital shield over a network of hexagons and circuits, with a green gradient.
By William Flaiz January 2, 2026
A practical playbook for RevOps leaders: roles, rituals, templates, and a quarterly roadmap to build data trust across your organization.
Abstract illustration of data transformation through a system. Numbers and data flow, changing from the left to a new form on the right.
By William Flaiz December 30, 2025
Your CRM has the same phone number stored 47 different ways. Here's why that happens and how to fix it permanently.
Digital workflow with glowing checkmarks moving through square panels to complete a checklist.
By William Flaiz December 29, 2025
Stop catching CSV errors after they've already broken something. These validation rules prevent bad data from getting into your system in the first place.
Abstract digital graphic with hexagons, dots, and glowing lines, set against a light blue background.
By William Flaiz December 23, 2025
Learn when simple rules suffice and when ML pays off. Spot outliers, cut false positives, and protect decisions with CleanSmart’s LogicGuard.
Grid of tiles with some highlighted in green, a green speedometer at the bottom.
By William Flaiz December 22, 2025
A practical guide to missing data: when to impute and when to flag. Boost data trust with SmartFill confidence scores for cleaner, reliable analytics.
Diagram of a data network with hexagonal grid and nodes connected by lines.
By William Flaiz December 18, 2025
Fuzzy matching misses duplicates that semantic AI catches. Learn why "Jon Smyth" and "Jonathan Smith" slip through traditional deduplication—and how to fix it.
Abstract illustration of data processing: a cube with data streams connecting to a honeycomb structure, all in shades of blue and white.
By William Flaiz December 17, 2025
CSVs are everywhere—and so are their problems. Encoding nightmares, Excel date mangling, delimiter chaos. Learn what goes wrong and how to fix it.
Abstract illustration of data transformation, with fragmented elements flowing toward a glowing cube on a platform.
By William Flaiz December 12, 2025
The cost of bad data is wasted spend, missed deals, and broken trust. Learn how to quantify it, stop duplicates, standardize, and build a lasting fix.
Diagram depicting data filtering through a series of layered structures, represented by rectangles, with connecting lines.
By William Flaiz December 9, 2025
You've got a dataset. You've got a deadline. You've got a boss who wants insights by Thursday. The temptation is to skip straight to the analysis. Don't. Dirty data doesn't announce itself. It hides in plain sight until your quarterly report shows revenue doubled (it didn't) or your email campaign goes out to 4,000 contacts who are actually the same 900 people entered multiple ways. I've seen both happen. The revenue one was worse. Here's what to check before you trust any dataset enough to make decisions from it.