Data Services for Data and Analytics Teams
Your team was hired to build models and generate insights. Not to spend 60% of their time cleaning datasets.
Data scientists and analysts spend an estimated 60% of their time on data preparation and cleaning, according to surveys from Kaggle and Anaconda. That's $60,000+ per year in salary going to work that doesn't require their expertise.
The irony is that companies hire data teams to extract insights, but the teams get buried in the prerequisite work: standardizing formats, resolving entities, deduplicating records, filling missing values, and validating data quality. The analysis that leadership actually wants keeps getting pushed back.
Verum handles the data preparation so your team can focus on the analysis.
The Data Problems You Deal With
60% of time on prep, not analysis
Your team spends more time cleaning data than analyzing it. Every project starts with the same tedious prep work before the real work begins.
Entity resolution is a recurring headache
Matching records across systems with different identifiers, name formats, and data structures is critical and time-consuming. Getting it wrong invalidates downstream analysis.
Data quality degrades between projects
You clean a dataset for one analysis. Six months later, someone needs the same data and it's decayed again. There's no maintenance layer.
Enrichment gaps limit your models
Your predictive models need firmographic, technographic, and contact data that doesn't exist in your internal systems. Filling those gaps manually is slow.
How Verum Helps
- Dataset preparation: Send us raw data from any source. We clean, standardize, deduplicate, and deliver analysis-ready datasets. Your team starts with clean data instead of spending weeks on prep.
- Entity resolution at scale: We match records across systems using fuzzy matching, probabilistic linkage, and multi-field comparison. Company names, contact names, addresses, and identifiers resolved into unified entities.
- Enrichment for model features: We add external data fields your models need: company size, industry classification, technology usage, geographic data, and contact attributes. From 50+ data sources.
- Ongoing data quality monitoring: Monthly validation and re-enrichment keeps your datasets current. Data quality doesn't degrade between projects because we maintain it continuously.
- Custom data pipelines: Recurring data needs (quarterly customer analysis, monthly market sizing, weekly lead scoring inputs) can be automated on a schedule. We deliver updated, clean datasets at whatever cadence you need.
Common Use Cases
Customer analysis dataset prep
Your customer success team wants a churn analysis. You need to merge CRM data, billing data, and product usage data into a single clean dataset. We handle the merge, deduplication, and entity resolution so you can go straight to modeling.
Market research enrichment
Building a market map or competitive analysis? We enrich company lists with firmographic data, technology usage, employee counts, revenue estimates, and contact information. Clean, structured data ready for analysis.
Training data preparation
ML models are only as good as their training data. We clean, standardize, and validate the datasets your models train on. Consistent labeling, resolved entities, and validated records improve model accuracy.
Board reporting data cleanup
Before board meetings, the data team scrambles to reconcile numbers across systems. We maintain clean, deduplicated datasets so your reporting always pulls from a single source of truth.
Frequently Asked Questions
What data formats do you work with?
We accept CSV, Excel, JSON, Parquet, database exports, and API connections. We deliver in whatever format your team prefers. For recurring projects, we can push directly to your data warehouse or cloud storage.
Can you handle datasets larger than 1 million records?
Yes. Our infrastructure handles datasets of any size. For very large datasets (10M+ records), we scope the project upfront to set turnaround expectations. Most datasets under 1M records are delivered in 3-7 days.
How do you handle entity resolution across multiple systems?
We use a combination of deterministic matching (exact email, phone, ID matches) and probabilistic matching (fuzzy name comparison, address normalization, company name standardization). Match confidence scores are included so your team can set their own thresholds.
Stop Wasting Data Team Hours on Cleaning
Tell us what datasets need preparation and we'll show you how much time your team can get back.
See What We'll Find