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DevPals — Header Component
DevPals — Data Intelligence
Data Intelligence

Scale on data
you trust, not
spreadsheets

Your supplier data is inconsistent. Pricing lags. Your team wastes time on manual fixes. DevPals builds automated data pipelines that validate and clean your data in real-time — so you scale inventory, pricing decisions, and revenue with confidence.

Book a 30-min call
40k+
Data points per customer
Real-time
Pipeline validation
100%
Senior delivery
Pipeline validated
Just now · 0 errors
Live Pipeline
Supplier Data Quality
Dashboard
Data completeness 97%
Pricing accuracy 94%
Duplicate records 2%
Validated
1.2M records synced
📊
Manual fixes saved
−74%
DevPals — Data Intelligence
First- & Third-Party Data
We use Big Data analysis based on
first- and third-party data

More than 40,000 data points on your customers — ranging from recent purchases to loans — to unleash insights on segments and achieve:

01
Targeted Acquisition
Customized look-alike profiles for top-performing traffic that converts.
02
Better Conversion
Profiling-based custom funnels that move buyers through faster.
03
Personalised Retention
Individualized messaging and product experience that keeps customers coming back.
04
Sales Automation
When it comes to messaging, sales cannot connect the dots — data does it for them.
40,000+
DevPals stands at a data fork in the road and acknowledges the business need to enhance client interactions and business processes. We understand how big data profiling and analysis can aid in data quality control — simplifying the process of exploring and rebuilding complex data lakes.
Data Quality
DevPals Big Data Profiling Practices

Rigorous profiling techniques applied at every stage — so your data is trustworthy before it ever reaches a decision.

01.
Distinct count and percent
Identifies natural keys — distinct values in each column that aid in the processing of inserts and updates.
02.
Percent of zero / blank values
Identifies data that is missing or unknown. Assists ETL architects in establishing appropriate default values.
03.
Minimum / maximum string length
To improve performance, you can set column widths to be just wide enough for the data — no waste, no truncation.
01.
Key integrity
Ensures keys are always present using zero/blank/null analysis. Identifies orphan keys — problematic for ETL and future analysis.
02.
Cardinality
Examines one-to-one, one-to-many, and many-to-many relationships between related data sets. Assists BI tools in correctly performing inner or outer joins.
03.
Distributions
Checks that data fields are properly formatted. Data fields used for outbound communications — such as emails and phone numbers — are verified and well-formed.
Architecture Decisions
What is our process for choosing a database?

When it comes to selecting a database, we take into account both Relational Database Management Systems (RDBMS) and NoSQL databases, in order to gain a comprehensive understanding of each ecosystem. We evaluate different systems based on factors such as data type, storage, structure, and intended use — with the goal of meeting the specific needs of our clients.

RDBMS
Required consistency, latency conditions, and transaction speed — including real-time querying mechanisms.
NoSQL
Data type, storage, structure, and intended use — evaluated for scale and flexibility requirements.
Decision factors
Data shape, query patterns, scale requirements, and team expertise all feed into our architecture recommendations.
AI Data Readiness Audit — DevPals
DevPals · Data Intelligence Assessment

Check your AI data readiness

Get instant access to our 15-point data architecture and AI readiness checklist. Takes 3 minutes.

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Hi, there — let's evaluate your data intelligence setup

How robust is your data foundation for AI?

Select items that apply across all categories. Mutually exclusive states are managed automatically.

Progress 0 / 15
Data Collection & Storage — please select at least one item
Our data is scattered across multiple siloed databases without a unified source of truth
Siloed systems prevent models from aggregating comprehensive operational context.
We lack automated validation checks for incoming data pipelines, leading to frequent data drift
Unchecked pipeline errors result in "garbage in, garbage out" for machine learning outputs.
Historical data is archived inconsistently or lacks proper metadata documentation
Without structured metadata and history, training robust predictive models becomes nearly impossible.
Data Quality & Governance — please select at least one item
We struggle with duplicate, incomplete, or unlabelled customer and operational records
Poor data hygiene distorts analytics and compromises AI model accuracy.
Data lineage, ownership, and privacy compliance (GDPR/security standards) are poorly tracked
Unclear data governance introduces severe regulatory and compliance risks when deploying AI.
Cleaning and preparing raw data consumes over 70% of our engineering team's bandwidth
Heavy manual data wrangling stalls actual AI prototyping and feature delivery.
We do not have a centralized data catalog or a searchable dictionary for internal metrics
Teams waste countless hours recreating existing metrics or querying conflicting definitions.
Pipelines, Scale & Real-Time Access — please select at least one item
Our reporting and data processing jobs run exclusively via batch processing overnight
Real-time AI applications and recommendation engines require low-latency streaming pipelines.
Query performance drops significantly when handling heavy analytical or complex joins
Underperforming data warehouses block scalable ML inference workloads.
Infrastructure & Operations — please select at least one item
Cloud data storage and query compute costs scale unpredictably without optimization
Unmanaged cloud data architectures quickly inflate infrastructure spending.
We lack automated monitoring for data pipeline failures or silent query anomalies
Discovering broken data pipelines from downstream business dashboard drops is too late.
We have no staging environment or version control for data transformations (ETL/ELT)
Testing pipeline changes directly in production risks corrupting core analytics layers.
AI Strategy & Readiness — please select at least one item
Leadership wants to implement AI initiatives, but data infrastructure is treated as an afterthought
AI projects fail most frequently due to unready foundational data layers rather than bad models.
We lack dedicated data engineering expertise to build and maintain robust vector stores or LLM pipelines
Implementing advanced AI or RAG systems requires specialized modern data architecture expertise.
Our internal teams cannot easily access or query data securely without direct developer intervention
Friction in data access slows down cross-functional product experimentation and insight generation.

At least one item must be checked in every category to proceed

Analyzing your data intelligence profile...
Evaluating data ingestion & storage pipelines...
0%
Want to turn your raw data into a reliable foundation for AI?
Book a free 30-min call — our data engineers will review your gaps and outline a clean data roadmap.
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Gain a competitive edge. Say goodbye to costly errors.

Gain a competitive edge with DevPals' Data Intelligence and say goodbye to costly errors in databases.

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Reviewed by a senior engineer.