Data, the underrated and overlooked strategic asset
Author
Débora
Published
Data: Something anyone can understand
The digital raw material
Imagine your data as the digital DNA of your organization: information that describes your business, your machines or products, your customers, etc. Everything that makes you… you.
Data can take very different forms:
- Structured data: It’s neatly stored and organized in the right drawers/in the right boxes. Easy to sort, find, and analyze.
- Semi-structured: clearly named but still scattered — like sci-fi novels, autobiographies, and textbooks piled haphazardly: they’re labeled, but don’t have a clearly assigned place.
- Unstructured data: text, photos, videos, team conversations. They often make up the largest share of the information stock (some say up to 80%).
- Streaming data: telemetry, IoT sensors, logs arriving in near real time.
Where can we find it?
Don’t confuse a database (the filing cabinet) with a datacenter (the ultra-air-conditioned archive room). A database is software that stores your info; the datacenter is the building, servers, and cables where everything lives and breathes. And yes, your data is indeed “hosted” in datacenters.
Yes, the "cloud" really sits on solid ground!
Data within your company
Data, everywhere and in every line of work
Whether we’re talking about machines, customers, patients, or logistics flows, every sector lives and breathes through its data. It takes different shapes but always plays the same role: describing, measuring, and informing decisions. Data is strategic in every sector. With such diverse data, you can imagine endless applications for artificial intelligence!
Industry
Real-time sensor data (temperature, vibration, current, pressure, etc.).
Healthcare
Medical imaging data, such as scans (AI models are currently the only technology capable of analyzing complex images).
Retail
History of everything moving in and out of stock, online customer journey tracking, A/B test results.
Civil engineering
Monitoring sensor data (accelerometers, strain gauges, etc.).
Business services
Accounting data, legal texts, etc.
Energy
Weather data, congestion history.
Logistics & transport
Planning data, GPS coordinates.
Reliable data: a strategic advantage.
Unreliable data: a trap.
An AI system, even supercharged with the best algorithms, is like a race car fueled with plain water: without quality fuel, it sputters!
- Poorly maintained base: empty fields, duplicates, inconsistent formats.
- Incomplete base: sampling bias, outdated or missing data.
Consequences :
- Flawed models: inaccurate predictions, automation that misses the mark.
- Slow decision-making: time wasted cleaning, recross-checking, and verifying.
- Lower trust: if your teams doubt the dashboards, the AI project will end up on the shelf.
In short, data reliability is the silent multiplier of your ROI.
Our tips and best practices for quality data
1. Start small, dream big
Start with a critical dataset, prove the value, then expand.
2. Clear governance
Roles, responsibilities, and identified data owners.
3. Continuous quality
Automate format checks, anomaly detection, and deduplication.
4. Living documentation
Business glossary + cataloging to know who uses what.
5. Security & compliance
Encryption, access management, GDPR by design.
6. Data culture
Train your teams: data is everyone’s business, not just the CIO.
Here is our most sincere advice
Don’t underestimate your data,
cherrish them before you start any AI project,
give them the quality and reliability they deserve…
…it surely is worth the investment!