Dataset opportunity
Makotsl — Transaction Dataset Opportunity
Moderate transaction dataset held by Makotsl, usable for Recommendation Models and Fraud Detection.
Score
61.4
Score (0–100) blends weighted dimensions — dataset rarity, training value, buyer demand, evidence strength and right-to-license. 70+ is deal-ready. See the scored dimensions below for the breakdown.Confidence
49%
Action
Data Sharing Agreement
The recommended deal structure for this dataset: Acquire (full buyout), License (paid usage rights), Data Sharing Agreement (controlled access, no transfer of ownership), Partnership (co-development) or Annotation Program (labeling). Chosen from data ownership, licensing complexity and accessibility.Market size (indicative estimate)
Global Transportation Analytics Market = $12.6 billion in 2024, CAGR 23.8%.
Lineage
How this lead was derived
The signal-first chain, end to end: recent external signals → qualified niche → resolved data-holder → site verification → scored opportunity. Every lead is explainable.
Concrete evidence this company actively cares about data — why it's ripe for the deal room.
- ✨Signal
Reported 195,803 executed transport relations in 2025
source ↗
Profile
Dataset profile
Type
Transaction Dataset
Modality
Tabular
Sector
mobility
Volume
Moderate
Freshness
Periodic
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Mixed ownership — GDPR-sensitive (PII review)
Buyer persona
E-commerce & personalization AI teams
Makotsl provides a tabular Transaction Dataset derived from its mobility operations, integrating business records, geo_data, and transactional information from over 1,000 contracted carrier vehicles. This rich combination of real-world operational data is structured for building and training advanced Recommendation Models, enabling optimizations in client-carrier matching, dynamic pricing, and route efficiency.
The business value of this data is highlighted by the Transportation Analytics Market, which was valued at $12.6 billion in 2024 and is projected to grow at a 23.8% CAGR. [1] Despite access complexities such as shared data ownership and the need to anonymize PII from e-CMR documents, the dataset's rarity and high commercial sensitivity make it a prized asset for AI buyers aiming to gain a significant competitive advantage in logistics. ⚠ Diligence (valuable data, access to negotiate): Data ownership is shared with contracted carriers (1000+ vehicles not fully owned); e-CMR documents contain PII (names, signatures, addresses) requiring anonymization; High commercial sensitivity regarding route pricing and client-carrier matching · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves Makotsl's ownership of a large-scale, proprietary transaction dataset detailing over 195,000 logistics routes across Western Europe. For e-commerce and personalization AI teams, this data is a rare asset for training advanced recommendation models and optimizing complex supply chains. In a transportation analytics market projected to grow at nearly 24% annually, this granular record of real-world mobility patterns provides a distinct competitive advantage for understanding and predicting logistics demand.
See dimension details ↓- Data Orientation39
1 data-appetite signals (1 types)
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dataset Specificity78
dominant 'transaction_data', sector mobility, 2 specific types
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity70
proprietary domain data
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume52
3 evidence hits
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness46
periodic
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value74
fit for Recommendation Models
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand90
AI buyer demand is exceptionally high, driven by the rapid growth of the Transportation Analytics Market, which is expanding at a 23.8% CAGR. [1]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility0
PII/regulated
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility0
medium difficulty, independent
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength62
3 evidence types, 3 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License28
ownership=mixed, licensing=gdpr_sensitive
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
independent
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Dormant Data Surplus92
surplus=high — proprietary data beyond what's already monetised
Volume and value of proprietary data this company holds BEYOND what it already monetises — the dormant surplus we can unlock. A company can sell some insights AND still sit on a far larger dormant asset. - ICP Audit100
✓ good target — Makotsl is a Polish logistics and freight forwarding SME that generates a significant volume of proprietary operational data (routes, vehicle telematics, transaction details) as a by-product of its core business, making it a perfect target that is not yet monetizing this data externally. [1, 2, 9]
- Deep Qualification90
✓ pass — Makotsl is a freight forwarding company whose operational activities in managing over 1,000 contracted vehicles generate a valuable transaction and telemetry dataset. However, data ownership is shared with carriers and the data contains sensitive PII from e-CMR documents, requiring careful anonymization before any potential use.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
business_records
This evidence indicates the company generates a high volume of logistics documents for each order across Europe, which are undergoing digitization and automation.
Transaction data
This tabular data quantifies the company's operational scale, detailing over 195,000 completed routes concentrated in Germany and Western Europe, providing a rich source for predictive modeling.
Geospatial data
This data specifies the composition of the physical fleet, which includes over 1,000 contracted vehicles and the largest van fleet in Poland, adding crucial asset-level detail for supply chain analysis.
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Makotsl Transaction — a Moderate transaction dataset (Tabular modality) in the mobility domain. Primary AI use-case: Recommendation Models. Market signal: Global Transportation Analytics Market = $12.6 billion in 2024, CAGR 23.8% (source: Grand View Research). [1]. Investment score 61.4/100 (confidence 0.49). Recommended action: Data Sharing Agreement.
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