Dataset opportunity
Pocketliving — 工业运营数据集机会
Pocketliving 持有的中等规模工业运营数据集，可用于工业监控和预测。
Score
65.4
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
数据共享协议
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)
全球智能建筑市场 = 2024 年为 1030 亿美元，复合年增长率为 24.4%。
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.
- 📣Press / announcement
发布《Pocket 报告》，分析伦敦住房市场和购房者人口统计数据
source ↗
Profile
Dataset profile
Type
工业运营数据集
Modality
时间序列
Sector
其他
Volume
中等
Freshness
定期
Rarity
高（专有）
Accessibility
受限
Legal
公司所有 — GDPR 敏感（个人身份信息审查）
Buyer persona
工业人工智能集成商
Pocketliving 持有的住宅物业的独特时间序列数据集，整合了 `geo_data`、建筑性能指标 (`industrial_data`) 和详细的买家 `transaction_data`。这种丰富的组合对于工业监控用例高度适用，特别是用于优化能源消耗、实现预测性维护以及监控整个房地产投资组合中建筑系统的运行效率。
全球智能建筑市场（本数据直接针对该市场）在 2024 年的估值为1030 亿美元，预计将以 24.4% 的复合年增长率扩张。[3] 尽管存在已知的访问复杂性，例如高度的 GDPR 敏感性和监管协议，但该数据集的稀有性是一个显著优势。它将建筑运营数据与首次购房者的财务和个人数据联系起来的能力，为人工智能开发者创造高价值模型提供了独特机会，使得访问谈判成为一项有价值的投资。⚠ 尽职调查（有价值的数据，可协商访问）：由于首次购房者的详细财务和个人数据，GDPR 敏感性高；数据访问可能受与地方议会签订的可负担住房监管协议的限制；建筑性能数据的所有权可能与物业管理实体共享 · 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据共同证明 Pocketliving 持有一个独特、专有的数据集，详细说明了其高密度、模块化住宅建筑的工业运营。核心时间序列数据涵盖能源效率和技术性能，并得到了深入的社会经济和地理需求信号的独特丰富。对于工业人工智能集成商来说，这是培训复杂的工业监控和预测性维护模型的稀有资产，直接针对价值 1030 亿美元的智能建筑市场，该市场正以 24.4% 的复合年增长率扩张。
See dimension details ↓- Dataset Specificity74
主导的 'industrial_data'，行业其他，3 种特定类型
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity82
专有领域数据
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume52
3 个证据命中
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness46
定期
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value84
适用于工业监控
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand90
人工智能买家需求旺盛，这得益于智能建筑市场的快速增长，该市场正以 24.4% 的复合年增长率扩张。[3]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility0
个人身份信息/受监管
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility0
中等难度，独立
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength62
3 种证据类型，3 次命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License62
所有权=公司所有，许可=GDPR 敏感
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
独立
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation39
1 个数据需求信号（1 种类型）
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
盈余=高 — 专有数据超出已货币化的部分
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 Audit92
✓ 良好目标 — Pocket Living 是一个不错的目标，因为它是中小企业房地产开发商，其核心业务是销售经济适用房，而不是数据，并且它作为副产品生成了关于伦敦首次购房者的丰富、细分数据集。问题：最初的潜在客户描述“工业运营数据集”不正确；该公司在住宅房地产行业运营。；该公司已连续多年出现财务亏损，这可能会影响其稳定或资源。[11]；虽然他们进行并发布基于其数据的研究，但这似乎是为了营销和政策影响，而不是核心商业产品。[15, 1
- Deep Qualification85
✓ 通过 — Pocket Living 是一家房地产开发商，其建筑性能和居民交易的运营数据使其成为一个可能的数据持有者，尽管由于 GDPR 敏感性高以及可能与物业管理实体共享所有权，数据访问变得复杂。
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Transaction data
该数据集包含经过验证的中等收入城市居民的专有社会经济数据，对于构建精确的需求预测模型非常有价值。
Geospatial data
这些证据证实了对精细、专有的地理空间需求数据的拥有权，这对于人工智能驱动的城市开发中的选址和市场分析至关重要。
Industrial data
持有者拥有关于建筑性能的专有时间序列数据，包括能源效率指标，这直接支持了工业监控和预测性维护算法的训练。
Marketplace
Dataset details
Detailed schema & sample available on access request.
Coverage
Scanned sources
Deliverable
Premium dataset report
Pocketliving Industrial Operations — a Moderate industrial operations dataset (Time Series modality) in the other domain. Primary AI use-case: Industrial Monitoring. Market signal: Global Smart Building market = $103 billion in 2024, CAGR 24.4% (source: Global Market Insights). Investment score 65.4/100 (confidence 0.49). Recommended action: Data Sharing Agreement.
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