These are fictitious examples created to illustrate the potential applications of the new technology and associated services. They do not reflect confidential details or strategies of companies.
Correlating Data Cost to Sales Performance
It is hard to tie data expenses to the bottom line.
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ORBintel Impact: Data profitability shifts to an auditable truth. Data Engineering and Data Science Teams shift from a Cost Center to a Profit Center, now that Executive teams can tie metadta to tie Data Cost to Sales Qualified Leads, to determine when in-house and purchased data directly impacts the bottom line.
Optimizing Manufacturing Operations
Unplanned equipment downtime in auto parts manufacturing disrupts production schedules, leading to costly delays and missed delivery deadlines. Additionally, unexpected machinery failures increase maintenance costs and result in inconsistent product quality, damaging customer trust and profitability.
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ORBintel Impact: Metadata used to optimize predictive maintenance of production equipment by analyzing real-time sensor data and historical performance metrics; to identify early signs of failure, schedule timely maintenance, and reduce unplanned downtime. Accurately costing the data quantifies financial impact of savings achieved through predictive maintenance; this helps prioritize data-driven interventions, justifies investments in IoT sensors and analytics tools, and optimizes resources to maximize productivity. When tied to Sales & Marketing Data, manufacturers can improve responsiveness, and this, in turn can reduce lead-times for assembly-line changeovers.
Optimizing AI 'Large Language Model' (LLM) Builds
A model builder cannot calculate true build cost and suffers hidden losses. An insurance audit reveals that development costs ranged up from $100 Million (1) and yet failed to deliver accurate information; exposing the vendor to denial of coverage. Note: There are known cases of accuracy failure, such as a 2024 Stanford study reporting issues with use by lawyers, with the study reporing: "legal hallucinations are pervasive and disturbing: hallucination rates range from 69% to 88% in response to specific legal queries for state-of-the-art language models."(2)
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​ORBintel Impact: Vendor improves build quality and is able to justify their claim to return to coverage; demonstrating that they have developed capacity to mitigate risk.
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(1) https://www.wired.com/story/openai-ceo-sam-altman-the-age-of-giant-ai-models-is-already-over/ & https://www.entrepreneur.com/business-news/anthropic-ceo-ai-will-cost-10-billion-to-train-by-2025/476750
(2) https://hai.stanford.edu/news/hallucinating-law-legal-mistakes-large-language-models-are-pervasive
Data Assets - Mitigating Cyberattack Risk
A multinational discloses victimization from a serious cyberattack. Analysis suggests that the attack produced at least $38 billion in damages and there were audits, fines and hits to the company's reputation. 33% of the data consisted 218.4 million obsolete personal customer records.
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ORBintel impact: Imagine being able to make a cost/benefit decision to move the records to physical servers? 33% of records could have been isolated in off-cloud (on-prem long-term) storage at an estimated cost of $80,500/year; or deleted if there was no requirement to retain the records.​
eCommerce Market - Value-driven Triage of Paper Data Inventory
A regional retailer has 20 years of customer preferences locked into paper records. The company wants to digitize paper data for decision-making purposes. The company uses optical character recognition (OCR) to scan the data into datasets; but is unable to accurately determine what data to use, retain for further purposes, use to claim sustainability tax credits, or completely eliminate from server inventory.​
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ORBintel Impact: Collections are sampled to determine capacity to meet defined Return-on-Investment targets that must exist to justify distillation to a new dataset. Unprofitable inventory is removed which cuts away numerous costs including opportunity cost, Capex, Opex, hubris risk, and insurance fees. Using OCR, the final selection is transferred to electronic format for data analysis purposes.
EU Artificial Intelligence Act - Cutting Compliance Cost
AI modeling is hugely costly as the majority of models fail to scale to production. The ones that work are infected with bias, which creates the potential for harm to all use cases. The EU’s proposed Artificial Intelligence Act (AIA) aims to mitigate this risk. However, total compliance costs are forecast to be US$417,000 (€400,000) for each high-risk AI product.(1)
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ORBintel Impact: Using ORBintel metadata to create the needed metrics; to improve performance to mitigate bias risk; the company cuts the cost to achieve compliance and this gets better every year.
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(1) https://itif.org/publications/2021/07/26/how-much-will-artificial-intelligence-act-cost-europe/
Corporate Operations Analysis
On its own, Data is hard to manage. Financiers and investors need better insights with which to analyze the operations of a target investment, and that's hard to achieve because intangible assets cannot be used to report their own utility.
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ORBintel Impact: The transformations' process create the means to solve the deep problem of creating a unified financial language for Life Cycle Analysis, Cost Accounting, and Finance.(1) Investors obtain the means to tie data flows to operating decisions. Organizations concurrently obtain the means​​ to improve bricks & mortar auditing; one of the disliked, much avoided tasks of inventory control.
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(1) Designing for Comparability: a foundational principle of analysis missing in carbon reporting systems; Jimmy Jia, Abrar Chaudhury, Nicola Ranger, 18 July 2023, Oxford Smith School of Enterprise and the Environment | Working Paper No. 23-04. ISSN 2732-4214 (Online): https://www.smithschool.ox.ac.uk/sites/default/files/2023-07/WP_No._23-04_Comparability.pdf
Scope 3 Emissions Reporting Market - No Stranded Assets - Profitable Sustainability
In October 2022, the COP26 Glasgow Financial Alliance for Net Zero (GFANZ) pivoted from requiring signatories to encouraging signatories to commit to the campaign. Mark Carney, the United Nations’ special envoy on climate action and finance, reportedly claimed that 'getting more data' will be the key to holding banks accountable for climate progress. The problem here is that data on its own is useless. Financiers and investors need better insights, and that's hard to achieve with intangible assets.(1)
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ORBintel Impact: Data becomes operationally cashable. Investors use the method to tie emissions goals to operating results. O&G operators and "First-adopter" investors notice opportunities to de-strand assets; and pressure Climate Treaty finance signatories to use the new measures to improve release of 'transition finance' funds, estimated to be $4.5 trillion per year.(2)
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(1) (2022) https://trellis.net/article/greenfin-interview-open-source-design-meets-climate-finance-data/
(2) (2023) COP28, Dubai: 07:01 min: https://www.youtube.com/watch?v=eWd5QsZf9oE