Use Case Concepts
Alternative Data Sales
Alternative Data vendors cannot calculate the true cost to build a dataset to supply a customer 24/7. The vendor cannot accurately price the dataset to sell at a profit. They suffer hidden losses.
Impact: Vendor determines profitable price-points and is able to justify this to the customer; demonstrating that they have staying power because they no longer have to operate at a hidden loss.
Mitigating risk of loss of servers
A corporation’s assets are 90% intangibles. Data is one asset class and other classes are tied to the value of data-informed decisions. The company identifies a dangerous risk: natural disaster damage to the servers owned by the vendor who stores their intangible assets.
Impact: The corporation uses our metrics and triage to cut the volume of dataset records. A natural disaster does occur, but there is significantly less harm since the assets have been reduced in volume.
Mars Geological Orbiter AI mitigates data packet risk of loss
A mining consortium is designing a geological scanning satellite for a 5-year mission to the Red Planet. Investors have expressed concern about the ability to accurately capture data.
Impact: The consortium works with our systems team to onboard an AI-driven modification to the ORBintel© core package. The AI rapidly triages raw data to minimize the bandwidth needed to transmit data to Earth. This mitigates risk by freeing up processingresources.
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, dispose to an aftermarket, use to claim sustainability tax credits, or completely eliminate from server inventory.
Impact: The paper collections are sampled to determine whether they will meet the defined return on investment targets that must exist to justify distilling the collection down to a new dataset. Unprofitable inventory is removed which cuts away numerous costs including opportunity cost, management cost, capex, opex, and insurance fees. The final choices are OCR'd for data analysis purposes.
UK discovers all-of-society competitive advantage
The UK’s competitive edge is being strangled by Data Science skills shortages. 10,000 new data scientists graduate every year, but this is not enough. Moreover, new graduates need at least one year of experience in a business to be profitable to the private sector. The shortages are costing British businesses more than £2bn a year. In 2020, HM Government created a £24m fund and scholarships. This is still not enough. Numerous companies cannot get the skilled staff nor can they afford to hire this highly competitive skill set. Harnham further reports demand for the "commercially-minded" data scientist who is also in short supply.
Impact: A British haulage company needs to improve its competitive edge. It uses ORBintel© metadata to profitably improve processes, productivity, competitiveness and team satisfaction. After seeing the benefit of immediate use of our SaaS solution, the company cuts 28% of cleaning costs by using an Education and Skills Funding Agency restructuring grant to create new skills' specializations—growing the team without needing to hire another hard-to-obtain PhD scientist.
Mitigating cost of Artificial Intelligence Act (EU) compliance
AI modeling is hugely costly as the majority of models fail to scale to production-ready status. The ones that work are infected with bias, which affects everything–from products offered at sale; to creditworthiness; to access to State-funded Legal Aid. The EU’s proposed Artificial Intelligence Act (AIA) is designed to mitigate this risk. However, total compliance costs are forecast to be up to €400,000 for each high-risk AI product.
Impact: A company defines ’worth’ metrics to establish continuous "KPI removal of racial bias" as the desired primary objective. Using ORBintel© metadata to create the needed metrics, the company achieves significant reduction in racial bias; and this cuts model failure. This cuts compliance cost per use case.
Documenting inventory losses for insurance claims
The aircraft leasing industry is struggling to weather the loss of aircraft seized by Russia and Belarus after the invasion of Ukraine. The loss is tied to the physical asset (not data) because the current methods of Finance & Accounting is to say that data is an intangible asset. It is hard to value. Valuations are done by teams of expert consultants, accountants and counsel.
Impact: The assets have been seized through force majeure actions of sovereign states. The proposition here is that the raw data (avionics systems' data + consumer data) gathered by aircraft has current cash value in-and-off-itself. The value of the data can be separated from the physical asset. The lease value of aircraft can be tied to the raw data and datasets; representing the shape, location and functioning of the physical asset and its occupants; such that the data forms proof of evidence of the value of the lost assets. This may be analogous to American Airlines' collateralisation of the value of Frequent Flier Miles; where loyalty has been redefined as a collateralizable inventory asset.