The shift in mechanisms for objectivity
INNORBIS applies metrological technics in its integrated assessment model, providing quality-assured metrics capable of supporting certified financial market sustainability definitions. The root metaphor in play is shifting from a reductionist, top-down clockwork universe to an emergent, bottom-up living ecosystem. Instead of wholes being nothing but the sum of the parts, we are acknowledging the world to be a much more complex set of wholes that are all each greater than the sums of their parts. But this shift does not remove the need for science. If anything, it makes clear and shared criteria for obtaining scientific results even more important.
Objectivity - based in results repeatedly reproducible within limits of precision
Measurability - consistent measurement, qualitative & quantitative
Completeness - including all relevant factors for decision making
Conformity Assessment provides a language, evidence, and legal proof for:
explicit confidence - requirements for products and services are met
public interest, public comparability
safety & order
protection of environment and consumer
Requirements for measurement include reporting precision and uncertainty in data, ensuring that product requirements are fulfilled.
INNORBIS uses a cloud-native Usage Data Platform (UDP) to automate the process of collecting data from an enormous amount of sources and formats. These input data are then evaluated, aggregated and processed into one powerful data set that we bind to one identity (company or country) for advanced forward looking analysis and comparison.
Going beyond the data access , INNORBIS simplifies measurement and connects its results with significantly more key performance indicators (KPIs) than the competition. Higher quality results like these deliver objective sustainability performance measures that are informative and useful no matter the size of business. The result is that we can can provide this superior business analysis as a service based on usage.
A fair amount of company data and sustainability information is available only as extended text responses to questions, or as company reports written by humans in natural language. These are difficult data sources to tap via automated processing. Ahead of the market, we are on seeking long-term solutions derived from training a neural network to parse and convert such statements into well-defined rating categories that can be used downstream. Past that, we are also involved in advanced theory-based methods that do not need data-based training, but can accurately evaluate the meaning of written text based on the complexity of the concepts.
This work is in close cooperation with experts in machine learning and model-based reasoning, unlocking sustainable and impact insights with the transparency of the patterns analysed, at the same time as drilling down to the specific reasons for better engagement. No need for professionals to collect the data and spend time on "copy paste" analysis. The patterns revealed for decision making.
”We are what we measure. It is time we measure what we want to be”
- William Fisher