Inspirient industrializes the key capability of the digital economy – we use Artificial Intelligence to automate business analytics, end-to-end, from raw data to presentation-ready management slides. Data no longer needs to be pre-processed, aggregated and visualised by analysts using a variety of tools and technologies.
What is the problem you are solving?
The promise of Digitization is that data can drive economic growth and enable new business models across industries. Yet, at the current stage of development, a lot of time is spent manually executing data analysis before it’s possible to think about business implications.
Besides being costly, manual analytics bears risks: Humans are often biased, especially if the same KPIs are used over and over again. Information outside of these KPIs might not receive adequate attention.
Furthermore, analyses are mostly executed by (junior) analysts. These employees have very good technical skills, however they structurally lack the experience and business comprehension of the senior management. This leads to oversights or misprioritizations in the pre-selection of insights presented to senior management.
As a result, companies miss out on valuable business opportunities on a daily basis that could otherwise put them ahead of their competition.
What is unique about your solution?
Inspirent exceeds current solutions on the market in two critical ways:
Our technology fuses comprehensive probabilistic analysis methods with business common sense, and thus is the first approach to enable end-to-end automation of business analytics. Data no longer need to be pre-processed, aggregated and visualized by analysts using a variety of tools. Instead, decision-makers can now immediately review relevant insights and directly weigh potential business implications and decide on follow-on actions.
Inspirient follows an approach that stands in contrast to traditional hypothesis-driven data analytics, as part of which only a few previously chosen hypotheses are examined. Inspirient does not require predefined hypotheses, instead all possible insights from a dataset are evaluated and cross-checked. The user is then enabled to scan and select the results relevant to him. This enables discoveries beyond standard KPIs and guides users to the critical, new questions to ask about their business.