More than half of all strategic decisions made by directors turn out to be wrong according to new research, conducted at Nyenrode Business University.
The main reasons of decision making failures are due to:
Lack of reliable informations
Bias which often causes directors (and managers) to make important decisions that are not based on reason but ‘gut’.
Where ever you decide to enter a new market, to develop a new SaaS Business Model, or to invest into datacenter infrastructures to support your IT development, decision making requires more thought than your average decision – a director can’t simply rely on automatic actions, speed, intuition or peer advices.
"Each year over 30,000 new products are launched but 80% of these fail as a result of poor decision making”, said professor Clayton Christensen, Harvard Business School.
For decision makers operating in Cloud, IoT and new IT markets, there are indeed plenty of new key challenges that may have huge impact on their decision making processes, thus including :
The increase of regulations around data privacy, ICT security policies to which they have to comply with (NIS, GDPR, SMSI ISO 27001)
A lack of comparability, reliability and visibility for a wide spectrum of Cloud and IoT providers and related businesses
A need to mitigate risks and to secure investments when operating in Cloud, IoT, and datacenters
Businesses need to ensure that their directors are making fully-informed decisions. They need to have decision governance structures and responsibilities in place which effects how decisions are made and that monitor the decision-making process.”
Decision making based on automatic learning and self adjustment
B2Cloud has developed a new B2B recommendation method that appears to be very innovative to existing recommendation engines and decision making process, maximizing the trustworthiness and the transparency of recommendations over the reactivity and insuring a good resilience against corrupted, incomplete and/or biased data.
Our Dynamic Recsys has been designed as a self adjusted system that is able to make continuous learning and monitoring of the weight of sources used for the recommendation (expert’s knowledges, business rules, performance indicators, user's interactions), to improve the reliability and the robustness of the recommendation process.
As a pertinent decision making process cannot be based on time costraints, our system is fully adapted to the evolution of the user’ profile and to its decision making cycle through a time session that can be whether decided by the user or suggested by the system.
For these reasons, our Dynamic Recsys can be customized to any kind of business facing with security, reliability and trustworthy