Curva Fin Bloque
POST OCTOBER 27, 2014

In today’s complex environment where external conditions change rapidly, relevant and timely information is essential to decide in any business. Information becomes a strategic resource that must be smart managed. Therefore, organisations need to be able to transform data from legacy and open sources into deeper insights and strategies about their business and to better cope with the uncertainty[1] of the future. In that context, most businesses are familiar with the relationship between risk and reward but in assessing potential opportunities and developing business plans rarely acknowledge risks and probability in a formal way. external conditions change rapidly, relevant and timely information is an essential requirement for making any business decision. Information has become a strategic resource that must be managed intelligently to utilize its full potential. Organisations need to be able to transform data from legacy and open sources into formats that reveal deeper insights and strategies about their businesses, as well as to better cope with the future. 

“Information costs money… Intelligence makes money”

What are the main problems?

A lot of organisation is convinced about the need of BI tools to better understand their environment to better decide.

The second problem, more important but less identified by the end users, is the trust on the data sources and imperfect information management. There are too few Intelligence systems presenting the confidence level to the user that he/she can have into the data presented and/or handling the imperfect information issues (incomplete, uncertain, inconsistent) to leverage information management. These problems raise a very important issue because the decision makers risk taking a decision on the basis of poor information quality and not trustable knowledge. Sometimes, language barriers represent another hurdle adding to poor or lack of knowledge.

However, despite the unprecedented availability of information, bad decisions are still being made in both the public and private sectors. It is not enough to provide access to voluminous information and expect good decisions to be made as a result. Numerous social, cultural and educational factors influence how well individuals and organizations are able to improve their decision-making ability. Clearly, modern information systems have had a significant impact, but decision-making involves more and more, a community of stakeholders who each bring their own data, their own interests and their own values to the problem, and it is crucial that these be taken into account.

 

Since 2013, a new item in the Hype Cycle is the term Prescriptive Analytics. This is an expression used to define systems that make specific recommendations for key decisions by analysing vast amounts of data. It is the next step in analytics, which began as descriptive, followed by predictive. Prescriptive analytics not only foresees what will happen and when it will happen, but also why it will happen. In a global village, data and analytics come as multilingual data. In addition, it provides suggestions regarding decision options for future opportunities or risks. It is based on a mathematical discipline called operations research (OR), which works in conjunction with business and domain rules frameworks to support also impact assessment of each of the options. Prescriptive analytics is able to continually add and update data to improve its predictions. Gartner predicts that it will take another five to ten years before prescriptive analytics will hit mainstream.

[1] D. Hubbard (Hubbard, 2007) defines uncertainty as: A state of having limited knowledge where it is impossible to exactly describe existing state or future outcome, more than one possible outcome.