According to authors, modern-day marvels are the result of machine learning, which are programs that combine through millions of pieces of data and start making correlations and predictions about the world , by using machines that uses cold hard data to make decisions that are sometimes  more accurate than a human’s, thereby reducing biases. Computers don’t hold any inherent biases, as machine  knows only one approach i.e. the objective analysis and are capable of analyzing massive amounts of information, thus having  a distinct advantage over humans. Utilizing the data for machine learning, can uncover contradictory and surprising results by making data more accessible, more understandable and  enabling the organization to achieve a new level of business intelligence, thus empowering decision makers at all levels with a powerful tool. Read more at https://aitrends.com/machine-learning/machine-learning-cure-decision-bias/

Knowledge gap is the disparity between what we know and what we need to know. Most of the information is imperfect in the real business world. The question that arises when leaders make decisions is the cost and its valuation. It is time consuming to know whether the decision is good and valuable. Organizing an internal effort to determine what reservoirs of knowledge exist inside the company and sometimes it exist in unexpected places, channeling people and published market studies are often good for the market. In case of an outside research partner, they must be open on methodology, listen to you and must be on the same platform on the basis of the timing and level of precision. It may be happen that you are in a stage where nothing more than validation is required to move to the next step. Common sense is most important to bridge the gap and move toward perfect knowledge which is both acceptable and appropriate. Read more at: 

Before choosing a Professional Employer Organization (PEO), it is very important to know what we want from a PEO. PEOs vary widely, in their size, focuses, and concentrations.  A PEO that works fine for one company may not be suitable for another.

Read more about what to consider when choosing the right PEO, at http://www.nextep.com/peo-goals/