The document discusses some key shortcomings of big data modeling, including models being based on unrealistic assumptions, identifying correlations between random variables as significant when they are just noise, and ignoring important variables. It notes that when evaluating an employee's earnings performance, a model may ignore their role as a manager. A manager should be aware that big data models may rely on impractical assumptions, identify spurious correlations, and omit important variables when making projections. They must consider an employee's leadership role and past performance for accurate analysis of their future earning capacity.

2. The article by Daniel Goleman clearly states the
problems with the Big Data which are generally
ignored. These can be due to algorithms being
based on unrealistic assumptions. There also
exists chances of using random variables which
seemingly may have correlations that look
significant but is nothing more than noise.
Moreover, the models may ignore key variables
essential for unbiased modeling.

3. Example for the computing an executive’s
earnings performance, the employer may
completely ignore his role as a boss. These models
are based loosely on past performance of the
employee to predict his future performance but in
each scenario, this may or may not be ideal for
projections.

4. A manager must know the shortcomings with Big
Data, i.e. firstly, it may be based on impractical
assumptions which may not be relevant to the
model; secondly, the significance of variables
which may have high correlation but in actual
sense is nothing more than noise and lastly,
ignoring the important variables in a model.

5. For the case of judging an employee’s
performance, the manager must take into account
his role as a leader and his impact on the morale,
loyalty, focus, and stress levels of his direct
reports for utmost accurate analysis. Not only this
a manager must know the employee and his
previous performances to evaluate an employee’s
future earning capacity.