To simply explain something and yet keep the very essence of it is an exemplary way to defining something. And, coming to the technical part of the business world, big data seems complicated but defines the parameters very clearly and that is the exquisiteness of the analytics. Data Science identifies the variable and defines the metric which in return predict the future and analyze the situations beforehand.

The interpretation helps the data science and the stakeholder to keep a tab and aim for better performance. A stakeholder identifies the variables and metrics which might have the capacity to be better predictors of performance and as for data scientist’s they take these variables and metrics to quantify them and identify which one can actually perform better. Stakeholders understand business, while, data scientist know data transformation, data enrichment, data exploration and analytic modeling well, which together combines for the best data science certifications team.

As a part of the data science team, one must continuously work on improvement and adopt the method of fail fast / learn faster. However, the fact cannot be ignored that it is a mutual effort from both the stalk-holders and data scientist which yields the best result. They collaborate to create a perfect attrition, without the stakeholder’s creative will to interpret metrics and variables, the data science team cannot quantify them and put them to good use, or vice versa. They are inter-dependent on each other. A data scientist utilizes various tools to jump start their analytic process by identify correlations in the quality variables.

The real work begins after the interpretation and visualization of the data. Herein, the various analytic tools are used to correlate variable and build a constructive analytic model. Furthermore, the data science team explore many other algorithms and analytic techniques to create the most predictive model. In a later stage, the produced predictive model is evaluated to ascertain the goodness of the model and how conveniently it suits the set of observation. But prior to all these step, before the data science team can start working on the number game, it is the stakeholder who gather the numbers and data on which the team works on. The identification of predication starts in collaboration with business subject matter expert. A data science team understand the key sources of business differentiation and based on that they construct their hypothesis and predictions.

Concluding one can easily say that the success of the predictive model and the interpretation of variables which can perform better has a lot to do with close liaison of a data science expert and a stake-holder cum business subject matter expert.