Sampling Data using Smarten Augmented Analytics!

By a small sample, we may judge the whole pic!

All of us might have used the concept of sampling in our routine life. For instance, while purchasing vegetables from a shop market, don’t we examine a few to assess the quality of the whole lot? Doesn’t a doctor examine a few drops of blood as a sample in order to draw conclusions about the blood constitution of the entire body? Most of the time while dealing with big data problems, it’s not feasible to collect data from the whole population. Thus, sampling techniques are a useful procedure for selecting a subgroup (i.e., sample) from a population that is expected to be a representative of the whole population, in turn saving the time, cost as well as the efforts needed in examining the complete data. If anything goes wrong with the sample of data, then it will be directly reflected in the final result.

Machine Maintenance Using Smarten Assisted Predictive Modelling!

1.  Machine Maintenance is always cheaper then downtime!

Sooner or later, all machines run to fail and monitoring the condition of the machine is crucial for any enterprise as any unplanned downtime can have greater economic impact resulting in reduced productivity and ultimately losing the customers. That being so, a regular way to keep the machine in a good condition is to timely monitor it and detect the patterns to predict the breakdowns. Predictive maintenance of machines helps in evaluating the timely collected machine data in order to gage its condition and predict when maintenance work is needed. 

Handling Outliers Using Smarten Assisted Predictive Modelling!

1.  Outlier, an Outsider!    

Outliers, also referred to as anomaly, exception, irregularity, deviation, oddity, arise in data analysis when the data records differ dramatically from the other observations. In layman’s terms, an outlier can be interpreted as any value that is numerically far-flung from most of the data points in a sample of data.