A- Introduction – Alchemy of Analytics
We all understand what is Big Data which is typically defined by three Vs, Volume, Variety, and Velocity, where Volume represents hugeness of data, Variety states that multifariousness of data in form of audio, video, text, signals and Velocity represents rapidity with which data swells.
Analytics is nothing but an amalgamation of machine learning and statistical modeling. Big data analytics helps in reviewing large and complex datasets for uncovering hidden patterns, unknown correlations, trends and various other user’s useful information. Although it is all true, it is still not a magic which can produce something out of nothing.
B- How Big Data processing works
Data scientists have numerous tools for pulling data from a large set of big data. Various algorithms are written for including several inputs which are identified as necessary to answer the question. Despite the tools being provided, mistakes are inevitable. There is always a scope of error but statistics and data science have fixed many of these issues long before big data came to be. Also if wrong data points are captured in the algorithm or some flaw occurs in data in some way the algorithm output will be flawed.
Using various statistical analysis tools data scientists can find non-obvious patterns in deep data. Big data intensifies the problem as the whole galaxy is full of fictitious correlations. We have seen that variable interactions which change the significance sign and curvature of an important predictor. Hence, if you are looking for a particular correlation in your data you must be clever enough to combine the right data, right variables and analyze the right algorithm. The fact is when you have now discovered this correlation does not mean that it actually exists in the real-world domain. It can be just a fiction of your specific approach to modeling the data which you have at present in hand. Sometimes even the most brilliant statistical analysts can make honest mistake of math and logic.
Even if you take pride of being great data scientist there are chances of
How can data scientists reduce the likelihood that, when exploring big data, they might inadvertently accept spurious statistical correlations as facts? Here are some useful methodologies in that regard:
- Ensemble learning: This approach validates that multiple independent models are using same data set. Such approach ensures that multiple independent models which are using same data set are trained on different samples whereby employing different algorithms and calling out various variables, therefore converging on the common statistical pattern. This will offer you trust that the correlations which they reveal have casual validity. Ensemble-learning algorithm does this by training on result sets of independent models. This happens by the medium of voting, bagging, boosting, averaging and various other functions which reduce variance.
- A/B testing: With this approach users determine alternatives between the models, which is between which some variables differ but others are held constant. Predict the dependent variable of interest. The real world experiments involve live data successfully running of incrementally revised A and B models which are known as ‘champion’ and ‘challenger’ models and they coincide with a set of variables having highest predictive value.
- Robust modeling: With this modeling technique you can determine whether the predictions which you have made are constant in respect to the alternate data sources, algorithmic approaches, sampling techniques timeframes and so on. This approach simply involves due diligence in the modeling process to determine whether the predictions you’ve made are stable with respect to alternate data sources, sampling techniques, algorithmic approaches, timeframes, and so on. Furthermore, a robust and scalable outlier analysis is imperative as it has been seen that with an increase in the incidence of outliers in large data sets can create fallacious correlations. This can keep in true patterns in data in dubious state or may reveal patterns which do not exist.
When you will apply these approaches and techniques constantly in your work, they ensure that patterns which you are revealing reflect the domain you are modeling. When applied consistently in your work as a data scientist, these approaches can ensure that the patterns you’re revealing actually reflect the domain you’re modeling. Without these in your methodological kit, you can’t be confident that the correlations you’re seeing won’t vanish the next time you run your statistical model.
Article Resources: Infoworld, Marketingland, Domo, Techcrunch