The more data I add from different aspects of a problem, the full view I have. It can help create what is often referred to in the analytics world as a degree view.
Prime examples of this are in customer analytics: customer experience, customer behavior, customer retention. For instance, in each of these use cases, if I only have data from some channels but not all, I have blind spots that may keep me from getting the most accurate answers.
The more data added, the broader the purview to the problem, creating increased accuracy and trust in the results. Perhaps a dashboard showed metrics that varied greatly from the norm. So immediately, the business wants answers that explain why or how the situation is happening. A prime example of this comes up in marketing analytics.
A dashboard may show which marketing campaigns are performing better than others and which are performing poorly. Making adjustments is not as simple as continuing the good ones and shutting down the bad ones.
In this case, the business wants the detailed aspects of the campaigns analyzed to determine the best course of action. Are there aspects of the marketing channel that are making campaigns succeed or fail?
Demographic characteristics of the targets? Features of the offers? With these details, the business can make the proper adjustments and action plans to adjust the marketing mix. Given swift answers — within hours — can also eliminate wasted costs incurred on the low marketing campaigns because the business had to wait for the answers.
Related to the problem above, adding more data to the mix helps create better segmentation models in general. It is done both with more comprehensive data and a greater volume of data. Creating broader data will add more variables to the equation that can be used for segmentation. Teams can explore algorithmically e. And using more comprehensive data will add a more generous amount of time to the analysis and improve segmentation accuracy.
As we have seen, adding more data to your analysis will help you produce better results. It is not just from just broadly adding more data, but also finding the right data to fit your problem and build a trusted product.
Adding more data will help in data science problems improve accuracy. It will explore detailed why and how questions, produce actionable results and gain a broader purview on various analytic situations in traditional analytics.
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Catalog-like data documentation and knowledge sharing facilitate trust in the data and crowd-sourced data governance. Transform Data in Snowflake With Datameer. John Morrell July 1, Transform Data in Snowflake Today! We just have more data. The effect that Norvig et. In that paper, the authors included the plot below. That figure shows that, for the given problem, very different algorithms perform virtually the same.
So, case closed, you might think. Well… not so fast. But, they are now and again misquoted in contexts that are completely different than the original ones. But, in order to understand why, we need to get slightly technical. The basic idea is that there are two possible and almost opposite reasons a model might not perform well. In the first case, we might have a model that is too complicated for the amount of data we have. This situation, known as high variance , leads to model overfitting.
We know that we are facing a high variance issue when the training error is much lower than the test error. High variance problems can be addressed by reducing the number of features, and… yes, by increasing the number of data points. Yes, you got it right: high variance. In both cases, the authors were working on language models in which roughly every word in the vocabulary makes a feature.
These are models with many features as compared to the training examples. Therefore, they are likely to overfit. And, yes, in this case adding more examples will help.
But, in the opposite case, we might have a model that is too simple to explain the data we have. In that case, known as high bias , adding more data will not help. See below a plot of a real production system at Netflix and its performance as we add more training examples. So, no, more data does not always help. As we have just seen there can be many cases in which adding more examples to our training set will not improve the model performance. If you are with me so far, and you have done your homework in understanding high variance and high bias problems, you might be thinking that I have deliberately left something out of the discussion.
Yes, high bias models will not benefit from more training examples, but they might very well benefit from more features. Well, again, it depends. Pretty early on in the game, there was a blog post by serial entrepreneur and Stanford professor Anand Rajaraman commenting on the use of extra features to solve the problem. The post explains how a team of students got an improvement on the prediction accuracy by adding content features from IMDb.
In retrospect, it is easy to criticize the post for making a gross over-generalization from a single data point. As a matter of fact, many teams showed later that adding content features from IMDB or the like to an optimized algorithm had little to no improvement. Some of the members of the Gravity team , one of the top contenders for the Prize, published a detailed paper in which they showed how those content-based features would add no improvement to the highly optimized collaborative filtering matrix factorization approach.
To be fair, the title of the paper is also an over-generalization. Content-based features or different features in general might be able to improve accuracy in many cases. But, you get my point again: More data does not always help.
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