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The Challenges of Data Science

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12 August 2015


The Challenges of Data Science

A few hard truths come up so often in the practice of data science that evangelizing these truths has become a large role of the data science team at Cloudera. For a system that seeks to enable complex analytics on huge data to be successful, it needs to be informed by, or at least not conflict with, these truths.

First, the vast majority of work that goes into conducting successful analyses lies in preprocessing data. Data is messy, and cleansing, munging, fusing, mushing, and many other verbs are prerequisites to doing anything useful with it. Large data sets in particular, because they are not amenable to direct examination by humans, can require computational methods to even discover what preprocessing steps are required. Even when it comes time to optimize model performance, a typical data pipeline requires spending far more time in feature engineering and selection than in choosing and writing algorithms.

For example, when building a model that attempts to detect fraudulent purchases on a website, the data scientist must choose from a wide variety of potential features: any fields that users are required to fill out, IP location info, login times, and click logs as users navigate the site. Each of these comes with its own challenges in converting to vectors fit for machine learning algorithms. A system needs to support more flexible transformations than turning a 2D array of doubles into a mathematical model.

Second, iteration is a fundamental part of the data science. Modeling and analysis typically require multiple passes over the same data. One aspect of this lies within machine learning algorithms and statistical procedures. Popular optimization procedures like stochastic gradient descent and expectation maximization involve repeated scans over their inputs to reach convergence. Iteration also matters within the data scientist’s own workflow. When data scientists are initially investigating and trying to get a feel for a data set, usually the results of a query inform the next query that should run. When building models, data scientists do not try to get it right in one try. Choosing the right features, picking the right algorithms, running the right significance tests, and finding the right hyperparameters all require experimentation. A framework that requires reading the same data set from disk each time it is accessed adds delay that can slow down the process of exploration and limit the number of things we get to try.

Third, the task isn’t over when a well-performing model has been built. If the point of data science is making data useful to nondata scientists, then a model stored as a list of regression weights in a text file on the data scientist’s computer has not really accomplished this goal. Uses of data recommendation engines and real-time fraud detection systems culminate in data applications. In these, models become part of a production service and may need to be rebuilt periodically or even in real time.

For these situations, it is helpful to make a distinction between analytics in the lab and analytics in the factory. In the lab, data scientists engage in exploratory analytics. They try to understand the nature of the data they are working with. They visualize it and test wild theories. They experiment with different classes of features and auxiliary sources they can use to augment it. They cast a wide net of algorithms in the hopes that one or two will work. In the factory, in building a data application, data scientists engage in operational analytics. They package their models into services that can inform real-world decisions. They track their models’ performance over time and obsess about how they can make small tweaks to squeeze out another percentage point of accuracy. They care about SLAs and uptime. Historically, exploratory analytics typically occurs in languages like R, and when it comes time to build production applications, the data pipelines are rewritten entirely in Java or C++.

Of course, everybody could save time if the original modeling code could be actually used in the app for which it is written, but languages like R are slow and lack integration with most planes of the production infrastructure stack, and languages like Java and C++ are just poor tools for exploratory analytics. They lack Read-Evaluate-Print-Loop (REPL) environments for playing with data interactively and require large amounts of code to express simple transformations. A framework that makes modeling easy but is also a good fit for production systems is a huge win.


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