Data Munging in ML Project
With the wide variety of verticals, use cases, user types, and systems consuming enterprise data today, the specifics of munging can take on a myriad of forms.
- Data exploration: Munging usually starts with data exploration. Whether an analyst is just poking around for brand new data in initial data analysis (IDA) or a data scientist is beginning to look for new associations in existing records in exploratory data analysis (EDA), the search always begins with some degree of data discovery.
- Data Transformation: Once a sense of the content and structure of the raw data is made, it must be transformed into new formats suitable for post-processing. This step involves a pure data scientist, such as de-nesting hierarchical JSON data, denormalizing disparate tables to access relevant information from one place, or transforming and aggregating time series data into desired dimensions and ranges.
- Data Enrichment: Optionally, once the data is ready for consumption, data users can perform additional enrichment steps. This includes finding external sources of information to expand the scope or content of existing records. For example, using an open source weather data set to add daily temperature to an ice cream parlor’s sales data.
- Data Validation: The final, perhaps most important, step is validation. At this point, the data is ready to use, but certain sanity or sanity checks are critical if you want to trust the processed data. This step allows users to detect typos, incorrect mappings, problems with transform steps, even rare corruptions caused by crashes or calculation errors.
When it comes to the actual tools and software used for data collection, data engineers, analysts, and scientists have access to an overwhelming array of options.
The most basic mung operations can be done in general tools like Excel or Tableau — from looking for typos to using pivot tables or the occasional info visualization and simple macro. But for regular mungers and wranglers, a more flexible and powerful programming language is much more effective.
Python is often hailed as the most flexible popular programming language, and this is no exception when it comes to data collection. With one of the largest collections of third-party libraries, especially rich data processing and analysis tools such as Pandas, NumPy, and SciPy, Python simplifies many complex data collection tasks. Pandas in particular is one of the fastest growing and best supported data collection libraries, while still only a small part of the massive Python ecosystem.
Python is also easier to learn than many other languages due to simpler and more intuitive formatting as well as a focus on readable English language syntax. Additionally, with Python’s broad applicability, rich libraries, and online support, new professionals will find the language useful far beyond data processing use cases, anywhere from web development to workflow automation.
Impact of Cloud with Data Munging
Cloud computing and cloud data warehouses in general have contributed to a massive expansion of the role of enterprise data across organisations and across markets. Data munging is only a relevant term today due to the importance of fast, flexible yet carefully managed information, all of which have been primary benefits of modern cloud data platforms.
Concepts such as the data lake and NoSQL technologies have now expanded the prevalence and utility of self-service data and analytics. Individual users everywhere have access to vast amounts of raw data and are increasingly trusted to effectively transform and analyze that data. These specialists must know how to clean, transform and verify all this information themselves.
Whether it’s modernizing existing systems like data warehouses for better reliability and security, or empowering users like data scientists to work on enterprise information end-to-end, data mining has never been more relevant.
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