What is Data Munging?
Data munging, also known as data wrangling, is the data preparation process of manually transforming and cleansing large data sets. This process is typically performed manually using spreadsheets or scripts to filter out unwanted data and create a more relevant, digestible output.
The problem with this process is that it was invented over 20 years ago and hasn’t changed much since. Manual data cleansing is impractical for organizations today. The time and money spent on this outdated process is more than it’s worth. Many data scientists spend almost half their time cleaning their data before it can be used. Organizations need to be able to get new data quickly without time-consuming manual data processing steps like data munging.
The Outdated Process
It is becoming more and more important every day for organizations to leverage data. It’s clear that internal datasets are a key asset to organizations and an important foundation for business decisions. However, it is just as important to use external web data to gain insights on your market, industry, and competition as the web offers a treasure trove of alternative data sets. The benefits of web data span across industries such as Finance & Equity Research, eCommerce & Retail, Manufacturing, Travel & Hospitality, Risk Management, and Marketing and Sales.
With the outdated process of data wrangling, it takes months to complete a project. By the time the data is ready for analysis and consumption, it is no longer accurate. Another issue with this process is that it takes even more steps after data munging to get the dataset into a usable format. Data munging is a small part of the process of gathering and analyzing large datasets from the web. It is important to clean datasets to ensure accurate data, but this process does not have to involve manual scripts and spreadsheets that take months to sift through.
Data munging also creates opportunity for error since it is a manual process. Scripts and formulas can contain small errors that could have a large impact on your business decisions. One simple mistake could lead to inaccurate data which defeats the purpose of the data cleansing. Organizations cannot afford to rely on these outdated data analysis processes when there are better solutions available.
The Quicker and More Accurate Solution
It has been decades since data munging was invented, and now there is a better solution. Web Data Integration (WDI) allows organizations to identify, extract, prepare, integrate, and consume web data as a single integrated process rather than performing separate, manual steps such as munging data. WDI allows the data cleansing to happen automatically, providing faster access to accurate, usable data. There is no need for hand-written scripts or spreadsheets since Web Data Integration takes data through all the steps automatically with built-in quality control.
WDI has a broad range of uses including competitive price monitoring, customer sentiment analysis, product inventories and detail comparisons, market data aggregation, industry financial data extraction, background checks, and can even pull images and descriptions from travel sites or online marketplaces. These solutions allow organizations to compile unique alternative data sets from web data to make crucial business decisions as well as provide more value to their customers.
Creating Value For Your Organization
Getting quicker and more accurate insights from your web data is the key to creating value for your organization. The ability to leverage web data in real time provides value to your company and your customers. Bad data costs the U.S. $3 trillion per year, not only due to the time it takes to “clean” the bad data but also because bad data is accommodated by data scientists and decision makers in their daily work.
With Import.io, you can put the worries of bad data behind you. Import.io’s system has built-in quality control functions to ensure accurate data without analysts spending half their time munging data. Web Data Integration gets more data than traditional data wrangling tools because the data gathered is not limited to HTML webpages. In addition to more data and higher accuracy, Web Data Integration gathers data quicker than other data science methods. For example, in financial services WDI can extract data from financial statements or monitor sentiment toward the financial state of institutions in the news. In situations like these, data must be fresh and Web Data Integration is the quick and accurate solution.
Import.io’s WDI system takes data from the identification of which data will be useful to your company to an easy-to-understand output that integrates with your current analytics or reporting systems. This integration allows trustworthy data to be consumed almost instantly by decision makers in your firm. In the digital world, opportunities can be missed if they aren’t acted on quickly, and with traditional data analysis methods precious time is lost. The opportunity cost of not having competitive data is high, but getting broken or fragmented data from outdated methods can be worse.
Leveraging alternative data from the web provides value to organizations by allowing them to find trends, issues, and opportunities that can’t be found in internal enterprise data – as long as the data is timely and accurate. Web Data Integration can give your organization the competitive edge needed to succeed.
Interested in learning more about Import.io’s Web Data Integration solutions? Contact a data expert to find out how your organization can leverage web data.