Data Wrangling: Evolution and Application in Data Analysis

By CIOReview | Monday, May 28, 2018
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With data quickly becoming a valuable asset for businesses, it has enabled the identification of trends, patterns, and modes of behavior, all of which can be used to offer a personalized product or service. Consequently, the emergence of IoT and sophisticated data collection models ensure that there are no boundaries to the types or amounts of data an organization can procure. While collected data forms the foundation, the crucial step of analyzing this data effectively to derive actionable insights is made possible with data wrangling solutions.

Helping data analysts to make better sense of vast, diverse and clumsy data sets, data wrangling is the first piece of the data analysis puzzle. Data can then be examined much faster and more effectively as data wrangling cleans and unifies complex data, enabling enterprises to understand their data better. Data wrangling would typically involve manually converting data from its original format into easily digestible parts using tools like visualization and aggregation. But, data wrangling solutions can drastically improve the process facilitating better data analytics as a result.

Data wrangling solutions to improve data analytics

The data from various sources on which the analytics is performed, has created the need for the development of intuitive, cost-effective and efficient data wrangling solutions since it has become increasingly difficult to ‘wrangle’ these complex data sets.

The Need for Data Wrangling

An organized data leads to giving invaluable insights to businesses leading to pioneering innovations, with meaningful statistics from previously unorganized information. Eventually, companies are empowered with effective data wrangling allowing to find the hidden value in diverse data sets, giving the enterprises a competitive edge with accurate information rather than just assumptions.

The potential of how businesses can utilize data to succeed evolves with the growth of data wrangling solutions, which are bound to become essential in the future of enterprises.