Data Wrangling to Transform the Age-Old Business Strategies

Data wrangling can be defined as a process of converting the original “raw” data into an organized set of information. Raw data can be in the form of texts, images, figures, and sheets, which is modified using certain tools and techniques. This data is further used for fulfilling a variety of purposes like analytics and decision making. In short, this practice includes transforming the manual data into a prescribed format in order to make it useful for a number of applications. The process of data is followed by discovering the raw data, profiling the same, structuring in a systematic order, cleaning the errors, validating, enriching and finally publishing the final data.

 

It has been estimated that the time spent on identifying, cleansing and integrating data is comparatively higher than the other steps followed in this process. This is due to the complications related to locating data scattered in various business applications. Growing data volume and the demand for advanced technologies like AI and machine learning are the important factors that have accelerated the growth of this practice.  Advancement in data-wrangling technology helps process valuable data needed in business development.

Infosys is reported to have invested in Trifacta in 2016. An additional investment of USD 6 million in Trifacta has been made by Infosys in the year 2019. Trifacta will provide a “wrangling” solution for the company’s big data platform and other software platforms that Infosys has recently built.

 

How data wrangling is different from data mining and ETL

 

• Sometimes, it is a bit confusing to differentiate between data mining and data wrangling. Data mining is the process where large sets of data are analyzed to find patterns, relationships, and trends that might be missed through traditional analysis methods. Whereby, data wrangling is converting raw data to organized data for decision making.

•  This has been developed, in the recent years, as a fast-growing section of the analytics industry.   Disordered and complicated data sets a roadblock to data analysis. It has transformed the process by simply streamlining the previous practices.

• Data wrangling is often compared to ETL as well. However, the difference is it is mostly used by business analysts whereas ETL is majorly used by IT professionals.

 

According to Allied Market Research, global data wrangling market is projected to register a significant CAGR from 2019 to 2026. Increasing adoption of big data analytics and demand for cloud-based data analytics have fuelled the growth of this market. Wrangled data are now widely being used by corporate houses, data architects, or data scientists, who further process the data or reuse it in other forms.

 

The benefits of using Data Wrangling

 

• When it comes to the advantages, it basically helps provide value through analysis. It can be organized into a standardized and repeatable process that transforms data sources into a common format, which can be reused multiple times.
• Moreover, quality data are even more helpful, as multiple data sources are wrangled into the same format.
• At times, a large bulk of data is processed for interpretation; here, it provides credibility to data analytics. It chooses the right data required to provide the necessary solutions.

 

Data munging is done by manual, semi-automated, and fully automated techniques. The most opted method is the fully-automated one, as this approach develops the logic upfront in a reusable ETL automated process. IT and data processing organizations are shifting their data to cloud-based environments. Most of the corporate and business houses are managing to multiply data environments by mixing private and public cloud solutions known as a hybrid cloud environment. Accordingly, the wrangled data works with hybrid cloud environment solutions that store the data and can be reused as and when needed.

 

To conclude, it can be stated the process of data wrangling is a source of organized data which is used in different analytics and data visualization applications. This practice is expected to expand the usage of data in business organizations and supplement the potential value of data to the respective business.

Reference Links:

https://www.livemint.com/Companies/xD3wLdFVHVMfzQ5i9FDpGP/Infosys-buys-minority-stake-in-USbased-startup-Trifacta.html

https://www.trifacta.com/blog/benefits-wrangling-data-across-cloud-premise/

 

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