How data wrangling solutions can improve data analytics

Data is fast becoming a valuable asset for businesses. With this insight, it is possible to identify trends, patterns and modes of behaviour that can be used to tailor a product or service to meet the needs of an audience exactly. However, in the age of the Internet of Things and sophisticated data collection models, there are no limits to the types or even amounts of data an organisation can collect. But in order to analyse this data effectively to find actionable insight, data wrangling solutions are crucial.

What is data wrangling?

Put simply, data wrangling helps data analysts make sense of large, diverse, messy data sets. It is the first piece of the data analysis puzzle.

It cleans and unifies complex data so it can then be examined much faster and more effectively, unleashing previously unnoticed insight and allowing businesses to understand what their data means much more clearly. Conventionally, data wrangling would involve an expert manually converting data from its original format into easily digestible pieces using tools such as data visualisation, aggregation and more. However, data wrangling solutions can greatly improve on this, aiding better data analytics as a result.

Data wrangling solutions to improve data analytics

Today, data used by businesses in their analytics comes from a variety of sources. This created a need for intuitive, cost-effective and efficient data wrangling solutions to be developed as it became increasingly more difficult to ‘wrangle’ these complex data sets. Although data wrangling solutions simplify the data analytics process, these tools are intended to be used by data analysts, or other data professionals.

The need for data wrangling

When data is organised well, it can give businesses invaluable insight, leading to ground breaking innovations. It allows companies to find meaningful statistics from what was previously unorganised information. Ultimately, effective data wrangling empowers businesses with the means to find hidden value in diverse data sets when then can be used to inform future processes, giving them a competitive edge that is based on statistics and trends rather than assumption.

As data wrangling solutions continue to evolve and develop, so does the potential of how businesses can use data to aid their success, set to become an essential tool in the future of enterprise.


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