![]() ![]() They can then analyze this data to identify potential customers for specific products. Companies can crunch data from multiple sources and combine it to provide a more complete picture of customer activity. Highly paid data scientists and business analysts can use their time more efficiently analyzing the most valuable information instead of hunting through vast amounts of raw data.ĭata crunching can also help companies achieve specific business goals, such as: The time savings translate into lower analysis costs. Automating data crunching also accelerates the process of cleaning up raw data, so companies also have more up-to-date information available for analysis. This means companies can save time by focusing their analysis efforts on the most relevant data. Data crunching hones datasets to a more manageable size, discarding unneeded data and eliminating duplication. Most companies gather more data than they can analyze. Data Crunching BenefitsĬonverting raw data into a usable form can be extremely time-consuming for data scientists, so it makes sense to automate data crunching as much as possible using programming languages or other tools. When companies are able to analyze data that’s combined from multiple internal and external sources, they may gain insights that wouldn’t be revealed by analyzing a single data source. It helps the company make more informed decisions, identify new opportunities and run more efficiently. Why Crunch Data?ĭata crunching enables a company to derive value from its data through analysis. Also, if the company’s departments use different applications, it may need to massage data into a common format so it can report on information from across the entire business. A company may need to convert information from external data feeds so it can apply its existing business intelligence tools to the data. It can also involve eliminating duplicated and erroneous data.ĭata crunching may be needed for a variety of different reasons. It commonly involves clearing out proprietary formatting and unwanted data, converting number and date formats and reformatting and structuring the information. ![]() Many programming languages and tools are used for data crunching, including R, Python, Java, MATLAB and SAS.ĭata crunching is needed to convert raw data into a form suitable for analysis.Automating the process can save companies time and money while making information more quickly available for analysis.Data crunching commonly involves stripping out unwanted information and formatting, as well as cleaning and restructuring the data.Data crunching is often an essential step in preparing large amounts of raw data for analysis or processing by other applications. ![]() Once these steps are completed, companies can apply analysis tools to the data to glean business insights. It includes stripping out unwanted information and formatting, translating data into the required format and structuring it for analysis or processing by other applications. What Is Data Crunching?ĭata crunching refers to key initial steps required to prepare large volumes of raw data for analysis. Because these preparatory steps can be labor-intensive, automated data crunching can save businesses time and money while making information available more quickly for analysis. Data crunching is the process of cleaning, reformatting and structuring raw data so that it can be used by analytical tools or other applications. Raw data often needs some preliminary work before it’s ready for analysis. East, Nordics and Other Regions (opens in new tab)Īnalyzing large amounts of data can yield business insights that help companies identify opportunities to increase revenue and cut costs. ![]()
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