Analyzing large amounts of data can yield business insights that help companies identify opportunities to increase revenue and cut costs. Raw data often needs some preliminary work before it’s ready 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. 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.
What Is Data Crunching?
Data crunching refers to key initial steps required to prepare large volumes of raw data for analysis. It includes stripping out unwanted information and formatting, translating data into the required format and structuring it for analysis or processing by other applications. Once these steps are completed, companies can apply analysis tools to the data to glean business insights.
- Data crunching is often an essential step in preparing large amounts of raw data for analysis or processing by other applications.
- Data crunching commonly involves stripping out unwanted information and formatting, as well as cleaning and restructuring the data.
- Automating the process can save companies time and money while making information more quickly available for analysis.
- Many programming languages and tools are used for data crunching, including R, Python, Java, MATLAB and SAS.
Data Crunching Explained
Data crunching is needed to convert raw data into a form suitable for analysis. It commonly involves clearing out proprietary formatting and unwanted data, converting number and date formats and reformatting and structuring the information. It can also involve eliminating duplicated and erroneous data.
Data crunching may be needed for a variety of different reasons. A company may need to convert information from external data feeds so it can apply its existing business intelligence tools to the data. 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.
Why Crunch Data?
Data crunching enables a company to derive value from its data through analysis. It helps the company make more informed decisions, identify new opportunities and run more efficiently. 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.
Data Crunching Benefits
Converting 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. An efficient data crunching process:
Saves time. Most companies gather more data than they can analyze. Data crunching hones datasets to a more manageable size, discarding unneeded data and eliminating duplication. This means companies can save time by focusing their analysis efforts on the most relevant data. Automating data crunching also accelerates the process of cleaning up raw data, so companies also have more up-to-date information available for analysis.
Saves money. The time savings translate into lower analysis costs. 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.
Data crunching can also help companies achieve specific business goals, such as:
Identify potential customers. Companies can crunch data from multiple sources and combine it to provide a more complete picture of customer activity. They can then analyze this data to identify potential customers for specific products.
Increase operational efficiency. Companies can pull together expense data from across the business to look for potential cost savings, such as opportunities to win volume discounts by sourcing similar products from the same suppliers.
3 Steps of Data Crunching
Data crunching consists of three main steps: reading the raw data, converting it and outputting the information.
Read raw data: This step pulls in data from the selected source. Raw data may be unformatted, in which case it may be necessary to extract the information that the company wants to analyze. You may need to validate it against other sources to identify errors.
Convert data: Several distinct operations may be needed to convert data from its original form to a format that can be used by analysis tools. Common operations include removal of unwanted characters and markup. It may be necessary to recognize multiple date formats and convert them to a common format. For example, a birthdate may have been input as 3/16/40 or March 16, 1940.
Output data in chosen format: The final data now is ready for output to a file or database that will be used for analysis. Many companies move this formatted data into a data warehouse, which is a type of database specifically designed for analyzing data from across the company.
Who Crunches Data and Where Is Data Crunching Used?
Many businesses have teams or individuals who handle data crunching to prepare data for analysis, or number crunching. The roles involved in data crunching include data scientists, data engineers and data architects.
Data scientists are analytical experts who apply their skills in math and computer science to solve business problems. Data scientists make sense of mountains of data and are adept at spotting trends and generating insights. Their work may include pre-analysis data crunching using programming languages such as Python or R.
Data engineers play an essential role in data crunching, since their job is to transform data into a form that is suitable for analysis. Data engineers build data pipelines that automatically crunch raw data and deliver it for analysis.
Data architects design data management systems, including data warehouses. They define the company’s data structures and the data flows needed to ingest data for analysis and reporting.
Data crunching benefits multiple groups within companies in many different industries. Examples include the following.
Marketing: Marketers often need to analyze data from a variety of different sources to better target customers and measure the success of campaigns. Data crunching helps marketers combine data from diverse sources, such as CRM systems and social media platforms, so they can gain a better view of customer activity and preferences.
Finance: Finance groups use analytics extensively to understand trends and factors influencing business performance and to make forecasts. Data crunching can be used to massage external data feeds and combine them with internal data for analysis. It’s often a key step in business reporting, or the internal and public reporting of operating and financial data.
Financial services: Big data and sophisticated algorithms have transformed the financial-services industry. Financial services firms crunch data from many different sources to track market activity in real time, facilitating automated high-speed trading.
Publishing: Media companies crunch data collected from websites to measure visitor activity, optimize advertising and affiliate revenue and target content to certain demographics. Publishers can rapidly develop ad hoc reports to help determine which variations of content should be marketed to different audiences.
Film: Entertainment companies use data analysis to determine whether their costly investments in movies will result in profits. They crunch data from various sources, such as social media, online ratings websites and box-office sales to identify the target market for specific films. They can also gather information on casting, theme, location and release date preferences.
Auto: Carmakers now crunch data from connected vehicles as well as sales and service outlets to improve quality of vehicles and better target marketing.
Oil and gas: These companies crunch a variety of massive datasets, including seismic data and information from drills and other sensors. Analyzing this data can reduce drilling time, improve safety and provide better intelligence about oil field capacity.
Best Data Crunching Languages
A number of programming languages are commonly used for data crunching, including several designed primarily for statistical analysis — here are some of the most popular.
An open-source language for statistical computing and graphics, R is one of the most widely used tools. It can be used to extract information from large, complex data sets and convert messy data into a structured form. An extensive ecosystem has grown up around R, including thousands of packages that extend the language’s functionality.
Another popular open-source language, Python is used for many different purposes including scientific and statistical computing. It's considered relatively easy to learn because of its intuitive and lucid syntax. It can be used for tasks as varied as importing data from Excel sheets to processing complex datasets for time-series analysis.
This is a general-purpose, open-source programming language owned by Oracle through its acquisition of Sun Microsystems in 2010. Some of the largest technology companies use Java to build their products, and it’s also at the heart of big data frameworks, such as Hadoop. Java is an established, trusted and fast-executing language, and it’s widely used for data crunching. Other elements of a company’s technology may already be built on Java, easing integration.
From MathWorks, it’s a matrix-based language designed to help engineers and scientists analyze systems and build models. The first commercial versions of MATLAB were released in the 1980s. Today, it’s extensively used for data-intensive scientific applications such as computer vision and signal analysis. MATLAB is used for data crunching as well as analysis. Its concise syntax enables data scientists and engineers to write functions using less code than with some other common languages.
From SAS Institute, this is a software suite used for statistics and analysis. Originally developed in the 1970s, SAS is still widely used in many industries and academic institutions. As the result of decades of enhancements, the software includes an extensive set of functions. The company offers products tailored for specific purposes, including customer behavior analysis.
4 Data Crunching Tips & Techniques
Without an automated process, data crunching can be extremely time-consuming — data scientists often spend more time cleaning and preprocessing data than analyzing it. Here are some tips for making the task more efficient:
Understand the use case. The questions a business wants to ask will determine which data you need and how you need to transform it.
Get access to the data early. Get permission to directly access the relevant source data in advance, if possible. Obtaining permission can take time, and you don’t want it to become a bottleneck that restricts the ability to analyze data.
Generate a detailed report of the dataset. Data scientists and data engineers may spend a lot of time simply trying to understand the information that’s in data sources. Some languages, like Python, include profiling tools that reduce the effort required by automatically performing an initial analysis.
Separate input, processing and output code. When writing data crunching code, it makes sense to separate the input, processing and output stages. This makes it easier to debug the code and reuse each stage for other purposes.
How to Automate Data Crunching
Data crunching is often a repetitive process that is regularly or even continuously performed on data from the same source. Business process automation can save a considerable amount of time and effort and ensure data quickly becomes available for analysis. Here are some key approaches to building a data pipeline that automatically generates the transformed data:
Map out the steps required. Identify every step involved in the process, from collecting the raw data to generating the final output.
Identify which steps to automate. Many cleansing and formatting steps can be automated by writing code or using software tools. Others, such as extracting data from some legacy systems, may require manual work.
Build and test the automation. Build code to execute each step in the data pipeline. Test the code repeatedly to verify it consistently produces the expected results.
Document the process. Thoroughly document each step so it’s easier to make changes in the future.
Analyze time savings. Analyzing the time savings compared to a manual process can help the business understand the value of automation and how best to apply it in the future.
Analyzing large amounts of information can be invaluable for decision-making, but companies often underestimate the amount of effort required to transform data into a form that can be analyzed. Building an automated data crunching process with the aid of advanced analytics platforms can save companies time and money while ensuring data quickly becomes available for analysis.
Data Crunching FAQs
The name “data crunching” is probably derived from number crunching, which usually refers to processing many complex calculations with computers. While number crunching describes processing numerical calculations, data crunching is an analogous term for processing large volumes of data to make it suitable for analysis.
Data crunching is often an essential step that prepares data for business analytics. Many companies use analytics to help understand and forecast business trends and performance. Analytics can be used to develop and drive marketing, sales and customer service strategies.
Crunching data prepares large amounts of information for further processing and analysis. It usually involves filtering data from various sources and translating it into an appropriate format for analytical tools.
The term crunching is often associated with numbers or data. It refers to the process of preparing data for processing and analysis.
It’s the term used to describe processing numerical data and calculations. Number crunching generally refers to taking large amounts of related numerical data and organizing it into a more useful format.