What is Data Wrangling?
Data wrangling, often referred to as data cleaning, data cleansing, data remediation, data munging — or even data janitor work, is the first important step in understanding and operationalizing data insights. The process includes connecting to data sources, reformatting the information so it’s consistent, removing duplicates, merging disparate sources, and filtering out unneeded “noise” in large datasets.
Data analytics teams often spend 50-80% of their time working on the mundane tasks involved in wrangling data and making it clean and ready to use in machine learning workflows, report generation, and related processes.
Efficient Data Wrangling is Critical
Given the time needed to acquire, clean, and organize data and how critical it is to almost every data analytics project, implementing tools that increase the data wrangling team’s effectiveness pays immense dividends.
Data wrangling is much more complicated, and its challenges more much more variable, than those encountered in Extract, Transform, Load (ETL) processes. ETL workflows require clean, organized data to start with, which is often not readily available. Analysts and business users need easy-to-learn tools that enable them to query and explore data on their own, without help from specialized IT or programming personnel — regardless of how well organized or clean the data is.
Conference Presentation: Unlock and Transform Difficult Data
Access Any Data Source
Useful data is often stored only in difficult-to-access formats, including everything from PDFs to spreadsheets to big data repositories. A robust data wrangling infrastructure makes it easy to connect to all sources and apply the transformations, filters, and calculations required to begin the process. Once you have connected to all your data, you can begin merging and joining data sources in logical ways to fill gaps, eliminate duplicated records, identify outliers, and reshape the data for further analysis.
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Data Wrangling Tools for the Enterprise or the Individual
Altair offers a wide range of advanced data wrangling software built on over 30 years of industry experience. International corporations, government agencies, academic institutions, and small- to medium-sized enterprises use and trust our technology to extract every bit of value from their data sources.
Our tools automate what would otherwise be tedious — and error-prone — manual tasks and are designed so any user can utilize them, eliminating the need for specialized training. You can deploy Altair software on desktop, on your own servers, or in the cloud depending on what works best for your team.
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Altair Monarch: Enterprise-Class Data Transformation
Altair Monarch is a comprehensive, self-service data transformation and process automation solution. It connects directly to a wide range of structured and semi-structured data sources, including PDFs, text, complex spreadsheets, JSON, XML, big data sources, relational databases, and many others. Business users and analysts can extract, cleanse, and transform data into consistent, governed, and secure rows and columns without specialized knowledge or training, and without writing any code. The platform includes more than 80 pre-built data preparation functions which makes it easy to build new error-free workflows in minutes.
Experience no-code, automated data transformation: Try Altair Monarch today, for free.
Streamline Audit Processes with Self-Service Data Preparation
Auditors are under significant pressure to keep expenditures down whether they work for an external audit firm or are part of an internal audit team. Achieving cost-effective audits requires organizations to do more with less - while maintaining or increasing audit quality. To succeed auditors not only need the right expertise and process but also the right data analytics tools.
Enhancing Your RPA Investment With Data Preparation
As more businesses use Robotic Process Automation (RPA) as a means to streamline operations and assess efficiency gaps, there are hurdles to fully realizing its benefits. For instance, existing data is often challenging for RPA processes to handle. Without a good solution, studies indicate that 80% of the time spent on RPA has to do with data preparation.
Discover how you can enhance your ongoing RPA investment with the right data analytics stack.
