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.
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.
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.
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.