What Is Data Preparation?

Data Preparation is the process of collecting, cleaning, and consolidating data into one file or data table, primarily for use in analysis.

Data preparation is most often used when:

Data Preparation definition
  • Handling messy, inconsistent, or un-standardized data
  • Trying to combine data from multiple sources
  • Reporting on data that was entered manually
  • Dealing with data that was scraped from an unstructured source such as PDF documents

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Altair Monarch

structuring unstructured data

Altair Monarch is the industry’s leading solution for self-service data preparation.

  • Built for business users not rocket scientists
  • Automatically extract from reports & web pages
  • Combine, clean and use with your favorite tools

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The key steps to your data preparation:

  • Access Data: Access data from any source – no matter the origin, format or narrative. Monarch excels at intelligently and automatically extracting data from complex unstructured and semi-structured sources, like PDFs. Increased access to data means less manual work, faster insights and faster time to value realized by your organization
  • Cleaning Data and Improving Data Quality:  Manual data prep is error-prone, time-consuming and costly.  Business decisions rely on analytics. But, if the data is inaccurate or incomplete, your analytics inform wrong businesses decisions. Bad analytics means poor business decisions. Altair Monarch is programmed with over 80 pre-built data preparation functions to speed up arduous data cleansing projects.
  • Blending and Reconciling Data: Multiple, disconnected systems house your organization’s accounting data, customer and sales activity, employee information, and more. Likely your Marketing team alone probably has at least 6 systems they are trying to reconcile data from. Trying to blend data at this scale in Excel requires advanced knowledge of macros, functions and/or VLOOKUPS. Plus, it’s not very repeatable. Leveraging automation to blend data is a game-changer for saving time, effort and errors. Monarch’s click-to-join capabilities to enhance your data, tell a compelling story about the business and use that story for actionable reporting and analytics
  • Transforming and Instantly Re-Formatting Data: Being able to quickly change the way data is summarized and presented enables business analysts and executives to quickly consider different perspectives and views of data. Monarch make it easy to package your clean and blended data for insightful reporting you can confidently share with the rest of your organization.
  • Exporting and Using Your Data: Once data is all cleaned up, blended and enriched for analytics, it needs to be sent somewhere. Being able to export to any common platform makes it easy to maximize investments you’ve made in other BI tools and drive insight through your entire organization. Monarch allows you to leverage pre-defined exports to specific file formats, databases or visualization tools from the Monarch interface
  • Expanding Your Connectivity: Every company has a unique technology stack. Enabling you to be flexible with your data connections simplifies your ability to create valuable analytics, based on all of your organization’s data. Monarch comes equipped pre-configured data connections for data sources such as Excel, CSV, PDF, relational databases, and more. It is also equipped with ODBC and OLE DB drivers, connectors to Big Data sources such as Hadoop, and other data connectors and drivers.
  • Repeating Tasks with Automation: Most reports are generated from the same systems on a monthly or quarterly basis. Without automation, business analysts are performing the same data preparation steps, exporting the finalized reports to the same format and sending them to the same group of people. Monarch can automatically perform this in seconds, rather than hours or days. Rather than spending time tediously re-formatting data and repeatedly generating reports, analysts are free to explore their data and find brand new insights to create value for their organization.

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Here’s an example:

There are multiple values that are commonly used to represent the same U.S. state. A state like California could be represented by ‘CA’, ‘Cal.’, ‘Cal’ or ‘California’ to name a few.

A data preparation tool could be used in this scenario to identify an incorrect number of unique values (in the case of U.S. states, a unique count greater than 50 would raise a flag, as there are only 50 states in the U.S.). These values would then need to be standardized to use only an abbreviation or only full spelling in every row.

Want to learn more? Check out these whitepapers:

Gartner Report: Embrace Self-Service Data Prep

Bridge the Gap Between Business Agility and Governance

Extending Self-Service Data Preparation Through Automation

Gartner Data Preparation Thumbnail Data Preparation Series - Bridge The Gap Data Preparation Series - Automation