What is Data Blending?

Data blending is the process of combining data from multiple sources into a functioning dataset. This process is gaining attention among analysts and analytic companies because it is a quick and straightforward method used to extract value from multiple data sources. It can help to discover correlations between the different data sets without the time and expense of traditional data warehouse processes.

Guide to Self-Service Data Preparation

Fast, Automated Data Transformation

Seamlessly connect to disparate data sources. Efficiently and confidently transform data for effective use in decision making.

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The Goals of Data Blending

  • Reveal a "deeper intelligence" within your data, by utilizing data from multiple sources
  • Provide accurate, actionable data in the hands of business analysts in a timely matter
  • Drive better decision-making skills by senior leaders in an organization

The most prominent environments where data blending is utilized are within sales/marketing, finance operations, and site/merchandising operations. In today’s world it is critical for these business sectors to adopt and integrate tools that will allow their users to visually mash their data so that they will have a competitive edge when it comes to responding to big data flows.

The Key Steps to Data Blending

Data Acquisition

Identify and obtain access to the data within your sources.

Joining Data

Combine the edited data for further use and analysis.

Data Cleansing

Redesign the data into a usable and functional format and correct/remove any bad data.

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Altair® Monarch®

Monarch is a market leading desktop-based self-service data preparation solution. Monarch connects to multiple data sources including structured and unstructured data, cloud-based data, and big data. Connecting to data, cleansing and manipulating data requires no coding.

Monarch can quickly convert disparate data formats into rows and columns for use in data analytics. Over 80 pre-built data preparation functions mean data preparation tasks can be completed quickly and error free. More time is spent on generating value from data as opposed to making data usable to begin with.

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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.

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Spend Less Time in Spreadsheets

Spreadsheets are powerful but not designed for today's analytics demands. See how Altair Monarch transforms data in minutes.

Use Cases

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.

Use Cases

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.

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