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Quickr to Office 365 Migration: Data Quality
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Raja Balaji    

Data migration from Quickr to Office 365 is an important step in getting your organization on a thriving enterprise collaboration platform. However, Quickr to Office 365 migration process can be complicated. If done incorrectly, they can be time consuming or worse; it can lead to crucial data loss and errors. In fact, according to Oracle, 75% of the migrations fail as a result of failing to verify and check for data quality (Oracle 2011). Considering that failed migrations can lead to lost time and money, the importance of getting a migration right the first time cannot be overstated.

With the right data quality methods in place, your organization can avoid losing time and money. You need to take careful steps to ensure data quality issues do not trip an otherwise successful Quickr to Office 365 Migration. This article will discuss common data issues in migration, and the best methods on how to solve them.

3 Common Quickr to Office 365 Migration issues and how to prevent them

Even after a Quickr to Office 365 migration appears to be complete, users may still face issues if the proper steps were not taken to ensure data quality. Here are 3 of the most common issues:

  1. Garbled attachments: Users may find that the attachments that were uploaded in Quickr are altered. Either the type of attachment has changed, the content itself is filled with junk characters, or both.
  2. Missing data: After a migration, the user finds that some data, one or more users, or Quickr places are completely missing.
  3. Access given to the wrong member: In this case, privileges and permissions to a document or an entire ‘Quickr place’ have been changed. This can be a major problem if it causes access to sensitive data to fall into the wrong hands.

The way to avoid the issues stated above is through verifying data quality during the planning phase of a migration. This process is used to determine that the data being migrated is fit for its intended use.

Quickr to Office 365 Migration

How to ensure Data Quality for a Successful Migration

The first phase in data quality, is to make sure all the data is properly collected and compatible with the target platform. Data quality is checked by examining these key dimensions;

  • Accuracy– This step involves careful verification that all data information is presented and listed correctly. It involves checking for simple mistakes, like making sure that no extra characters (like underscores) were added, or that spaces were not taken away, and checking for complex issues, like if the metadata is reading incorrect file information like, date, file size, creator, etc.
  • Completeness–All metadata information for the content being transferred must be complete, intact, and up to date. Maarga will verify that all the metadata info collected for the Office 365 migration matches the metadata information created in Quickr.
  • Validity- In this part of the analysis, Maagra will look for errors and issues within the data itself that could cause migration problems. Detailed records are kept of any data issues, and the organization will be informed if some data, or a portion of some data, is erroneous and cannot be transferred.
  • Integrity- During this step, all migrated data is carefully checked for accuracy and consistency across all platforms. All transfer rules are followed accordingly.
  • Accessibly- Maagra will verifies that all privileges and permissions are transferred intact.

Common pitfalls – Where things can go wrong in a Quickr to Office 365 Migration

These information attributes are places that can cause migration and data issues:

Date Format: There are some differences in supported date formatting between Quickr and Office 365.

Comments: Some symbols or comment sections may not be supported by the target platform.

Language Supported: Languages beside English must be identified as in some cases, these languages will need to be converted into a supported format like ASCII.

Data Conversion: Often times, data is not ready to be migrated dude to compatibility issues with the target platform.

A 5 step approach to ensure data quality:-

In order to ensure a successful Quickr to Office 365 Migration, Maarga has tried and tuned a five step approach to achieve data quality repeatedly.

Step 1:- Cleansing: Identify all of the organization’s meaningful and relevant data.  In addition, data that is irrelevant, data that are erroneous, and data that is relevant but incompatible is also identified during this stage.

Step 2:- Matching: In the phase of extraction scan for duplicates in data in order to prevent them from being duplicated also on Office 365.

Step 3:- Profiling:  Analyse and identify all the data that is significant to the business. This step acts as an input for framing data quality rules

Step 4:- Data Quality Rules: In this step careful decisions are made based on the rules framed by learning the data, and the requirements obtained from other processes in data quality.

Step 5:- Dashboard Monitoring: Reports and dashboards are used to monitor the progress and success of the data quality.

A measured approach to data quality and validation ensures a smooth transition and reduces the chance of data issues occurring in the crucial first few weeks of migration. So what are you waiting for? It’s time to unlock your organization’s collective energy with a thriving enterprise collaboration platform.

Maarga is a boutique consultancy (www.maargasystems.com), with deep expertise in Migration projects from lotus notes to cloud based solutions. Reach out to a migration expert at sales@MaargaSystems.com

References:-

Oracle. 2011. Sucessful Data Migration. October. Accessed April 2017. http://www.oracle.com/technetwork/middleware/oedq/successful-data-migration-wp-1555708.pdf.

Maarga is a boutique consultancy with deep expertise in Lotus Notes migration, digital transformation and enterprise collaboration. Reach out to Maarga with your needs at Sales@maargasystems.com

The post Quickr to Office 365 Migration: Data Quality appeared first on Maarga Systems.



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Mar 28, 2017
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