MICROFOCUS ALM TO JIRA MIGRATION

Major Insurance Company – RPA Case Study

 

 

Problem Statement:

Our Client, a Major Insurance provider, required a solution to rapidly automate the data migration from Microfocus ALM to Jira.

The licensing subscription was pending and a large CAPEX cost, while the data was complex with large data loads.This meant there were limited cost effective solutions that could deliver the required business outcomes

The Clients original approach was engagement via Data Specialists(manual labour) which had significant limitations due to the large datasets, with the impact being major delay to project timelines, with forecasted project completion to be 6-8 months which was not a viable option.

Client Outcomes – Metrics

GB Data

%

ROI

The project scope was delivered in line with pre-agreed milestones, in advance of the project schedule and within the set budget.

With the exception of occasional IT environment instability, the DM-BOT automatically migrated large datasets(350 GB & 2.5M records) accurately.

The project only required 2x Migration Runs with the first a 98% success rate. A final migration run was required to complete necessary data cleansing and any anomalies detected during the UAT phase.

The automated validation also proved the automated ETL process was efficient, which exceeded client expectations

The TL Consulting Solution:

TL Consultings DMaaS framework combines our Pro-serve team dataa dvisory and analysis capabilities, with our own in-house built RPA Data Migration BOT solution.

The DMaaS capability caters for all the Client requirements and success criteria, with the DM-BOT being an RPA solution accelerator, automatically migrating and shifting large data volumes, while managing data integrity and relationships following a proven agile methodology (scrum).

The benefits of the DM-BOT solution include increasing accuracy and quality, intelligent automation, bigdata migration, efficiency and ROI to name a few.

%

ACCURACY (FIRST RUN)

Million records

Major Insurance Company Case Study

 

 

Problem Statement:

Our Client, a Major Insurance provider, required a solution to rapidly automate the data migration from Microfocus ALM to Jira.

The licensing subscription was pending and a large CAPEX cost, while the data was complex with large data loads. This meant there were limited cost effective solutions that could deliver the required business outcomes

The Clients original approach was engagement via Data Specialists(manual labour) which had significant limitations due to the large datasets, with the impact being major delay to project timelines, with forecasted project completion to be 6-8 months which was not a viable option.

Client Outcomes – Metrics

GB Data Migrated

%

ROI

%

ACCURACY (FIRST RUN)

Million records migrated

The project scope was delivered in line with pre-agreed milestones, in advance of the project schedule and within the set budget.

With the exception of occasional IT environment instability, the DM-BOT automatically migrated large datasets(350 GB & 2.5M records) accurately.

The project only required 2x Migration Runs with the first a 98% success rate. A final migration run was required to complete necessary data cleansing and any anomalies detected during the UAT phase.

The automated validation also proved the automated ETL process was efficient, which exceeded client expectations

The TL Consulting Solution:

TL Consultings DMaaS framework combines our Pro-serve team data advisory and analysis capabilities, with our own in-house built RPA Data Migration BOT solution.

The DMaaS capability caters for all the Client requirements and success criteria, with the DM-BOT being an RPA solution accelerator, automatically migrating and shifting large data volumes, while managing data integrity and relationships following a proven agile methodology (scrum).

The benefits of the DM-BOT solution include increasing accuracy and quality, intelligent automation, bigdata migration, efficiency and ROI to name a few.

Contact us to migrate your data today

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