I was asked to provide a health check with insights into the clients Discovery operations. I created a custom application called Pithos – a python-based application which collected data from CMDB and Discovery. This data was then imported into Power BI and we were able to present the following back to the client:

• High disk utilisation
• Presence of core dumps
• Segregated architecture, but no network segregation
• Sensitive data such as plaintext usernames and passwords stored in unstructured data fields
• Scan schedules impacting performance

This was followed up with a roadmap of improvements for the client with the option of being delivered by my company.

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I worked with presales to deliver a $55k project with insights into the clients Discovery operations created with Pithos – a custom built python-based application which collected data from CMDB and Discovery. This data was then imported into Power BI and we were able to present the following back to the client:

• Sensitive data filters not capturing plaintext username/password
• Under-specced Outpost sizes causing performance issues
• Active user accounts from departed users not removed
• Various data quality issues
• Configuration discrepencies between prod and dev environments

This was followed up with a roadmap of improvements for the client which included development of a runbook, and 10 consultancy days of remediation.

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I worked in presales to present insights into their ITSM operations gathered from Talos back to the client. My involvement was in data cleansing and preparation for injesting into Talos for supervised and unsupervised learning. Once Talos had created the reports, I would then import these to PowerBI to develop the insights. With AI we were able to identify:

• Issue candidates for automation e.g. “password reset”
• Lack of categorisation and service catalogue, poor summaries written in tickets
• 3rd party structured automation information posted into notes fields
• Categorical data located in unstructured notes fields
• Under-utilisation of knowledge
• Change requests logged as tickets leading to high level of tickets with low MTTR
• High level of critical abnormalities requiring problem RCA
• General lack of granularity in categories, impacting management reporting

This was followed up with an offer to the client to deliver the recommendations such as service desk training, restructuring categories, data cleansing, and knowledgebase improvements

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I was technical lead on a project worth $200k to implement the Helix Discovery SaaS solution. Since scanning hybrid infrastructure requires on-prem components there was still substantial Solution Design work required.

At this time the SaaS product was less than 18 months old and there were gaps in features and functionality in how it worked compared to the on-prem version.

This led to challenges with unkown behaviour and bugs in the collector/outpost components which had to be resolved whilst maintaining client confidence in the solution they had bought. All bugs were logged and tracked with the vendor, but thankfully sufficiently minor that they did not delay implementation of the solution.

The customer was also given value added data quality reporting allowing them to identify various gaps and misconfiguration across their estate.

AI Led Operational Improvements

I worked in presales to present insights into their ITSM operations gathered from Talos back to the client. My involvement was in data cleansing and preparation for injesting into Talos for supervised and unsupervised learning. Once Talos had created the reports, I would then import these to PowerBI to develop the insights. With AI we were able to identify:

• Issue candidates for automation e.g. “password reset”
• Categorical data located in unstructured notes fields
• Under-utilisation of knowledge and overuse of top 5 knowledge articles suggesting need to break down into multiple topics
• Poor knowledge tagging to incident correlation, impacting the performance of the chatbot
• High MTTR for self-service leading to higher phone-resolved issues with shorter MTTR
• Discovery of sensitive (GDPR) information in unstructured text fields

This was followed up with an offer to the client to deliver the recommendations such as service desk training, restructuring categories, data cleansing, and knowledgebase improvements.

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I was in charge of the architecture for the deployment £200k worth of solutions and a managed service for a large German consulting firm that wanted to implement a new Asset & Configuration Management function using BMC’s toolset.

Once the deployment was completed and the customer onboarded, I acted as an 3rd line escalation point issues logged through the managed service. In particular I got involved with issues with Data Quality and modelling their network environments. As part of the service they were also gauranteed regular health checks – which we provided in the form of a unique set of analytics written in Python which were exported to Power BI for presenting insights back to the customer, such as:

• Root user logins
• Vulernable open ports
• Recommendations for search and query optimisation
• Compliance issues
• Data quality issues

This unique service was provided only by the company I was working for at the time where I developed it.

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