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 contracted to help win a bid for providing a managed service worth $200k to a large US-based vehicle manufacturer. They were intially discussing a multi-year deal for plant inventory discovery, however it was quickly realised by the client that it would make sense to turn this into a managed service.

The service included upgrade of current Discovery solution with right-sizing the architecture, installation of Remedy with CMDB and support services. The scope of Discovery was to scan the plant networks – the challenge being that these were on profinet and were not only sensitive equipment but scanning was only permitted under very strict windows.

We managed to complete the implementation of the solution upgrade and installation, with the implementation of the full managed service coming later due to delays on the client end. As part of value add of the service, the customer was also provided with a custom solution I developed in python that worked with the Discovery API to be able to load IP ranges with updated location information and automatically update the scan data and schedules.

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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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My company was initially approached directly by this large telecoms company to deploy Discovery with CMDB. My role as the Principal was technical lead for the project and Solution Architecture. Having worked with the client to draw up the Solution Design, they dropped the project after the first milestone, and used my Solution Design to deploy it themselves.

We were then approached approximately 12 months later by a large managed services company that had taken on the client and wanted to outsource to our Asset & Configuration service solution since they did not have the technical expertise. An intial gap analysis revealed they had mostly followed the design but were struggling in some key areas.

I worked with the MS provider to (now our customer) to workshop and architect the service, worth £240k, that we would provide which included the following roadmap:

• Extend the current deployment and resolve gaps in estate scanning
• Integrate with with Rapid7 to enhance CMDB with ownership details
• Data Quality Assurance
• Deployment of uControl for service modelling
• Credential and license management analysis
• Initiative to identify application owners to reduce risk and improve efficiency in reporting

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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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