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 with this German company to enhance their Discovery and CMDB with various storage metadata and data from unsupported storage systems. I wrote extensive TPL code which integrated with APIs for NetApp, Dell Isilon, IBM Spetrum, and Nasuni.

For Nasuni I had to develop the whole model using data extracted from the APIs to model the Management Console, Volumes and Shares.

The outcome of this project was to allow the client to accurately track storage usage and billing.

During this time I also assisted the client in troubleshooting Azure discovery and retrieving additional matadata from VMware.

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I was technical lead on a project worth $40k 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.

An example challenge was in deploying shared outposts (documented as supported) between prod and dev SaaS instances. During deployment, an update was made to prod which delivered a change in key management to the outpost, and caused it to stop communicating with dev. The issue was resolved a week later when dev was updated, however it did mean that future architecture had to be updated to avoid this type of issue. Thankfully it did not impact delivery of the project.

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

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As part of a larger digital transformation I was responsible for the architecture of their Discovery, modelling application architecture and data integration pipelines to the CMDB.

The initial deployment was minimal and did not cover the whole estate, which was airgapped. I was challenged to come up with an architecture which would be suitable enough capture data across the airgapped environments. The proposed solution involved manual DB snapshots which would be transferred by ftp, however this was later dropped in favour of deployment of a mutual shared platform, which was desperately needed.

This was a part-time multi-year engagement, during this time I developed and delivered various customatisations to their data collection and pipeline, such as:

• SSL Certificate Discovery
• Netcool edge device intergration
• Service modelling
• Reporting and analytics
• Written maintenance manual

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I was responsible for the improving the managed service architecture of a multi-year service contract already in-flight. There had been significant issues raised over the quality of the service up until my engagemement.

The main complaints were:

• Support resource not seen to be solving any reported issues
• Missing roadmap meetings
• Not feeling the value

An action plan and strategy was put in place, for me to conduct quarterly service reviews where I would present the latest features of the products and recommend strategy for upgrade and utilisation of these features, including any enhancements we could provide to the service.

The support resource issue was identified as being a follow on outcome of the customer not willing to log tickets and instead engaging the named resource directly. In this way the resource was being treated as a contractor and being asked to work on various unspecified tasks and issues. In discussing the issue with the client, I suggested they could either continue with no SLAs and agree to the support resource under 1-day per week effort, or that in order for us to track performance and ensure requests were deailt with, they would need to engage with the service and log tickets.

However, as many of the requests would have been out of the scope of the service and meant engagement with professional services, they did not want to implement this change. They prefered to carry on under the existing arrangement, without further complaint.

During this time I was also able to implement health checks for them and identify a number of security vulnerabilites which were esclated to their CISO. One was the identification of plaintext username/password strings in running processes, and the other was production level software running under a non-service account credential.

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I worked 3 short projects with a US-based energy company over a period of three years to develop customisations to the data pipeline to their CMDB. This was to enable a front-end form for application ownership selection. For this they needed specifically to be able to extract metadata from their IBM DB2, MSSQL, Oracle, MongoDB and MariaDB databases for promotion to the front-end display.

The solution involved custom TPL code applied to BMC Discovery, which used a combination of SQL connections of JDBC and API connectivity with MongoDB. Both Discovery and the CMDB sync connections needed to be modified to make it work.

MongoDB presented the most challenging connection as Discovery was not built for a direct integration, so the solution called for running the API retrieval queries directly on the server as it scanned, with a local encrypted text file which contained the API credentials. The output was then retrieved the normal way.

The solution allowed for the client to be able to correctly identify and assign ownership for their database dependent applications.

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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 the technical lead on a project worth $170k to migrate the on-premise Discovery solution to to Helix Discovery SaaS.

This was a significant project as it was the first migration I had been asked to architect, so I had no templates or documentation to work off. I created the SOW, Solution Design and Project Plans. I decided that the best approach was to conduct a gap analysis and then work out what needed to achieved from there. As I had already worked several project with the SaaS platform, I was not totally in the dark.

The SaaS migration in some was would be technically easy because it was about determining what functionality would be decomissioned as it was not avialable in the cloud. However this needed to be carefully navigated with the stakeholders to reassure them that the functionality was either not needed for the SaaS platform, or there would be an alternative method/workaround for them to achieve the same results they got from the on-premise solution.

One of the interesting things turned up from the gap analysis was that they were under-licensed. As well as my company as a partner company being affected, we had a fiscal duty to report this back to the vendor. This did trigger a discussion with the client which was not initially well-recieved.

After many discussioned had taken place at the c-level I was asked for technical input as to whether there was a way they could be brought back into compliance. Fortunately, I did have an inelligant workaround solution which required a slightly more hands on approach to managing the scanning schedules and data collection. It did however provide satisfaction of the client and vendor, allowing the migration to continue successfully.

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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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