Bionics – a primer



Greater automation is the future for the IT industry, and we’ve called DXC’s automation programme ‘Bionics’. It’s about being data driven with a flexible tool kit, rather than being all in on a particular vendor or product. To understand what we’re trying to achieve with Bionics (besides reading the rest of this post) I recommend reading The DevOps Handbook, and to get the foundation skills needed to contribute please run through the Infrastructure as Code Boot Camp [DXC only link].


‘Bionics’ is the name that we’ve given to DXC Technology’s automation programme that brings together CSC’s ‘Operational Data Mining’ (ODM) and HPE ES’s ‘Project Lambroghini’. This post is written for DXC employees, and some of the links will go behind our federated identity platform, but it’s presented here on a public platform in the interest of ‘outside in'[1] communication that’s inclusive to customers and partners (and anybody else who’s interested in what we’re doing). What I’ll present here is part reading list, and part overview, with the aim of explaining the engineering and cultural foundations to Bionics, and where it’s headed.

Not a vendor choice, not a monoculture

The automation programme I found on joining CSC can best be described as strategy by vendor selection, and as I look across the industry it’s a pretty common anti-pattern[2]. That’s not how we ended up doing things at CSC, and it’s not how we will be working at DXC. Bionics is not a label we’re applying to somebody else’s automation product, or a selection of products that we’ve lashed together. It’s also not about choosing something as a ‘standard’ and then inflicting it on every part of the organisation.

Data driven

Bionics uses data to identify operational constraints, and then further uses data to tell us what to do about those constraints through a cycle of collection, analysis, modelling, hypothesis forming and experimentation. The technology behind Bionics is firstly the implementation of data analysis streams[3] and secondly a tool bag of automation tools and techniques that can be deployed to resolve constraints. I say tools and techniques because many operational problems can’t be fixed purely by throwing technology at them; it’s generally necessary to take an holistic approach across people, process and tools.


The constraints that we find are rarely unique to a given customer (or industry, or region) so one of the advantages we get from the scope and scale of DXC is the ability to redo experiments in other parts of the organisation without starting from scratch. We can pattern match to previous situations and what we learned, and move forward more quickly.

Design for operations

Data driven approaches are fine for improving the existing estate, but what about new stuff? The key here is to take what we’ve learned from the existing estate and make sure those lessons are incorporated into anything new we add (because there’s little that’s more frustrating and expensive than repeating a previous mistake just so that you can repeat the remedy). That’s why we work with our offering families to ensure that so far as possible what we get from them is turnkey on day 1 and integrated into the overall ‘Platform DXC’ service management environment for ongoing operations (day 2+). Of course this all takes a large amount of day 0 effort.

Required reading

What the IT industry presently calls ‘DevOps’ is largely the practices emerging from software as a service (SaaS) and software based services companies that have designed for operations (e.g. Netflix, Uber, Yelp, Amazon etc.). They in turn generally aren’t doing anything that would be surprising to those optimising manufacturing from Deming‘s use Statistical Process Control onwards.

Theory of constraints lies at the heart of the Bionics approach, and that was introduced in Goldratt‘s The Goal, which was recast as an IT story in Gene Kim (et al’s) The Phoenix Project. I’d suggest starting with Kim’s later work in the more prescriptive DevOps Handbook, which is very much a practitioner’s guide (and work back to the earlier stuff if you find it inspiring[4]).

The DevOps handbook does a great job of explaining (with case study references) how to use the ‘3 DevOps ways’ of flow, feedback and continuous learning by experimentation[5].

Next after the DevOps Handbook is Site Reliability Engineering ‘How Google Runs Production Systems’ aka The SRE Book. It does just what it says on the jacket, and explains how Google runs systems at scale, which has brought the concepts and practices of Site Reliability Engineering (SRE) to many other organisations.

Learning the basics of software engineering

The shift to automated operations versus the old ways of eyes on glass, hand on keyboards means that we need to write more code[6]; so that means getting ops people familiar with the practices of software engineering. To that end we have the Infrastructure as Code Boot Camp, which provides introductory material on collaborative source code management (with GitHub), config management (with Ansible) and continuous integration/continuous delivery (CI/CD) (with Jenkins). More material will come to provide greater breadth and depth on those topics, but if you can’t wait check out some of the public Katacoda courses.

Call to action

Read The DevOps Handbook to understand the context, and do the Infrastructure as Code Boot Camp to get foundation skills. You’ll then be ready to start contributing; there’s plenty more reading and learning to do afterwards to level up as a more advanced contributor.


[1] My first ‘outside in’ project here was the DXC Blogs series, where I republished a number of (edited) posts that had previously been internal (as explained in the intro). I’ll refer to some of those past posts specifically.
[2] I’ve been a huge fan of understanding anti-patterns since reading Bruce Tate’sBitter Java‘. Anti-patterns are just so much less numerous than patterns, and if you can avoid hurting yourself by falling down well understood holes it’s generally pretty easy to reach the intended destination.
[3] It’s crucial to make the differentiation here between streams and lakes. Streams are about working with data now in the present, whilst lakes are about trawling through past data. Lakes and streams both have their uses, and of course we can stream data into a lake, but much of what we’re doing needs to have action in the moment, hence the emphasis on streams.
[4] If you want to go even further back then check out Ian Miell’s Five Books I Advise Every DevOps Engineer to Read
[5] More on this at 3 Ways to Make DXC Better
[6] Code is super important, but it’s of little use if we can’t share and collaborate with it, which is why I encourage you to Write code. Not too much. Mostly docs.

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