New types of software and delivery mechanisms (SaaS, mobile) mean new problems and scale: “We were so used to dealing with tens of servers and suddenly it was hundreds and thousands of servers,” which in turn created more work for the development teams. More: “The digital expansion of business equals more work and firefighting,” Cox said. Less time spent doing dumb-shit: employees used to spend the eight hours of the park closed every night, manually updating each server.

Eventually every advisor will be a robo-advisor, which means there will be convergence. Without some marketshare numbers, it’s tough to tell if the banking startups are making a dent against incumbent banks. Josh Brown suggests that banks are quick to catch-up and have nullified any lead that companies like Weathfront could have made: It wasn’t long before the weaker B2C robo-advisors folded, the middling players were acquired and the incumbents launched their own competing platforms.

Everyone always wants to know metrics. While the answer is always a solid “it depends - I mean, what are your business goals and then we can come up with some KPIs,” there’s a reoccurring set of technical metrics. Nicole lists some off: These IT performance metrics capture speed and stability of software delivery: lead time for changes (from code commit to code deploy), deployment frequency, mean time to restore (MTTR), and change fail rate.

There’s some good “how do I actually get my organization do all this unicorn stuff” comments in this interview with DreamWorks Animation’s Doug Sherman. Here’s one sample bit on winning people over to microservices. Instead of going into the lab for six months to work on a tool that they think will be useful, they do a lot more user-driven work upfront and then do (it sounds like) weekly small batches to keep the users apprised of the tools and, you’d guess, give continuous feedback:

From The Economist a few weeks back: The real difference is that Trumponomics (unlike, say, Reaganomics) is not an economic doctrine at all. It is best seen as a set of proposals put together by businessmen courtiers for their king. Mr Trump has listened to scores of executives, but there are barely any economists in the White House. His approach to the economy is born of a mindset where deals have winners and losers and where canny negotiators confound abstract principles.

Appian raised just $48m as a private company, compared with $163m for Alteryx, $220m for Okta, $259m for MuleSoft and more than $1bn for Cloudera. In fact, all four of the unicorn IPOs raised more in a single round of private-market funding than Appian did in total VC funding.Not having done an IPO-sized funding in the private market meant that Appian could come public with a more modest raise. (It took in just $75m, compared with this year’s previous IPOs that raised, on average, $190m for the four unicorns.

Since the late 2000’s, one of the cloud strategy theories was that existing telcos and network providers could become public cloud providers. Many, if not all have tried and/or trying. Thus far, it’s been a rocky road: few synergies seem to be sleeping on the ground, ready to roused up to go fight the giants, or, at least, carve out niche spaces. As summarized in a 451 report on CenturyLink:

flic.kr/p/nJR5oK There’s a few stories out about Canonical, likely centered around some PR campaign that they’re seeking to IPO at some time, shifting the company around appropriately. Here’s some highlights from the recent spate of news around Canonical. Testing the Red Hat Theory, competing for the cloud-native stack Why care? Aside from Canonical just being interesting - they’ve been first and/or early to many cloud technologies and containers - there’d finally be another Red Hat if they were public.

In a brief write-up of HPE/Micro Focus from Paul Kunert: Shareholders will also be asked to approve a $500m return of value, approximately $2.09 per share," the statement to the City added. Well, who doesn’t like money? That said, performance is declining: The [HPE Software?] business has shrunk in recent years, with turnover dropping from $4.06bn in fiscal 2012 ended 31 October to $3.19bn in fiscal 2016. Profit before tax during that period slipped too.

A brief note, from William Fellows at 451, on HSBC’s use of Google Cloud’s big data/analytical services: They have lot of data, that’s only growing: 6PB in 2014, 77PB in 2015 and 93PB in 2016 What they use it for: In addition to anti-money-laundering workloads (identification and reducing false positives), it is also migrating other machine-learning workloads to GCP, including finance liquidity reporting (six hours to six minutes), risk analytics (raise compute utilization from 10% to actual units consumed), risk reporting and valuation services (rapid provisioning of compute power instead of on-premises grid).