Analytics, Cloud and the modern CIO
By: Thomas Keil
Tags: AI, Study, Cloud Computing, Cio, Analytics

On Twitter I came across a comment from Kerem Tomak of Commerzbank: “When you talk about machine learning and AI, you have to talk about cloud! It’s the key enabler of Advanced Analytics and AI.

My favourite private clouds: Easter Holiday 2019 in Brittany

I have not been able to track down Kerem’s entire lecture, but I certainly agree with that statement. It’s in the nature of things that analytics applications benefit from the cloud: you need a lot of computing power in the model training phase and later in production, depending on the scenario. But in between, analytics applications can be built in the workplace.

Let’s think about an example of this. Everyone knows the section of a website that says: “Other customers are also interested in…”. These recommendations are based on the analysis of previous purchase decisions. A recommendations engine predicts the likelihood of buying one of the products recommended based on a customer’s previous behavior on the website. This algorithm has been “trained” on many, many purchases in the past, and the better approaches even “learn” in real time using vast quantities of data about large numbers of individuals.

Not every IT effort is the same

Many functions on shopping platforms are relatively simple and not personalized (for example, the product description or the “buy now” button). Recommendations, however, require more IT and technical effort, because every single website visitor needs their own individual recommendations. You can just imagine the algorithm’s workload just before Christmas! Realistically, though, is there anything you can do to help manage demand? I think there are two possible strategies:

Strategy 1: More hardware for everyone. You add new machines and stronger connections to handle the load. Amazon, the largest online retailer in the world, did just that, years ago, and built huge amounts of computing power to cope with peak loads. At lower demand times, Amazon leases these resources to other companies. That’s how Amazon Web Services, now one of the largest cloud services providers in the world, came into being.

Strategy 2: You focus on certain parts of your website. You pack particular bits of the website, such as the recommendations engine, into a container that you can copy and boot up hundreds of times if necessary. This container technology is now common in many companies and makes life much easier for hard-pressed IT teams.

Priorities shift

But what does that mean for the operator of the infrastructure, such as the CIO? Historically, this strategic choice was a matter of negotiating with server manufacturers about servers and computing power. Now, however, service quality, availability, elasticity and suitable pricing models are more important.

In a world of digital transformation, with countless IT applications and enormous amounts of data, the “modern” CIO (and in a non-representative survey, all the CIOs surveyed felt that they were included in this group) therefore cares about four basic fields of action:

Scalability : Useful insights and technologies that deliver real value must be quickly scaled by small test installations as needed. It is no use having a new recommendations engine that works convincingly better, but can only manage a fraction of requests, because the hardware does not work properly.

: Useful insights and technologies that deliver real value must be quickly scaled by small test installations as needed. It is no use having a new recommendations engine that works convincingly better, but can only manage a fraction of requests, because the hardware does not work properly. Innovation culture : Ideas are in demand — and must also be tested and validated or abandoned rapidly (“fail fast”). A thought that pops up on Monday should have been tested by Friday. Companies that are agile have a real advantage in the market.

: Ideas are in demand — and must also be tested and validated or abandoned rapidly (“fail fast”). A thought that pops up on Monday should have been tested by Friday. Companies that are agile have a real advantage in the market. Control and security : This may at first seem to contradict the culture of innovation, but companies do not gain anything if smart employees implement smart ideas using illicit or incorrect data. IT teams need to clarify sensitive issues in advance and offer a secure data management framework. This means that the burden of the admissibility check falls away from the business units, and IT can ensure compliance with governance requirements. To use a metaphor, the toy box in the nursery will contain only plasticizer-free, educationally-valuable toys, and the children can play with them as much as they like.

: This may at first seem to contradict the culture of innovation, but companies do not gain anything if smart employees implement smart ideas using illicit or incorrect data. IT teams need to clarify sensitive issues in advance and offer a secure data management framework. This means that the burden of the admissibility check falls away from the business units, and IT can ensure compliance with governance requirements. To use a metaphor, the toy box in the nursery will contain only plasticizer-free, educationally-valuable toys, and the children can play with them as much as they like. Customer focus and co-innovation: Yes, of course, we all care about the customer. But do we really? At the beginning of a design process, have you ever asked your customers what they want? Data-driven products and services are suitable — if the infrastructure is right. Business users are closer than IT teams to their customers’ needs, so it may well work better for them to be able to start up their own applications, and draw down the data they need. Analytical models such as recommendations engines need data scientists and programmers, but, if possible, in collaboration and on the same platform.

The importance of change

This may sound easy and natural, at least in theory. In corporate reality, however, CIOs face a variety of “practical constraints”. They have fewer people, higher pressures, and new technologies, and it is not easy to switch from managing a shortage to shaping the future. This move will only succeed if the company management actually “switches” its approach too.

Fancy a chat?

I would like to find out if these theoretical considerations are accurate. For example, is it really about scalability? What role do innovation labs play? What about the corporate culture? I am looking for people to talk to for a global study on innovation at scale. Do you have time for a one-hour conversation? Then please contact me: thomas.keil@sas.com. I hope to be able to get Kerem Tomak involved too!