Digital Portfolios

Soon, almost all enterprises will be digital. The smart, quick running start-ups of today will start to feel the pains of maturity as they expand the range of activities that they are involved in and the markets in which they operate. The old world survivors which have adapted will also feel the pain as their IT or should we now say Digital Estates become increasingly complex.

As this happens, many of them will start to appreciate the need for Portfolio Management of their Digital Assets. The constant churn of Digital as Usual (DAU) means that almost everything will be obsolete or approaching obsolescence and business requirements will keep changing. Enterprises will need to continuously assess their portfolio and prioritise improvements, changes and rationalisation, as well as the response to threats and changing legislation as governments react to Digital Disruption in a number of ways.

Anyone responsible for providing Digital Services to their enterprise needs to be able to deal with this and the complexity which lies underneath, so that they can spend wisely and assign resources for the most optimum effect. To do this requires a high degree of collaboration with other business functions to ensure that a balanced and appropriate approach is taken. This is where Digital Asset Portfolio Management (DAPM) comes in.

At its very simplest, DAPM is about Business Quality, Technical Quality and Affordability. These 3 things need to be monitored and continuously managed through the layers of a Digital Product. The key layers being Customer & Business Environment, End-to-End Product Process, Applications, Data, Infrastructure (cloud, virtual &/or physical).

Changes in Customer expectations, business trends, legislation etc. can impact the overall Digital Product's relevance, market fit and legality. This is a major aspect of business quality and will imply the need for change to End-to-End Product Processes. Although Product Processes may also be impacted by other issues such as changes in volume, ability to deal with increasing product complexity, scarcity of resources or competition (in performance terms) with other products in the market place (i.e. the bench mark suddenly shifts and your process has been left behind). Likewise, applications may fail to keep pace with changing needs. Data may become corrupted or inadequate due to poor information management or a bad fit between the data and the real needs of the business. Infrastructure gradually becomes obsolete, difficult to support or integrate, and weaknesses in security become apparent.

All this gets even more complicated if mergers happen and applications become duplicated, or technical strategy changes introduce new technical platforms into the enterprise.

One of the key things that DAPM has to do, is identify all the major components required to deliver a digital product (including containers and serverless functions) and keep track of their condition and costs. This allows the calculation of unit costs to support a product transaction and enables investments to be assessed in terms of not just impact on quality and effect, but also the on cost of supporting a product.

A mature digital organisation needs to build DAPM into its budgeting and planning activities, and use this to inform its technical strategy. Alternatively, businesses with a huge legacy problem who want to transition to digital, may need to use DAPM to identify and prioritise which applications need to be retired, replaced or upgraded to enable their move.

However you look at it DAPM is an essential digital practice.

Where Next for Wearables

I recently found this article https://www.wareable.com/wareable50/best-wearable-tech-2017 on wearable technology trends, which is worth a read.

The thing I really liked was the Thynk Relax device, which basically uses electrical neck stimulation to help you relax and improve sleep. It's a bit pricey but slots alongside Dreem's headset as something which is really focused on individual well-being and in the the nature of Zeitgheist addresses one of the major issues of the age, Stress.

However overall this article gives a snapshot of where investment is going. Out of 50 (or so items mentioned) Mainstream trends are:

• Watches and Wriststraps account for 14 of the main items, all with slightly different slants.
• Smart Clothing and shoes get 7 mentions.
• VR, AR and Mixed Reality get 6 with evolved approaches to smart glasses and weareable cameras counting for another 4.
• Hearables and earbuds count for another 3 with more mentions of possibilities as well.
• Home automation platforms and static digital assistants (which are not currently weareable, but this is an obvious evolutionary path)  garner 6 mentions.

Looking across the products, one gets a sense of evolution and convergence. Some things are being simplified to address user centric design concerns (make things simple). Single use items such as payment rings probably will not survive long as products in their own rights. Products are starting to address response to human emotions, e.g. straps which detect them and home management systems which adjust the home environment to fit your mood and music/media based output to clothing. There is also a slight shift away from just sensing to doing things to you as well.

The overall wearable eco system is evolving to sense everything to do with the wearer and his or her environment, to provide him or her with information input (visual, audio, heating and cooling of clothing, and bodily stimulation), to screen out unwanted stuff, e.g. background noise, as well as issue commands to your car, devices in a smart house etc. and to provide AI based advice and updates of information from the internet.

The foundations are really being laid to get rid of the smart phone, home computer and pad, and replace them with smart clothing, jewelry and even smart temporary tattoos. Interestingly as this wave hits, Google and Apple appear to be trying to keep up, but with slightly underwelming products, and Microsoft is barely visible in the race. It's time to try and work out who will be the next consumer and design driven giant in the tech field.

The Perils of Bad AI

Everyone by now will have read that they are about to be replaced by robots and AI bots at work. Many people have also heard how employment agencies and some large firms use AI based software to screen applications and most who do complain about it.

So, recently, I tried an experiment with CV parsing software. I used a "top 10" CV parser and took some copies of a friend's CV which had been tweeked to apply for slightly different roles and submitted it to the free demonstration site.

Within hours I had received back the analysis of the 3 different CVs. I almost fell off my chair in astonishment of the analysis produced. In all cases, it cited the wrong position as his most recent one. It ignored half of his work experience, reducing over 20 years experience to just over 10. It got his level of seniority wrong and in one case it totally missed what was his major skill. In fact it bore such little relationship to what the CVs said that it could be said to be "made up" or "fabricated". It certainly was not accurate and was misleading.

I then decided to change the presentation of the information slightly in the CV. I moved his most recent role to the second page and where he listed earlier previous experience, I changed the orger in which role, organisation and date was presented. The content itself remained the same. Guess what? his most recent role was identified as an even earlier one. But it now identified the missing 10 years or so of experience. It still however did not correct the level of seniority that it assigned.

The thing which strikes me is that the agencies are using the software to deal with a problem of volume. Each advert typically attracts 300 to 1,000 applications. But they are not advising people how to structure their applications so that the software reads the CVs (or resumes) accurately. 

In my opinion, any agency or recruiter using this type of software is not going to pick the right candidates and is opening itself to breaches of data protection legislation (at least in europe) as use of any incorrect, out of date or deficient data which causes loss, harm or embarrassment is a criminal offence. Arguably, failing to shortlist someone because you rely on erroneous information would be in that category.

This shows how fragile and potentially dangerous some of the existing AI and machine learning applications are. They are powerful when properly trained and tested, but treating them as a facile magic bullet which will automate all interpretative assessment out of processes is dangerous.