ITW – interesting things this week # 2

by alexander roan

Welcome to the second edition of ITW – ‘Interesting Things this Week’ ??

In this newsletter I’ll be sharing some content from across the web that I found interesting. It’ll be loosely connected to business strategy and operations transformation, and I’ll try to avoid duplication with mainstream media.

This weeks post is around 1,000 words and a 4 minute read.

1) Too many management books!

I enjoyed Bartleby’s brief post on business books at the economist. Particularly his description of the management books piled on the economist’s desks:

Admittedly, many of these books are hard to read. The authors use “architect” as a verb and “learnings” as a noun. They randomly Capitalise every Other word. And they are overly fond of PEAs (pointless esoteric acronyms).

I think that many of the authors of management books follow a pattern of reverse-engineering an ‘approach’ or ‘method’ for success. They seem to do this based on their own anecdotal experience. They rarely consider probability or simply ‘being in the right place at the right time’. There are many ways to manage a business I’m personally wary of anyone that tries to codify it into a simple set of rules.

2) Investments in gaming and at home fitness

CB insights posted about funding increases in home fitness and gaming.

Considering these two industries together reminded me of Nintendo’s ringfit which attempts to combine the addictiveness of role-playing games with the benefits of a home workout (personal note – if I had been exercising for those few years I played World of Warcraft I’d probably be like Hercules now!) I haven’t played ringfit, but I heard it’s surprisingly fun. As people have been spending more time at home in 2020, many have noted the benefits of not having to commute to work or gyms etc. Online fitness training videos on YouTube and other platforms are very popular. I wonder if there will be other ringfit-like products. There must be a high potential to apply motion sensors, peripherals, augmented reality, virtual reality and other technologies to various different styles of exercise.

3) Skills for remote managers

The ICAEW shared a useful and concise summary of considerations for those managing teams remotely. The advice also makes sense for simply being part of a remote team. The author writes about four skills:

  • Communicate effectively
  • Provide context
  • Create clarity
  • Focus on culture

In practice managers tend to communicate based on tasks. Context and clarity is often missing. I believe that this starts from strategy deployment. Many organisations fail to deploy strategy well, this leads to a lack of context across teams in an organisation. Remote working means contact time may be reduced and the ability to have nuanced discussions over a coffee may be missed.

5) The future of maintenance

McKinsey shared an interesting article on asset maintenance.

In 2019 I had the opportunity to work with Programmed; a maintenance business in Australia and New Zealand. If you don’t work in engineering or a related disciplines you probably don’t consider asset maintenance much. It’s a massive topic hidden in plain sight. Every element of the infrastructure around us has to be maintainted. There are a lot of challenges within the industry, including things like:

  • What’s the right frequency for maintenance
  • When should we do maintenance
  • How do we handle complex or aging assets that require unique skills
  • How do we deal with assets that are widely spread out

How well you can manage these factors has a big impact on cost and effort.

Maintenance is an industry with a high level of potential to benefit from new technologies. The addition of sensors and online connectivity; aka ‘internet of things’, coupled with increasing computational capabilities means that algorithms can now better predict failures and optimise maintenance schedules. Another example is using augmented reality which can help provide instructions on maintenance of complex assets. I highlighted this in my recent article about digital too. I also thought the mention of drones for assets monitoring was interesting.

5) OpenAI releases a commerical AI based text generator

I read a Wired article reporting that OpenAI released a commercial version of their neural network-based text generator technology via an API (application programming interface).

There are many text generators out there, but this is interesting as it represents a change of stance from OpenAI who previously cited ‘fears of misuse’ as a reason not to release this technology on an open commercial basis.

What is a text generator? – given a text prompt, it will return some text attempting to match the pattern you gave it. You can “program” it by showing IT examples of what you’d like it to do; its success generally varies depending on how complex the task is. Wired explained that if you give it simplified paragraphs written for school kids, then give it one un-simplified paragraph, it will then attempt to simplify it.

The technology doesn’t work perfectly yet, but it’s very close to passing for human.

The term AI may give a false impression of what this technology does. It’s easy to assume the text output is based on some human-like intelligence.

However, that’s not correct. The neural network is trained a mass of web and digital book data. The transformations it makes are based on its training data. This means that any bias/style/inaccuracies etc. in the training data will be present in the way the output is transformed. Think widely published racism, conspiracy theories, slang etc.

Philosophically; as a human, when we write content we have the ability to think about our writing in light of the context of current events and people around us, we can question what we write as we write based on our own intellect. A neural network cannot do that, it applies the patterns that were present in past writing.

In the current climate there is a lot of media focus on overcoming biases of the past. This is at odds with the way neural networks work.

What I’ve been up to this week?

This week I wrote an article encouraging project managers, program managers and PMO professionals to focus more on strategy and people. This was based around a lot of common mistakes and efficiencies I saw in change programs in recent years.

And finally, something fun…

Media seems to have gone a bit post-apocalyptic in the past decade. During this time I highly recommend the light-hearted tropes of Korean drama. Romance, comedy and the best cliffhangers!

I recommend ‘crash landing on you’ as a starting point, which is currently airing on Netflix.

I’ll also be sending this newsletter via e-mail in future, if you would like to receive it in your inbox please subscribe:

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ITW – Interesting things this week # 1

by alexander roan

Welcome to the inaugural version of ITW – ‘Interesting Things this Week’ ??

In this newsletter I’ll be sharing some content from across the web that I found interesting. It’ll be loosely connected to business strategy and operations transformation, and I’ll try to avoid duplication with mainstream media.

This weeks post is around 900 words and a 3.5 minute read.

1) Opening an account

(by builtformars)

In this post ‘builtformars’ analyses the different experiences while opening a bank acccount across challenger and traditional banks. While the blog is focussed on UX, it’s also an interesting study on process. Whilst reading this I was thinking that a similar analysis could be carried out across various processes to easily compare customer experience across competitors. It’s a fairly cheap and low effort way to gain some insights, and isn’t often done so pro-actively.

2) Public attitudes on the fair use of data and algorithms in finance

(by the behavioural insights team and centre for data ethics and innovation)

Neural networks are the most common form of machine learning applied to decision making. These networks can; given an input, categorise it which is a kind of decision making. They do this based on ‘training data’. The classic examples relate to images. If you train a neural network with 1,000,000 images of cats from the internet the networks gain a degress of accuracy in being able to identify when a cat is present in an image on the internet. The accuracy is based on the range of cat images provided during the training stage.

Because of this training bias is big issue. Consider a neural network that reviews loan applications, it might be trained on previous loan applications and whether they were approved or rejected. Any bias in this training data will be designed into the neural network.

The report linked looks at the perceptions of the fairness of proxy information used in algorithms. If for example a neural network finds a pattern of certain postcodes having a high rejection rate, this may represent; just for illustration, an area where ethnic minorities live, in this case the postcode may be acting as a proxy for ethnicity.

The CDEI and BIT study looks at public perction on the use of algorithms. Unsurprisingly one of the conclusion is that the there is a negative perception of algorithms in loan-making.

Even though this technology is not well understood by the general public it’s promising to see that the public are cautious here. I expect that we wil see more focus on fairness and bias as these technologies find more and more commercial applications.

3) Morality in business

An example of good morals and an example of questionable morals

On Monday I read about Javier Rodriquez, CEO of DaVita; a chain of kidney treatment centres. His company automatically qualified for and received nearly $250 million from the US health care enhancement act (part of the pandemic relief package). The company and it’s directors decided to return the money as they felt they didn’t weren’t in need of it.

On the other hand, last night I read about Take-Two Interactive; the parent company of Rockstar, producer of the massively successful games Grand Theft Auto and Red Dead Redemption. Apparantly they cancelled a contract with a smaller developer; Star Theory Games, and then proceeded to poach over a 1/3 of the staff. It seems they started poaching even before the contract cancellation was finalised or communicated.

What’s the right line between profits and morality? A lot of companies incude a section on corporate social responsibility in their annual reports, but this tends to address topics like diversity, the environmen etc. I don’t see much on morality and ethics within commercials or supply chain.

4) Another failed use-case for blockchain

Recently I wrote a short article on how blockchain works. Once you understand how it works and how it compares to other technologies it becomes much easier to identify the use-cases that hold genuine value. On Tuesday I heard that Civil a blockchain journalim start up has closed down. I think this is a clear example of being excited about a technology and just looking for applications w/out really thinking about how appropriate it is and what the value proprosition is.

5) The Endangered Asian Century

I came across an interesting article from Lee Hsien Loong; no other than the prime minister of Singapore, addressing APAC and the sometimes difficult position the countries are palced in between America and China.

I remember since my University days; way back in 1996 – 2000, there was always talk of asia pacific becoming the economic super power of the world, with the west in decline. It’s interesting to see a slightly different perspective.

What I’ve been up to this week?

If you are interested in ERP or SAP I wrote about their HANA and S/4HANA products.

And finally, something fun…

When I’m working, or exercising or even taking a nap I sometimes like to play some music in the background. Recently I’ve been into lofi music. It’s super relaxing and it’s not at all intrusive so you can enjoy it while doing other things. You can look up some excellent artists like Jinfang, Tomppabeats or Nujabes or you can tune into one of the many streaming channels on youtube and let them do all the hard work of curating a playlist, I particularly like a channel called “ChilledCow”

I’ll also be sending this newsletter via e-mail in future, if you would like to receive it in you inbox please subscribe:

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Articles Technology

A No-Nonsense Guide to Digital and Technology Strategy in 2020

‘Digital’ – A Magnet for Nonsense!

‘Digital’ was and still is a popular term in the business world. In recent years a lot of papers, presentations and communities of interest have appeared on this topic. Unfortunately, the majority of them seem to create a lot of content, but little meaning. Put simply, a laymen could read a typical brochure on ‘AI’ or ‘Blockchain’ and still have no clue what the physical product or service is and how it differs from more traditional techology.

Back in 2000, I first started my career in information technology, not long after joining we were re-branded as ‘information & decision solutions’. I think the majority of people in information technology will be familiar with the constant re-branding of teams and functions.

This is the case with ‘digital’

Digital technology is a hodge podge of technologies which are new / popular / highly saleable. I tried to figure out what the common theme is with technologies that get accepted into the ‘digital’ podium, but there doesn’t appear to be one.

In this post, I’d like to have a plain english, no-nonsense discussion of digital (or more simply new & popular) technologies.

I’ll start by taking a look at what major companies are saying about digital, then I’ll look at structuring it into a simple taxonomy to promote a clearer understanding. I’ll then take a look at key areas of digital one by one. Finally I’d like to talk about the approach taken to develop a digital strategy.

Before getting into the detail I would like to give some advice to anyone thinking about investing in digital products or services:

  • Take a ‘doubtful’ stance, don’t get caught up in the hype;
  • If someone can’t explain a technology; how it works and why it’s useful, in a few sentances to a laymen, do not listen to them;
  • Be careful of conflicts of interests when dealing with suppliers and partners, I’ll talk a little more about this next.

The ‘Digital’ Cash Cow

There is no doubt that ‘digital’ was and is a cash cow for consultancies, systems integrators and other tech orientated firms.

It’s a perfect sales opportunity for these businesses:

  • It covers a broad range of products and services;
  • There is a level of mystic involved;
  • It’s fast moving, and hard to stay abreast off;
  • In some domains a high degree of technical competency is required;
  • There are stories of massive wealth / success (e.g. Bitcoin).

This creates a situation where companies want to invest in it, but they are not always well placed with knowledge and skills to plan or execute.

There is a positive role for consultancies, systems integrators, research companies and even independent contractors in helping define and implement digital strategy, but extra care needs to be taken:

  • Make sure you are not being sold nonsense!
    • Looks for clear and easy to understand descriptions;
    • Look for live examples that are delivering benefits;
  • Don’t use expensive partners for very simple technologies – Robotic Process Automation is a good example of this;
  • Don’t invest in overly complex solutions – I’ve seen a variety of ‘proof of concepts’ developed with Blockchain where a more traditional database would be much more suitable;
  • Be careful to ensure that people involved in digital strategy have the right experience and expertise. As digital and tech has become more popular it has attracted a lot of professionals who lack real experience and understanding of technology.

Let’s Look at Specific Technologies

The scope of digital is loosely defined. If I were to brainstorm a list of terms from the top of my head it might look something like this:

  • Internet of things / smart things etc.
  • Blockchain / distributed ledger
  • Artificial Intelligence
  • Natural language processing
  • Voice recognition
  • Facial recognition
  • Virtual reality / Augmented reality
  • Mobile devices – 5G etc.
  • Geolocation / maps / google earth etc.
  • Robotics
  • Next generation ERP
  • Cloud

But, as a more structured starting point let’s start by looking at what some experts say as of May 2020.

What the Experts Say

I’ve decided to look at two firms; Accenture – which can represent both a management consulting and systems integrator perspective and Gartner – which can represent a research perspective.


Accenture UK’s technology home page leads with a 2020 trends report entitled, “We, The Post-Digital People – Can your enterprise survive the tech-clash?”.

Despite the vagueness of digital, I like the use of “post-digital” in their title, it suggests a broader way of thinking than simply referring to a bundle of new technologies as digital.

Accenture start by referring to tech-lash (pushback against tech), before highlighting data to infer people are generally still positive about tech, they then coin the phrase tech-clash as a way to describe the situation we find ourselves in where tech is theoretically good, but often isn’t designed or implemented well. I like this viewpoint and I think it summarizes the one of the major challanges we face designing and implementing technology.

They go on to talk a challenge existing in how companies plan and deploy technology according to business / customer requirements etc. It reads to me that their viewpoint is that the old way of managing tech is no longer appropriate.

I am not sure about this. I think that before we say that we have to check whether a business has a formalized and effective way of managing their tech portfolio (many don’t), after we assess that we can think about whether it works for new technologies. To my mind; theoretically, methods like ITIL and COBIT etc. should work with new and old technologies alike. In fact, when you think about it, technology by it’s nature has always been new & disruptive. Can you imagine the excitment on the project to set up the first mainframes!

I would definitely accept that many technology departments have become bogged down in with too many processes / levels / standards / products etc. but this should be fixed regardless of ‘digital’.

Following this brief intro Accenture call out 5 key trends.

If I read the text and try to pull out the technology products or services I get the following:

  1. The I in Experience
    • User experience
    • Data ownership / privacy
    • 5G
    • Augmented reality
  2. AI and me
    • Automation of simple tasks
    • Collaboration between human employees and machines
  3. The Dilemma of smart things
    • I’m not sure what this refers to but it sounds like systems for subscription style products e.g. Peloton or Zipcar
  4. Robots in the wild
    • ‘Physical’ robots outside of factory / industrial use
  5. Innovation and DNA
    • Distributed ledger / blockchain
    • Artificial intelligence
    • Extended reality
    • Quantum computing

Let me critique them one by one:

1. This is pretty clear. It’s a focus on major points of importance or interest for the end-user. However I wouldn’t call this a new trend. This has always been a key area of focus for technology. The topics covered are also quite wide and don’t centre around any specific technology, plus it omits some key end-user topics.

2. The is not clear to me. I assume AI refers to artifical intelligence. Looking at the specific examples cited, automation of simple tasks is not something that would require AI (there are problems with this term that I’ll come to later). Collaboration between human employees and machines could mean almost anything related to technology, but I accept if it get’s more specific there is some really interesting stuff coming in this space.

3. I’m not clear at all what this means. If I was to guess ‘smart things’ would refer to smart devices i.e. internet of things, but the description points more towards subscription style services. Smart devices and subscription combined do open up a lot of interesting scenarios.

4. Robots in the wild is fairly clear.

5. This one seems clear, but appears to be a catch-all for other areas of interest that don’t fit the four themes above.


Navigating to the Gartner information technology home page a number of featured articles / insights are shown.

Looking through this page of trending topics the following technologies are mentioned:

  • Internet of Things
  • Cybersecurity
  • Autonomous things
  • Blockchain
  • Digital twins
  • Smart spaces
  • Artificial intelligence
  • Cloud

This is the kind of basic list that I might expect to see. And very typical of the issue of using generic terms w/out explaining what we are really talking about. The only one that stood out as less common was digital twin, “a replica of a living or non-living physical entity”. This immediately reminded me of the interesting article of how the model of Notre Dame in the computer game Assassins Creed could be useful in re-building Notre Dame following the fire damage in 2019. I’m also reminded of Elon Musk talking to Joe Rogan last week on the more sci fi aspect of digital copies of living beings.

It’s a little more challenging to critique Gartner as most of the detail is hidden behind report downloads.

For the purposes of the critique on Accenture and Gartner I am purposefulluy only looking at their high level descriptions. They should be able to clearly explain the ‘how’ and ‘why’ of their viewpoint on digital strategy to a layman on their landing page. It is arguable that if I dig into the detail I will get a much clearer view, but past experience of doing so is a hit or miss.

This particular critique aside, I’d note that Accenture and Gartner both have some excellent content and services.

Creating a Map for New Technologies

To build a better understanding of how this all fits together the first thing I suggest is to build a simple map of digital technologies that you may be interested in. I think it’s better to cast a wide net in the beginning and then eliminate those that may not be relevant to your business.

By map, the form can be a simple categorised list. There are different ways to approach this, one might be to categorise the technology by the way it impacts the user, another might be to categorise the technology by how it works or what it does.

The digital maps presented by companies are often confusing as they categorise things in various ways in one list. One minute they are looking at the end user impact, the next how the technology works.

I prefer to first categorise the technology according to how it works and then look at customer impact as part of a value assessment. One major advantage of this is that it fits well with traditional technology methods and aligns well with how systems architecture is managed.

For this discussion, I’ve 8 category buckets:

As with any taxonomy you can spend a long time debating the right categories. In my experience it’s best to draft a hypothesis quickly, debate with some colleagues and don’t be afraid to adjust as you go.

In this example I split user experience into three sub categories, I wanted to categorise virtual and augmented reality as primarily ‘visual’ ‘user expereince’ technologies.

After categorisation, we can start to note in specific technologies that we want to consider for our business. Let’s fill in the matrix with my list, Accenture’s list and Gartner’s list.

This is as far as I’ll take this taxonomy for this discussion, however for a real business I might turn it into a matrix in various ways allowing me to map e.g. benefits or business units to the technologies mentioned. I might then colour code by complexity or value etc. This should be a useful format to ensure a team / function have a similar understanding of what is being discussion.

Let’s look next at each of these categories in more detail.

User experience – touch / type

The way we interact with desktops, laptops, tablets, phones and smart watches etc. is continuoully evolving. At one extreme – smart use of touch on mobile, and at the other – more traditional technologies are investing heavily in user interface (e.g. the major ERP company SAP focussing on their customisable Fiori interface).

User experience – virtual

Augmented reality – An example I discussed with colleagues last year is a product based on glasses which can project context-relevant information. Imagine you are onboarding a new shift worker in a factory. The worker wears the glasses, then when looking at varius parts of the manufacturing equipement, the glasses overlay operating instructions or status info. This can accelerate on-boarding, reduce errors, reduce downtime etc.

Virtual reality – An easy example is training for certain dangerous or difficult jobs e.g. pilots. As virtual environments get better and VR wear becomes cheaper and more accessable I expect an explosion in this space.

Smart spaces – A smart space is simply a space which includes multiple smart devices that can connect together to give a space relevant experience or benefit. Examples include airports with facial recognition for passport control and barcode scanning for baggage handling. Or alternatively hospitals with trackers for patients and medical equipments / drugs etc.

Non-traditional databases

Databases are a broad and complex topic. Luckily most business people don’t need to a deep understanding of database technology. However as databases are being used in marketing and sales materials it’s worth investing a little time to understand the basics.

The last decade has seen something of a revolution in database design. Traditionally databases were designed to record and store primarily numerical records. Think of a list of shipments or a list of accounting entries. As IT hardware became cheaper and the internet arrived on the scene data volumes exploded and shifted from primarily numerical to a wide variety of formats; images, text documents, audio, video etc.

Databases rely on database management systems that control how information is writen and read. Advancements in the management systems as well as hardware has created a lot of new database products that have massively changed what is possible.

Big data, refers to the ability to handle massive amount of data across different hardware. This is a technical solution that can allow companies to handle these massive data volumes in an efficient way. There are excellent articles out there which outline examples such as the way Amazon set’s up it’s data centres. In a nutshell by using multiple devices cheaper technology can be used at scale rather than cutting edge expensive devices.

In-memory computing, refers to the increasingly cheap price of random access memory. This means more information can be stored and read without writing to disk. In general a huge part of the response times of computers relates to the time taking to read and write data. In-memory computing has allowed traditional systems such as ERP to become much faster. The major ERP company SAP have led with this using in-memory to develop a new database management system they call HANA. Up until recently systems landscapes have been designed with one ‘operational’ database for recording information and one for analysing information. This is because it’s difficult to optimise a traditional database to both read and write effectively. HANA is disruptive in that it can work effectively as an operational database and an analytics database.

NoSQL, refers to a wide range of new database operating systems that can handle non traditional data requirements. A popular example is MongoDB; a document orientated DB.

Distributed ledger / Blockchain, I choose to categorize Blockchain as a database as it’s a technology that essentially records information. The benefit of a public block chain network is that the information is ‘immutable’ i.e. cannot be changed. And also, it can be distributed amongst participants with no central ownership. These are great benefits and make blockchain very interesting. However these only apply to a truly public network. Many corproate applications of Blockchain are not public, for those that understand the tech they replace proof of work with proof of authority. This removes the benefit and in my opinion a traditional database would be a simpler, cheaper, and more appropriate solution.

Information Processing

I think this is the area that lacks clarity the most and is the area where we see terms such as AI or algorithms being used to make products and services seem more advanced than they are. Let’s take a look at some of the key terms:

AI / Artifical intelligence: This term should set alarm bells ringing in your head. I think it has become meaningless through application to almost any technology product. Some people will label any system with logic that replicates human behaviour e.g. IF the kettle is boiling, THEN pour the water in the cup, as AI. Other people will only consider something as AI if it can beat a human at Chess and has potential to wipe out humanity! You can’t take this as a meaningful term when considering technology.

Machine Learning: This is getting closer to a specific technology. Machine learning describes the ability of a computer system to ‘train’ itself. Machine learning is very popular in the field of image recognition. An often cited example is giving a system 1,000,000 images of cats on the internet, the system will learn to recognise when a photo on the internet has a cat in it. Machine learning is a general term that describes this, but is still not specific in how the technology actually works.

Neural Networks: This is one type of machine learning. It’s based on an attempt to mimic the way the human brain works. It’s constructed of ‘nodes’ that mimic nuerons and each carry out one simple operation. Layers of nodes can then carry out more complex operations. Nueral networks are quite interesting and worth a read.

One important thing on machine learning and neural networks is that they have to be trained on existing samples and often a very high volume. If there is any bias in those samples the neural network will build in that bias. I believe there are already examples related to insurance quotes for minorities etc. I expect to see a growing need to audit these and potential litigation here in the future.

Algorithms: An algorithm is simply a mathematical formula. If I have a small program that converts degrees fahrenheit to degrees celsius I could brand it as an AI algorithm driven solution.

Analytics: Another term that is quite often misused and can represent anything from very simple to very complex. Essentially when talking about analytics we should be referring to applied statistics and mathematics. Sometimes analytics is broken into the following:

  • Descriptive: Explain what happened and why
  • Predictive: Forecast what will happen in the future
  • Prescriptive: Understand why what is forecasted will happen.

The bottom line in information processing is to make sure to understand what specifically is being talked about.

  • If buying or building an analytics solution I want to know what specific statistical and mathematical methods and models are included.
  • If I am buying or building a machine learning solution I need to understand the details e.g. is it a neural network, how much training is required, what is the accuracy, how is biased handled etc.


Cybersecurity is a complex topic that deserves it’s own detailed discussion. Advancements in computing power, analytics and the volume of data stored in a cloud environment make it easier than ever for actors to attack private networks. With this in mind any organization needs a solid cybersecurity plan and also needs to carefuly consider the security impact of any new digital technologies brought into their network / architecture.

The best way to get a feel for the importance of cyber security is to listen to some episodes of darknet diaries


Traditionally systems are not advanced in how they manage data. A good example is GDPR which tightly controls what personal information can be held and for how long. Any systems that handles personal data has to have capability to manage this. Further to that specialized ‘data management’ systems exist that can help to manage that across an organizations technology landscape.


Internet infrastructure and standards themselves are an important enabler for new products and services covered in other areas. This can be particularly important when considering customers from different geographies and income groups where their method and quality of interent access will vary.

Internet is a key consideration for a wide range of technology initiatives such as Cloud / homeworking / offshoring etc.

It’s also particularly important when designing mobile applications. Does bandwidth support video calls, does the internet infrastructure support geo-location etc.


Different form factors create opportunities for how we use various componenets of technology with end users.

Mobile in particular has had and continues to have a hugely disruptive effect on traditional industries. Think of staffing, delivery and taxi’s. Mobile devices have allowed app based businesses to form and succeed which utilise the following capabilities in conjunction with a mobile device:

  • A customer user interface with booking / delivery requests etc.
  • A partner user interface to sort / display active requests and allow acceptance
  • In built e-contracts / legal documents where necessary e.g. staffing
  • Geo-location / map integration showing partners where to go e.g. in the case of delivery to the customers location or staffing to the work location.
  • Pay integration – ability to pay via card / paypal etc.


In the technology map I’ve considered two forms of automation

Physical robots is it’s own space and I won’t consider it in detail here.

Process automation or ‘robotic process automation’ is a fairly traditional space. There has been a recent boom in this with firms such as UiPath becoming quite successful. This is often branded under ‘digital’ as exciting and disruptive, however the technology at play is very simple.

In a nutshell robotic process automation allows you to take a set of steps a user does with one or more systems and automate it.

For example, if an accountant looks up a record, then checks the client against another systems, then say checks a rate against another systems and then approves or declines, if fixed rules for all cases can be written this can be automated using RPA.

I recall around 15 years ago we used automation tools to mass test transactions in ERP systems which more or less did the same.

I have a couple of recommendations on RPA

  • RPA itself is very simple and does not require consulting or systems integrator assistance. Companies can learn to develop RPA scrips themselves, simple training is all that’s required.
  • However I do recommend RPA work should only be considered as part of a broader process improvement initiative, there are better options than RPA in many cases e.g. eliminating the process entirely or changing underlying systems that require high volume of manual effort.

RPA could be considered as a ‘band aid’ that sits on top of poorly designed systems. It can provide a large benefit in terms of freeing up a lot of human time, but all RPA scrips will need to be managed on an ongoing basis.

Next generation ERP

Traditional enterprise resource planning software providers such as SAP, Oracle, Microsof etc. are also developing their own disruptive changes. We already touched on the HANA database which has allowed them to vastly improve their business suite product; which is now called S/4 HANA.

There are too many new and changing products in the ERP space to cover here. However a noteworthy area of interest that I would like to highlight is subscription management.

Traditional customer relationhip management systems are not designed to handle subcritpion models, however this is becoming a more and more popular way to engage and contract with the customer.

Creating a digital strategy

To cut through the nonsense at the strategy level I recommend we should treat digital strategy as no different from any other part of strategy. Innovation should be a fundamental part of strategy and digital is simply an innovation slant on technology.

Different companies do strategy in different ways. Generally speaking there is a higher level corporate strategy that will define key targets (sales, margin etc.) as well as direction for each business unit (e.g. objectives for products and customer groups).

The strategy then will typically flow down to individual business units who will create a more detailed plan that aims to deliver the goals in the corporate strategy.

Information technology should be part of the plan for each business unit (specifically how tech will support that BU), and should also have it’s own comprehensive plan.

The plan for information technology itself should deal with topics such as overall architecture, systems development and systems support etc.

When you consider this process, they key for a successful strategy is to ensure that the IT experts are correctly involved at each stage.

  • The CIO with the support of senior architects works with the Executive Committee on technology elements of the corporate strategy. This will often focus on things such as budget for major projects, new technologies to support business objectives.
  • Domain specific architects and technology product experts will work with the individual business unit leads on the business plan for each business unit, ensuring that technology is embedded in each plan.
  • Finally, the information technology strategy will involve all key leaders in information technology and bring together everything they are doing.

At each stage thought should be given to how technology can be contribute to the business. Some smart questions to ask are:

  • What technologies are our competitors investing in?
  • Are there any ‘new digital businesses’ entering our market segments (if so, find out everything about them!)
  • What direction are our existing technology partners taking with their products / services
  • What are the experts saying about our industry / geography etc.

This is a somewhat simplified view of strategy development. I would highly recommend companies take a proactive approach to optimzing strategy. If your strategy does not result in good plans for digital it’s highly likely you are missing other opportunities in the market and not addressing all relevant business risk.

I’ve seen instances of talk of creating seperate digital strategies and forming seperate digital teams. I don’t like this approach for a number of reasons. This will end up in silo thinking and silo product development. It might have to be done on a tactical basis, but I would not recommend it.

If you silo digital thinking too much the following issues may occur:

  • Your digital investments may not align well to business objectives as it’s somewhat removed from the general strategy and planning process;
  • Setting up a new ‘digital’ team is likely to result in a group of people who are biased towards digital and are more likely to invest in products that are not yet ready or have a lack of value;
  • Even if your digital investments are successful they won’t bring the rest of the business along with them.

If the existing organization lacks capability and capacity to embed digital in the existing strategy process and existing business management systems then I simply suggest adding new employees or consultants into the existing teams to beef up capacity and capability.

Those people can also create a virtual CoE on digital to bring thinking together and present summarise on the topic, but the key thing is they are embedded with existing management in all units and levels.

Dealing with digital disruptors

If you are are in an established business facing competition from ‘digital’ disrupters e.g. new app based businesses. I would recommend splitting your digital aspects of your tech strategy into two:

  • Innovation of existing products and services. This will often involve things like automation and improved analytics.
  • Development of new products and services based on the ‘art of the possible’ with new technologies.

The reason I would split this out as it may be impossible to leverage new technologies on existing processes. Existing IT architecture may also make it impossible or very expensive or difficult to change some existing processes and systems.

This may sound like I am contradicting what I said earlier. This work should still be developed and done within your existing strategy process and management structure, but the products defined should be split on this axis.

This is really an accelerator for businesses facing current or future market share loss due to disrupters.

What’s your view of ‘digital’ technology?

What technologies did I miss that you think are interesting?

What would you be interested to read more of my thinking on connected to this topic?