Is Your Enterprise Digital Ready? - Summer Time Reading Recommendations

Sometimes it is better to make sure that other people think that they had the idea first. As Information Professionals are not always believed when they try to persuade their business colleagues that they need to change their ways, if they are going to get the best out of their Digital Strategies and Investments.

The truth is that there are some fundamental issues to address which separate Good or Average performing companies from Excellent ones when it comes to exploiting IT or Digital investments and transformations. Proponents of Digital Strategy, Agile and DevOps often raise them, but due to the fact that these issues are being raised in a technological context, non IT colleagues tend to either listen and not hear or just dismiss them as the mad ravings of techno boffins.

Key among these are:

1. Having a "Real Business Strategy" based on deep market insight and how to disrupt or exploit it in your favour;
2. Working as a Team within a healthy Business Culture;
3. Adopting Design Thinking to help empathise with customer needs, really understand what you are trying to address and then to creatively address options and iterate design to create elegant and well targetted solutions rapidly, whilst embracing leraning from failure as a critical part of the approach;
4. Systems Thinking to understand the end-to-end process, identify and manage critical business bottlenecks and organise around product or work delivery, instead of hierarchical functional silos.

A large part of this is really concerned with taking an ego-less multi-functional team approach to addressing what is really needed and then pursuing continuous delivery, automation and improvement in small steps. 

Fortunatley there are some great business books on some of these subjects which evryone should be encouraged to read as they address the issues from a more general business perspective and introduce the key concepts that all the Digital Geeks are so keen on.

My recommendations are:

Good Strategy Bad Strategy is a great expose on how to do Strategy properly. Most readers will recognise many of the bad examples which are lacerated by Richard Rumelt (one of the leading fathers of Business Strategy) in what is a fairly easy and entertaining read.

Winning Teams Winning Cultures  - addresses a lot of the key issues in building a positive enterprise culture. This comes from Larry Senn (of Senn Delaney a culutural change consultancy) and Jim Hart who are long time practitioners in the field of cultural change. It's an interesting book, because just like democracy it is difficult to bomb culutural change from 40,000 feet into an organisation. It requires authenticity, long term commitment and sytematic sweating by the management team to achieve.

The Human Constraint - the author (Angella Montgomerry) takes the principles advocated by Demming and Goldratt and updates them to apply to all businesses (not just manufacturing) in the practical adoption of system thinking and the Theory of Constraints to business in general.

Finally there are plenty of sources on Design Thinking available on the internet. The UK Design Council publishes the Double Diamon model which provides a simple model for explaining the process. I recently saw a great webcast by Ileana Stigliani, fom Imperial College Business School, on the subject Unleash Innovation Through Design Thinking

(see: https://www.ivyexec.com/professionals/classes/details/unleash-innovation-through-design-thinking ).

Digital Adoption Framework

A lot get's talked about Digital, but there are few comprehensive approaches to adoption available for reference. 

This is why I was interested when I came across Vadrim's DAF diagram, reproduced below. Enjoy!

Can Machine Learning Save Big Data?

Business Intelligence (BI) and Big Data (BD) are supposed to help enterprises improve their overall business control by identifying trends, anomalies and insight into what is really happening, so that they can take appropriate action to correct problems and address opportunities, as well as predict what might happen with a degree of informed confidence. Yet most businesses fail to either exploit BI and BD solutions or even if the do just don't get the expected returns. 

One of the biggest problems facing anyone trying to do Business Intelligence or Big Data is quality of data. In some cases, someone has just chosen the wrong source of data (a Master Data Management issue). An example of this that I fell over many years ago was that an organisation tried to build a customer database for Sales and Marketing, using financial sales ledger data. This caused immense problems because Finance is not interested in the same sort of data as Sales and Marketing and don't care who the key contacts are or what the organisdation structure is, as long as someone pays the invoices.

However, an overwelming problem is that the data is dirty. Legacy systems usually were not designed with BI data in mind and external sources used for BD usually involve different terminology and definitions when discussing or dealing with the same thing. Alternatively, the same terminology may be used to use completely different things by different people or organisations. Furthr issues arise when users are asked to input data which is of no utility to them into a system. The act of inputting the data just complicates their day job without helping them do it. So they often provide default information. Additionaly, other forms of context may inform what was meant by an iitem of data or a vague description.

So this often reduces organisations to using a Subject Matter Expert (SME) to manually interpret data and stick everything onto a spreadsheet to conduct a particular analysis. This is costly, labourious and slow. It may even be innacuarate as SME fatigue (or conflicts in opinion between different SMEs) leads to inconsistency and errors in interpretting data. So many analyses just don't get carried out or produce information too late,  lack much detail beyond generalistic interpretations or may even be suspect in accuracy.

In some cases, if the analysis is likely to be needed frequently with new or updated data sets, it may be possible to write programs with interpretation rules to deal with many of the problems mentioned above. However this is painstaking and laborious and often requires continuous maintenance, constricting its viability for many analyses as new costs and delays are built in.

Machine Learning however may offer an answer. If you can get an SME to provide some examples and use them to train a neural network, you can quickly develop the facility to improve data quality. The machine learning tool should clean up data where there are clear rules and identify areas where there is uncerainty or conflict in interpretation. This requires a relatively small amount of effort to put right and then improves consistency. The more data and examples you feed at a machine learning system, the better it becomes, so you get ever improving interpretation of dirty data. This is quick too. 

I found this out from experience, when in a previous company I worked in, we used machine learning to interpret data readings and categorisation of test results for thousands of readings taken of ship hull  plate thickness around different parts of the vessels being inspected, so that they could be analysed by structural analysis experts. A manual process which used to take a highly skilled engineer several days to complete (without creating any value) was reduced to less than 20 minutes and improved accuracy. This meant improved job satisfaction, higher productivity, better quality of output and a more responsive (i.e. quicker) service for the client.

So there you have it. Machine learning can bring some very quick gains with quite simple applications. You don't need complicated Deep Learning solutions to deliver value and it can vastly improve your BI and BD efforts where you have to live with dirty data.

Gin & DevOps

I went to an IT event the other night for CIOs and other senior IT executives. This was a networking event centred around a couple of talks and a Gin Tasting. This was hosted at a leading financial Institutions centre for showcasing Innovation and Digital Applications, near London's hot bed of FinTech start-ups in Shoreditch..

The Gin Tasting was good and informative. So were the talks on Leading Change and DevOps. They were good too, interactive and got us going.

The thing which struck me though, was that although everyone was keen on change, digital technology and agile, few of them had any awareness of DevOps. When asked whether anyone had heard of the 3 Ways, only 2 people had. Only one person had read the Phoenix Project or heard of Goldratt and the concept of managing bottlenecks in a process or even Lean Concepts.

Given the general acceptance that DevOps is moving into the mainstream, this was quite a surprise. Digital without Agile & DevOps is like a G&T without the tonic.

Complacency at AWS Summit

Sitting there last week at the AWS Summit in London, I was struck by how similar Werner Vogel's message was to Googles at the GCS Next conferenece. It was just that AWS's CTO was far less punchy in his delivery and much more obsessed with technical detail. At the same time there was no recognition that AWS might actually have competitors. 

It is true that Amazon, with its AWS service, is far in front of Microsoft and Google in terms of richness of its technical offering and market penetration, but at the same time this gap is closing. Recently, I saw a great conversation on LinkedIn started by a recruiter for cloud specialists who had noticed a growing demand for Azure specialists, having spent the previous year only recruiting AWS staff. This shows, that in large corporates at least, a dual market is naturally emerging as Microsoft leverages the installed base of AD and Office products to move them to O365 and Azure. Also, I did not see the sort of financial innovation that Google is bringing to the market with its highly flexible pricing options.

In fact it took me a while to identify what differentiates AWS from its closest competitors. As much of Werner's pitch was spent identifying improvements to current offerings within its services. The things which stood out were:

• AWS has more virtual server type offerings than its competitors, with some services now into their 5th release or so of maturity. Although this comes at the downside of increased complexity and need for an intepreter to translate for the uninitiated as many of the services are labelled in the format AN (A = a character and N = a version number) in the manner of a technical labelling convention rather than a meaningful name.

• AWS is now offering "serverless computing" via its Lambda service using functions instead of virtual servers. Whilst this obviously offers a great deal of agileness for rapid delivery, it may be risky in terms of encouraging sloppy development resulting in monolithic resource hungry applications which are expensive to run and difficult to maintain. It also may present a new form of vendor lock in risk, as migration to other cloud platforms in the future would be less easy.

• AWS is now offering a niche service for FPGAs. Which allows a mature and tuned application to be "burnt into hardware" with corresponding double digit improvements in performance. This may be important for people with well proven machine learning applications

• The suite of security services available is also impressive with extensive DDoS capability embedded as standard.

• AWS now has a UK point of presence and others are being established throughout Europe to ensure that data privacy and export sensitive security concerns can be addressed. Although global coverage is still a little patchy and the Middle East in particular seems poorly served.

• AWS's IoT offering also appears to be maintaining its edge. It's still the only one to have a rules engine built in and additionally, Amazon is now launching a standalone environment which can be run on the Things (i.e. the intelligent devices which are networked in IoT networks) which is consistent with the rest of the service.

For me the most interesting part of the day was visiting the start-up partner stands and a presentation on Amazon Launchpad. Launchpad is its service for helping "ready to go to market" startups to promote themselves and gain extra visibility in the online market place, coupled with help in delivering their on-line presence. This is currently focused on "tangible product" companies, but may be extended to services in the future. Noticeable amngst them were Cocoon (a company which provides remotely manageable security devices based on infrasound), Beeline (a company which provides simple digital navigation devices for bicycles) and Roli (a company which makes electronic music generation simple and accessible).

So, to recap and summarise, AWS maintains its technical edge, but appears over comfortable and complacent. It needs to hone its marketing and value proposition to continue to remain relevant in the future.