Releasing Software Developer Superpowers

Article is aimed at anyone looking to gain the edge in their software development team creation or advancement in the digital age. Concepts can be applied outside of sw dev at some level. Open to discussion – views are my own.

UX is not just for Customers

User Experience is an ever growing component of product development, with creating user centric design paradigms to ensure that personalisation and consumer/market fit is achieved. From a development team view, leveraging some of the user experience concepts in how they work can achieve operational efficiency, to accelerate product development. For example, how is the experience for each of the developer personnas in your team? How do their days translate to user stories? Can interviewing the development community lead to creating better features for your development culture?

Build Products not Technology

Super important. Sometimes with developers, there is an over emphasis on the importance of building features, a lot of the time for features sake. By keeping the lens on the value or “job to be done” for the customer in the delivery of a product at all times can ensure you are building what is truly needed by your customer. To do this, select and leverage a series of metrics to measure value for that product, along with keeping your product developent in series, and tightly coupled to your customer experience development.

Leverage PaaS to deliver SaaS

This sounds catching but its becoming the norm. 5 years ago, it took a developer a week of development time to do what you can do in Amazon Web Services or Azure now in minutes. This has led to a paradigm shift, where you being to look at the various platforms and tools that are available to enable the developers to deliver great products to customers. Of course, there will always be custom development apps, but you can help your developers by getting them the right toolkit. There is no point reinventing the wheel when OTS open source components are sitting there, right? Products like Docker and Spring and concepts like DevOps are bringing huge value to organisations, enabling the delivery of software or microservices at enhanced speed. Also, the balance between buying OTS and building custom is a careful decision at product and strategic levels.

“The role of a developer is evolving to one like a top chef, where all the ingredients and tools are available, its just getting the recipe right to deliver beautiful products to your customer.”

Create Lean Ninjas!

Evolving the cultural mindset of developers and the organisation toward agile development is super important. Having critical mass of development resources, plus defined agile processes to deliver business success  can really reshape how your organisation into one where value creation in a rapid manner can take place. However, its important to perform ethnographical studies on the organisation to assess the culture. This can help decide on which agile frameworks and practices (kanban, scrum, xp etc) can work best to evolve the development life cycle.

Implement the 10% rule

Could be slightly controversial, and can be hard to do. Developers should aim to spend 10% of their time looking at the new. The new technologies, development practices, company direction, conferences, training. Otherwise you will have a siloed mis-skilled pool of superheros with their powers bottled.

However, with lean ninjas and effective agile company wide processes, resources and time can be closely aligned to exact projects and avoid injecting randomness into the development lifecycle. Developers need time to immerse and focus. If you cant do that for them, or continously distract them with mistimed requests – they will leave. If you can enable them 10% is achievable.

Risk Awareness

We are seeing an evolution in threats to enterprise all over the world, and in a software driven and defined world, getting developers to have security inherent design practices prior to products hitting the market can help protect companies. Moons ago, everything sat on prem. The demands of consumers mean a myriad of cloud deployed services are adding to a complex technology footprint globally. If they know the risk landscape metrics from where they deploy, they can act accordingly. Naturally, lining them up with business leaders on compliance and security can also help on the educational pathway.

Business and Technology Convergence

We are beginning to see not only evolution in development practices –  we are also seeing a new type of convergance (brought about by lean agile and other methods) where business roles and technology roles are converging. We are beginning to see business analysts and UX people directly positioned into development teams to represent the customer and change the mindset. We are seeing technology roles being positioned directly into business services teams like HR and finance. This is impacting culture, wherby the saviness in both directions needs to be embraced and developed.

Growth Mindset

We mentioned mindset a lot in the article. That because its hugely important. Having the right culture and mindset can make all the difference in team success. As Carol Dweck talks about in her book “Mindset”, you can broadly categorise them into two – growth and fixed. This can be applied in all walks of life, but for team building it can be critical.

In a fixed mindset students believe their basic abilities, their intelligence, their talents, are just fixed traits. They have a certain amount and that’s that, and then their goal becomes to look smart all the time and never look dumb. In a growth mindset students understand that their talents and abilities can be developed through effort, good teaching and persistence. They don’t necessarily think everyone’s the same or anyone can be Einstein, but they believe everyone can get smarter if they work at it.

Creating a team where being on a growth curve and failures are seen as learning can really enable a brilliant culture. As Michaelangelo said “I am still learning”. Especially as we evolve to six generations of developers. How do we ensure we are creating and mentoring the next set of leaders from interns through to experienced people?

Check a Ted talk from Carol here – link.

And most importantly … HAVE FUN!

IoT and Classical Business Models

Many companies, especially in the Information Technology (IT) section are aware of the IoT explosion, one of the biggest challenges facing any company is how they prepare for the change that will result from the increased business impact that IoT will present.

With figures in the trillions in terms of the market for IoT, how do companies ensure they can get a slice of the pie? If they currently do not sit within the relevant market segment, analysis will be required to determine if it can be an opportunity or a threat to their business as a whole.

IDC in 2014³ predicted that IoT will actually overtake the Information Communication Technology (ICT) over time. It predicts IoT will grow 12% year on year, whilst classical ICT will grow just 4%. Figure 3 below illustrates this.

Considering that most business is consistently monitoring the bottom line, it is not only the opportunities that it will present, but how it will impact how we work. With limitless numbers of sensors monitoring processes, improving business energy efficiency, enabling new ways of working in teams, business will need to be more open to change, and more dauntingly, open to the elements of a “big brother” type scenario.

There are trends that are ensuring an evolution of business practice as we know it. Normally, new technology platforms impact on a single strand, with the exception of the impact of the internet. But IoT has the potential to become an entire business ecosystem, where creating and capturing business value will be paramount. However, this is not a straightforward suggestion. Barriers to this include the current early position of IoT in its lifecycle, and the sheer volume and types of devices to be considered. From an ecosystem perspective, by nature it would indicate a seamless quantity of micro-systems working together in a self-sustaining fashion. Trying to estimate what this will mean for IoT is still not clear.

Consider the classical technology adoption lifecycle. There are five types of innovation adopters, the first being the innovators themselves. The list is completed, in sequence by early adopters, early majority, late majority and laggards. With the current immaturity in IoT, and the lack of clarity in the various emerging technologies, the challenge for business is to try to advance the early adopters to early majority, so the business needs to be able to scale. The early adopters are less fussy when it comes to product design, but once the number of adopter’s increases later in the life cycle, the early majority will want polished product offerings, with appropriate services.

With the IoT still in its relevant infancy, it is appropriate to compare it to the early stages of the Internet. When we look at the recent business ecosystems that have been spun out of the Internet for EMC, such as Pivotal Cloud Foundry, one would postulate about future ecosystems opportunities for EMC from the IoT spectrum.

Another important consideration for companies is to consider the skill-sets and people that are required to drive their Big Data strategy as a result of their growing IoT ecosystem. A key tenant for this will be the data itself, and in the February IT@Cork Tech Talk by my EMC colleague Steve Todd, and even more recently in his blog on data value (value was something I had never associated to data until this talk), Steve spoke to the importance for major companies to begin to consider a more structured approach to their employees that are involved in data set discovery, identification and migration (Data Architect) and also a Chief Data Officer to represent the company from a data perspective. Interestingly, my role in EMC changed last year, to the role of a Data Architect. So I could first hand relate to this. When faced with a business challenge in big data, 5 steps that can be critical to success are as follows.

1: Demystify and then map the current devices, tools, processes and trajectory of data across the business unit or company (AS-IS Diagram)

2: Scour the company and external sources for any technologies that can enable a more scalable and clearer approach

3: Look to centralize data storage, to allow the company to focus on being agile and scalable, and also remove duplicate data (concept of a Business Data Lake)

4: Develop an ingestion framework to ensure the data lake has a sufficient landing platform for data.

5: Build the Analytic’s platform that is pointed at the centralized “Business Data Lake” to meet existing and future needs of the business.

When we apply this to IoT, we start to that every company, no matter how small, will begin to generate huge data-sets, and there will be a new skillet needed at companies that never had previously to ensure they can gain as much insight from the data sets. Sure, there are companies that can provide these solutions, but realistically, the future state will surely be to have these as core skills, just as “internet skills” once appeared on resumes?!

It is proposed here that key stakeholders across multinationals can overcome these challenges and design practical IoT business models if they consider an ecosystem style approach, instead of looking at modular needs of individual business units. This will allow the business to get a high level perspective of where IoT can bring value to their business offerings.

Reference:

3: Digital Universe Article

http://www.emc.com/leadership/digital-universe/2014iview/internet-of-things.htm

An IoT Data Flood. Are we ready? (Intro)

A flood of 50 billion pieces. That’s the predicted number of internet enabled devices that will span our globe in 2020 to create the expanding Internet of Things (IoT). And it is a conservative estimate, when you consider other types of technologies that could be enablers, namely Near Field Communication (NFC) and Radio Frequency Identification (RFID). The speed of internet connectivity in the future is likely to hyperscale, and makes Moore’s Law, which we successfully navigated, seem tortoise like.

So what will all this mean? Crop fields will be smart. Crime will diminish. Stagnant business models will become fluid. Heck, we may even predict the weather. But one thing will remain: The need to collect, store and analyze this data. The coming years will see a dramatic and disruptive innovation of the classical data center model as we know it. It will not be practical for all these remotely distributed devices to transfer their data to centralized data centers. In recent years, data center consolidation has accelerated, yet it does not fit well with IoT. It is proposed in this article that a single person’s life and home of tomorrow will generate more data than the industrial plant of today.

Whilst estimating the impact of the Internet of Things (IoT) over the coming decade would be difficult to do accurately, one thing is apparent. It is going to be a game changer. With the number of devices , the ability to connect, communicate and remotely manage these automated devices is becoming an enabler, from the parking lot to the factory floor to the homes we live in.

A critical enabler for IoT longer term is the concept of smart cities, where both human centric wearables and machine sensors will work together to make the cities of tomorrow more efficient, secure and safe. By 2050, it is predicted that two thirds of the world population will live in cities. This migration naturally represents great challenges especially in healthcare, security and energy use.

Sogeti², a global collection of over 120 technologists, makes an excellent association between smart cities SMACT (Social, Mobile, Analytics, Cloud and Things) and the concept of a platform. The City as a Platform is twofold: it is the infrastructural capacity plus the human dimension, the empowerment of behavior via data and applications. It shows that the digital architecture of a city is beginning to look like a platform with various abstraction layers that support one another. There are 11 scenarios in which a city can become smarter: waste, healthcare, grids, retailing, supply chains, tourism, e-government, smart meters, food, traffic and logistics management.

From Figure 2 above, the top shows the activities of everyday life, with citizens, students, consumers and commuters. Below this is an abstraction layer containing technology such as an Application Programming Interface (API). Streets become smart if we can link camera systems with facial recognition technology. As you traverse the layers, you will notice common elements of any platform, with communication and/or collaboration between these layers. These are already in action, apps like Air B&B and Halo/Uber show that smartness in applications can make cities more efficient in regard to transport and space respectively.

Many people own internet connected devices, such as their smart phone, laptop or smart TV, but this is the beginning of an age where technical advances and cost reductions mean elements such as baby monitors, fridges, temperature sensors, in-home heating and lighting will all be connected. The list of devices is growing all the time. But if we stop and think about what these devices mean for the classical data center model, it soon becomes apparent that this deluge or flood of data will impact data storage, processing and analytic platforms that we use today.

The strain is evident already, and that is with the devices that we control (laptops and phones generating data by surfing the net for example). It’s still just two devices per day per person. Imagine if that number increases over 50? Imagine the data load and bandwidth implications when those devices are sending data regularly? Then consider an entire city of people with the same level of internet connected devices, leading to billions of devices generating vast quantities of data which must be processed and stored. Understanding this impact is important if you are to ensure that your infrastructure is correctly designed to support an IoT strategy that your organization will need to remain competitive in the coming decades.

In my next blog post, I will explore the impact of IoT on Classical Business Models. Stay tuned!

References:

1: Connectivist Chart on IoT Growth

http://www.theconnectivist.com/2014/05/infographic-the-growth-of-the-internet-of-things/

2: Sogeti Labs: City as a Platform Article

http://labs.sogeti.com/internet-things-cities-platform/