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!

Just how “Data Intelligent” is your company?

Whilst terms like “Big Data”, “Data Analytics”, “Business Intelligence” and “Data Science” have seemingly being around for many years, not a lot of companies have really understood the boundaries between these, and the interrelationships between them to lead their efforts in data to genuine business impact.

Business impact is the key end goal from any investment in data initiatives in your company. Whilst data exploration is always a useful exercise, if it does not lead to benefit for either your internal organisation or your customers, then it can be a waste of company resources.

Although the specific approach to the application of analytics – either through BI, Data Science, or application building – may vary according to an enterprise’s needs, it is important to note the broad applicability of BI. Its capacities are constantly expanding to include greater access to more forms of data in intuitive, interactive ways that favor non-technical users. Consequently, the business can do more with the data accessed through these tools in less time than it used to, which makes applying discovery-based BI an excellent starting point for the deployment of analytics. A nice approach outlined by Michael Li of LinkedIn here, shows an EOI model  for driving business value.

According to Gartner: “By 2015, ‘smart data discovery,’ which includes natural-language query and search, automated, prescriptive advanced analytics and interactive data discovery capabilities, will be the most in-demand BI platform user experience paradigm, enabling mainstream business consumers to get insights (such as clusters, segments, predictions, outliers and anomalies) from data.”

Data Transformation is key

Companies around the globe normally have these questions to answer: Just where is all my data? What format is it in? Can I use it? A large amount of the challenge is maximizing the business impact from your data is to understand what I like to call your “Data Atlas”. And it is normally a journey.  The larger the company, the greater the size of this challenge. Multinationals for example, being in existence for a long period of time have offended for a longer period of time, with it common to have multiple data centers, hosting strategies, database types, data types, data format, and how the data is actually used. It can be difficult for these companies to get their data into the formats required for the latest data software platforms. This can be a time consuming exercise, which can

Looking at the industry, one company that is doing wonders in solving this type of challenge for companies is Analytics Engines, based out of Belfast. Their “Fetch Do Act” methodology offers a click to deploy, end to end big data analytics platform that enables rapid transformation of your data into business insights within a single environment. Check it out here. This major advantage of this approach is that it accelerates your data transformation, so you can focus more of your time on the “Act” element. Remember Big Data is just a tool. 

Defining Data Science?

Explore. Hypothesize. Test. Repeat.

That’s what scientists do. We explore the world around us, come up with hypotheses that generalize our observations, and then test those hypotheses through controlled experiments. The positive and negative outcomes of those experiments advance our understanding of reality. Now one of the best definitions for Data Science I have come across is described by DATAVERSITY™ as:

“Data Science combines the allure of Big Data, the fascination of Unstructured Data, the precision of advanced mathematics and statistics, the innovation of social media, the creativity of storytelling, the investigation and inquiry of forensics, and the ability to use all of those skills together while still being able to demonstrate the results to non-technical audiences.”

Just like in any other Science industry, everything you do with a sample, whether it be biological, chemical or physical, is considered science. Up front analysis, sampling, applying statistics, interpreting and securing the end results.

Beware the Hype

Industry indicates that the hype curve of analytics has peaked, but as it settles, terms like machine learning and predictive analytics are coming up the hype curve, and will have a huge role to play in the coming years. But ensure you only adopt them when the use cases require them. See past the buzz and ensure your strategy takes on board industry trends, but is somewhat unique to the personality of your company. Stay focused, and ensure simplicity is at the forefront of your mind. It is also becoming easier to outsource and partner on some of these advanced methods, typing “machine learning platform” into Google will give numerous results (here).

Customer Centric Analytics

Exploration and experimentation is an important part of your data journey. The key is not to let it become all you do, and to understand the difference insight and impact. Insight does not result in improvement unless you can translate it to business impact. The “data to action” loop below does a nice job of visualizing the difference between data to insight and insight to action.

Know your customer. Every data custodian has one. The IT Manager’s customer is the Data Architect, who’s customer is the Data Scientist. They in turn must ensure they meet the requirements of the business sponsor, and having a use case to solve or KPI to meet will help you to build comprehensive return on investment (ROI) statements, and ensure a quicker acceptance in the importance of analytics in your companies business future.