One of the advantages of being CTO for a digital services business is that I get to spend a lot of time talking to executives about technology in a wide variety of organisations, across a disparate blend of industries and scales. A consistent set of trends and challenges always emerges. Here’s some of the highlights…

Data integration.

Perhaps top of the list is data lakes, warehouses and marts, which have been a hot topic for years. But this year with the large uptick in adoption of cloud native data platforms such as Databricks, the scope and shape of these initiatives is changing.

Companies are starting to focus on more discrete data integrations, taking old stand-alone data and finding faster, more meaningful ways to integrate it and leverage it in decision making.

While the focus of the past few years has been backwards looking, often dealing with purely historic data for reporting purposes on slow time cycles, there’s a move towards more real time and transactional integrations – which can clearly be seen in the rapid shift in technologies and approaches in the data engineering world.

Omnichannel.

The advent of data integration has put analytics firmly at the core of the omnichannel business. The tipping point has really been passed and omnichannel expectations are no longer just for the early adopting customer. From coffee shops to tool outlets, expectations for a seamless transactional experience from digital to the real world are now the baseline.

Customers no longer want to deal with paper receipts or reward cards, instead expecting an integrated experience that doesn’t require them to manage physical artefacts, but equally doesn’t use ‘digital’ as an excuse to reduce the number of human interactions that are necessary to feel connected to a brand, product or service. Omnichannel is here to stay and is a baseline requirement for any modern retailer,  driving deeper, more transactional data integrations, as well as demanding strategies for optimisation.

IoT, 5G & Digital Twins.

The promise of IoT and 5G is also nothing new, but it continues to be a hot topic in 2022. 5G coverage is just starting to reach meaningful levels, and the heady early days of IoT gadgets of questionable value are behind us. Companies are starting to look more seriously at industrial IoT, and in the consumer IoT space, the focus is moving away from flashy features to learning how to leverage IoT for operational and logistical support.

IoT platforms on the cloud are largely commoditised, and hardware costs (chip shortages aside) at an all-time low.

One of the most promising outcomes of this is the rise in Digital Twins. Creating digital clones of real-world systems is in its infancy, so this may take several years to play out. But the implications, especially for large scale operations and infrastructure, are huge.

Automation.

The transition to hybrid working is an area that has been perhaps overlooked in terms of its  impact on automation. Face-to-face interactions get augmented with technology, process, more structured meetings – which naturally lend themselves to enabling automation.

However, the rise of digital tooling is increasing automation and transactional-level integrations. From the most basic no-code and low-code solutions like Power Apps right through to AI driven hyperautomation, it’s clear that this is an area that is continuing to grow.

Cloud & service-oriented architectures.

There’s no longer a question about cloud. It won the battle. Huge investment in the move to cloud resulted in a lot of lift-and-shift of systems into virtual machines. As cloud services like Azure have matured, for many organisations who have already invested in cloud infrastructure, the question is ‘now what?’.

The answer is to leverage the power that cloud native services provide – to modernise and transform systems and to explore potential value. A move towards service-oriented architectures and a more coherent approach to technology across your organisation is certainly worth evaluating.

As a result of these trends, one directly correlated impact is the continued war for tech talent. Although the level of new entrants in the industry is increasing, a lack of experienced seniors who can guide and support technical decision-making around it, can inhibit organisations from moving more quickly with the confidence to drive real value from technology evolution.

Got a challenge in mind or keen to discuss the latest trends further? We’d love to hear from you.

Download this infographic to determine the right approach to modernising your legacy apps and systems based on your time, budget and digital priorities.

A Proof of Concept (PoC) is a high-value component of software development projects.

It’s a time-bound, fixed-scope exercise that tests the feasibility of a technical concept using measurable goals and success metrics. PoCs act both as a way for organisations to discover and learn about new technologies, and a way for organisations to manage the risks associated with exploring new technologies. However, as with any project, PoCs entail inherent limitations and pitfalls that can create cost, time and resource implications. Here’s my top 3.

1. Lack of business readiness.

One of the main pitfalls with a PoC is the inability of the broader business to adapt to the new capability it demonstrates. In practical terms, this can mean that a level of business transformation is often required beyond the original scope of a PoC, to adopt to a new technical system or process. It may be as simple as individual employees changing their workflows, or as complex as reconfiguring multiple business units. This can lead to a sort of ‘false positive’ from a successful PoC: the concept may be technically feasible, but broader adoption may present obstacles.

It’s vital to consider business process change when implementing a PoC into production, and is a crucial consideration in how an MVP (Minimal Viable Product) should be scoped.

2. Defining the wrong success metrics.

Technology is just a tool. Selecting the right tool for the right job is dependent upon having identified the right job to begin with.

Creating a PoC to answer a broad technical question - rather than applying technology to identify a specific business question - is a common pitfall. Consider the statement:

“Let’s see if we can use Machine Learning in Azure to help our business”

This is a difficult thing for a PoC to prove as it has no clear outcome, and it pre-supposes that Machine Learning is the right tool to solve this undefined need. Instead, we could add clarity to our goal by reframing:

“Let’s see if we can use Azure Data Services to help reduce time spent producing financial reports.”

This is a good starting point, and we can quickly refine the key technologies that need to be proved and simple ways of measuring the factors that define success. Good problem statements for PoCs are as specific as possible, often dealing with the viability of very low-level technical challenges such as integrations or data migrations with very narrow success criteria.

3. Building a PoC for production.

It is common wisdom that a PoC should not be put into production. Although this is almost completely true, it is an often-misunderstood statement. A PoC is immediately compromised if it is built to be productionised. This is because the additional considerations and work entailed in creating a production-grade solution adds significant overhead without knowing if the solution is even feasible, often increasing costs and risk against an uncertain outcome. However, if a PoC is deemed a success, that does not mean all the work undertaken needs to be thrown away by default. It means it can be objectively evaluated, and any viable component parts can be reused and carried forward into a follow-on project to build a production-ready MVP.

Moving to a production-grade solution means a much greater focus on code quality, reusability, security, testing and NFRs (non-functional requirements) among other things. Whilst all imperative in production systems, these will often add little value to the goals of a PoC. However, it is always wise during development to adhere to best practices to maximise the potential for possible expansion and refactoring, preventing the solution from becoming entirely disposable.

A good PoC will be rooted in a business value-based outcome and undertaken to prove that the proposed capability through a new application of technology or data is not only realistic, but the proposed approach is also feasible.