When it comes to data projects, it is crucial to start small and remain focused. Trust us; we would know. This approach aligns with the principles of the Microsoft Agile methodology. Emphasising regular releases and frequent testing—a practice equally vital in the Data Landscape as it is in Technology or Business.
Data Complexity
Data, known for its inherent complexity, witnesses rapid advancements in techniques and technologies. Amidst industry buzzwords and news about AI and ML, individuals often need help comprehending and extracting value from these concepts.
Set Achievable Goals
Clients often struggle to articulate goals clearly, and even when they can, ideas are often open to interpretation and change. This is why providing them something tangible to review and provide feedback on rather than attempting to shape everything all at once is incredibly valuable. It is common to end up with a product quite different from the initial vision. But this often results in a solution that better fits the client’s needs.
Two exercises that would be useful at this stage would be to conduct a design led ideation workshop and technology architecture session.
Design Led Ideation Workshop
Design led ideation workshops are conducted with the stakeholders. This involves understanding what is inhibiting the business today as the first part. This means defining the core problems from each stakeholders point of view (often this brings a sense of empathy from different points of view). This is then followed by ‘ideation’. The focus here is to brainstorm a range of creative solutions to the problems identified by the previous part. Remember, no idea is too outlandish at this point. The outcomes of these two sessions will be:
- Clearer view of business challenges across various business units
- Understanding of how these challenges affect stakeholders (customers or internal stakeholders)
- Possible solutions to the challenges raised throughout the workshop
Technology Architecture Session
A technology architecture session is an in-depth discussion and planning meeting focused on the design and implementation of a flexible AI architecture that can utilise available data sets.
Identify Data Sources and Understand Data Flow
The first step in developing a technical architecture for AI integration is identifying your data sources. This could include databases, real-time data streams, third-party APIs, etc. Understanding the flow of this data within your organisation is crucial to effectively designing your architecture. You’ll need to map out how data is collected, processed, stored, and accessed. And ensure your AI solutions can interact with this data seamlessly.
Design a Modular and Scalable Architecture
Your architecture should be designed in a way that allows for the easy addition or removal of components without disrupting the entire system. This is particularly important in the rapidly evolving field of AI, where new tools and technologies are constantly emerging. Your architecture should also be scalable, able to handle increasing volumes of data or more complex AI tasks.
Ensure Interoperability and Avoid Vendor Lock-In
Use open standards and APIs to ensure your architecture can work with a variety of AI tools and technologies. This not only provides flexibility but also helps avoid vendor lock-in, where you’re dependent on a specific provider’s technology. You should be able to switch AI tools or add new ones as needed, without significant disruption to your system.
Start Small and Release Value Early
To release value early, we can build many small things. This approach allows us to release value incrementally instead of all at once. As a result, we can start getting returns on our investment much earlier in the project’s lifecycle. This approach also helps to alleviate the stress of critical stakeholders who may otherwise see the project as a financial burden with no immediate benefits.
Many refer to this as the MVP – or minimal viable product – where you invest just enough to test the theoretical idea to overcome the problem. This incorporates a fail-fast mentality, without costing too much time, resource and money.
“Noteworthy studies, such as a survey by the Agile Alliance, reveal a substantial 78% satisfaction among stakeholders when value is incrementally released throughout a project’s lifecycle.”
It can be challenging to consider everything at once, and humans are not exceptionally skilled at it. Even if you spend a lot of time trying to perfect your plan and write it down clearly and concisely, you will probably overlook some details or misrepresent something that will haunt you later. However, if you break down your plan into smaller, distinct parts, you can concentrate on getting each part right without worrying about the next steps, which may change as your project develops.
Don’t Throw Too Much Caution to the Wind
There is a potential warning related to the approach being taken. Pursuing very organic growth may lead to a situation where everything doesn’t connect back to the original vision. It can increase costs, cause frustration among stakeholders as they can’t see where the investment will end or how far through the build is. Ultimately it results in a disconnected technology and design landscape. According to a McKinsey Report, 65% of projects prioritising organic growth without clear guidance face challenges in maintaining a cohesive technology and design landscape.
To mitigate these pitfalls, it’s helpful to have people who can take ownership of these elements from a guidance perspective. This is where a good Product/Business leader paired with a good Technology/Architecture leader can be very useful in ensuring the path forward remains relatively straightforward.
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