Why Data Science Architecture is Essential Today
Wed Nov 29 2023
Author
Noah Xiao
In the realm of AI and technology, data science architecture emerges as a key component, shaping how organisations leverage AI for business success. At jahan.ai, we recognise this as a multifaceted field, merging diverse disciplines to build AI and ML solutions that drive real value.
What is Data Science Architecture?
Data science architecture represents a crucial, multidisciplinary function, encompassing AI/ML, mathematics, software engineering, IT systems, data management, cloud platforms, human-centred design, security, and more. There are many considerations that go into a good data science architecture including (but not limited to):
- Business problem definition and business process optimisation,
- Research and literature review (to learn from what has worked before and why)
- Machine learning neural network architecture,
- IT solution architecture (how the DS solution fits into the tech ecosystem of an organisation),
- software system design,
- data pipeline design (including managing data quality),
- DS Platform and tooling,
- Interface design (how the output of model will be accessed and used)
- Landing the initiative and driving effective change management
- Responsible AI,
- Ways of working,
- How to realise benefits
It is often useful to peer review and defend a data science architecture to ensure that all its aspects are coherent and aligned
Why Data Science Architecture is Essential
- Core to Business Functions: Data science solutions, including AI/ML, optimisation, and analytics, are becoming increasingly crucial and valuable, necessitating their deep integration into day-to-day business operations. This integration requires the solutions to be not only accurate and reliable but also trustworthy, scalable, and tailored to meet the specific needs, use cases, experiences, and strategies of business users. A well-designed architecture serves as both a blueprint and a set of guidelines, ensuring that these solutions are effective when deployed.
- The Optimal Trade-offs: As the number of variables grows in shaping a data science solution, e.g. the specific AI/ML models, software tools and patterns, IT systems, data patterns, cloud platforms, and services, human-centred design considerations, security policies, open and closed source tools and services, and responsible AI principles, and more, it becomes harder to make the right decisions and trade-offs to optimise outcomes. This critical balancing act is precisely where data science architecture plays an essential role.
- From Point Solutions to Global Solutions: A point solution is about solving one single puzzle, while a global solution aims to see the bigger picture and ensures the solution not only serves one purpose nicely but also is expandable and scalable to other related problems and future considerations. At jahan.ai, we believe massive value lies within the intersections between different problems. For example, a demand forecast model not only tells how many customers will visit next week, but also should guide a retailer about what the price elasticity of demand is in making promotion decisions, and what roster plan should look like when optimising the shift in stores. This is why we are passionate about building ecosystems and building products that is well suited to be used across functions to maximise business consistency and efficiency. A well-thought data science architecture takes these considerations from an ecosystem and byproducts that extend beyond a single model, requiring solutions that align with both the explicit and implicit needs of a business. These solutions should be designed not only for the present but also for the future.
- More Sophisticated AI: AI is becoming increasingly sophisticated and complex. While Artificial General Intelligence (AGI) may not be a reality today, expert AIs like Dall-E, AlphaGo, and our forecasting product at jahan.ai, are highly advanced and widely used in various aspects of life and business. The challenge with more sophisticated AI is the need to consider a broader range of factors - system integration, data quality, service reliability, user experience, developer experience, measuring business benefits, and managing change, along with newer considerations like ethics, privacy, digital twins, chain of thoughts, and LLMOps. They are all relevant in building an AI product, demanding a good data science architect should have rich experience and knowledge across all these fields.
- Realising ROI: AI/ML is a significant investment if taken seriously. It's no longer just a buzzword or a novelty confined to innovation labs; it's a real business tool generating tangible value. For any significant and essential application in business, these technologies should be approached with seriousness. For a business, it's crucial to see these benefits materialise. Having a well-designed architecture, crafted by experts with extensive experience and insights into all aspects of data science-driven solutions, not only smoothens the delivery of outcomes but also ensures that the benefits are maximised. This architecture is designed to seamlessly align with real use cases, directly addressing all pain points.
An Example
Predicting customer demand in a retail store is a crucial task, but leveraging this solution to not only drive stock replenishment and understand demand drivers, but also, and to optimise pricing, promotions, product ranging, space allocation, and media decisions. All of these have direct impact on business growth and efficiency. This gives a taste of how important it is to ensure that the forecasting architecture can be expanded into an ecosystem that sees the bigger picture, supporting multiple steps and functions for a business.
The solution also needs to be designed in a way that it doesn’t just predict an outcome but also can be called in a modular way to enable simulation and what-ifs, understand the causal impact, and not only feed other operational systems with the forecasting output, but also be utilised by other machine learning models to optimise each of the many use-cases that need to be solved.
Another challenge lies in navigating through the thousands of combinations of AI/ML models, tools, services, patterns, methods, frameworks, and policies to find the most optimal approach to ensure the solution is robust, scalable, strategic, and operationally maintainable.
The example illustrates some lenses a good data science architecture should factor in. This requires the architects to have not only technical expertise but also strong strategic thinking and a mature understanding of business.
- Cross-Disciplinary Expertise: We encourage our team to broaden their knowledge beyond their primary fields, ensuring diverse expertise in delivering optimal solutions.
- Focus on Business Value: Our technical decisions are driven by the goal of delivering business value and ensuring long-term strategic alignment.
- Quality > Quantity: Quality first is in our genes, and we prefer simple but elegant solutions, avoiding unnecessary complexities and dependencies. Focusing on quality helps us to build the right thing, well, the first time
- Commitment to Rigorous Review: Our architecture decisions undergo strict peer review, ensuring their sustainability and reliability.
- Encouraging Research and Innovation: Staying at the forefront of technological trends, and the latest in academic and scientific research, allowing us to enrich our solutions with diverse and effective problem-solving methods.
Today, data science architecture is no longer an optional component but a necessity for success. At jahan.ai, our data science architecture practice ensures our AI/ML products and consulting services offer simple, optimal, and accurate solutions for our clients. We invite you to explore how our expertise can transform your business operations. Connect with us to learn more about our innovative approach and how we can assist you in driving business value with AI/ML. 🚀💡
At jahan.ai, we build end-to-end AI twins for retail and supply chain businesses. We take the concept of a digital twin, a virtual replica of business processes, further with advanced AI, which not only mirrors these processes but also automatically optimises them. In this way, we support businesses in driving efficiency, going beyond their potential.