Artificial intelligence (AI) holds the potential of cost savings, a competitive edge, and a place in the future of business for organisations that can grasp it. However, even if the rate of AI adoption is increasing, the degree of investment is frequently out of step with financial rewards. The correct data architecture is necessary for AI success. This piece explains how.
Only 26% of AI initiatives are currently being widely implemented within a firm. Sadly, this implies that many businesses invest a lot of effort in AI deployments without seeing any real results.
All Businesses Must Operate Like Tech Companies
Meanwhile, technical teams and engineering and IT leaders are under increasing pressure to use data for commercial growth in a world where every business must operate like a digital company to remain competitive. Businesses are eager to optimise ROI from data that are expensive to retain, especially as spending on cloud storage rises. They do not, however, have the luxury of time.
Data architecture for mapping cannot continue for months on end with no clear end in sight in order to satisfy this requirement for quick outcomes. Focusing on routine data cleaning or Business Intelligence (BI) reporting is also backward.
Data architecture must be built by tech executives with AI at the centre of their goals.
They’ll end up retrofitting it later if they choose to do anything else. Data design in modern enterprises should aim for a certain result, and that result should include AI applications that clearly benefit end users. Even if you aren’t (now) ready for AI, this is crucial to positioning your company for future success.
Beginning from Nothing? the best practises for Data
Data architecture calls for expertise. There are many tools available, and how you combine them depends on your business and what you want to accomplish. A deep dive into the products you’re considering and their use cases is always the beginning point when trying to discover what has worked for other businesses.
In addition to a wealth of literature on effective data practises, Microsoft maintains a good resource for data models. A more strategic, business-oriented approach to data architecture can be developed with the aid of some excellent books that are available.
Ajay Agarwal, Joshua Gans, and Avi Goldfarb’s book Prediction Machines is excellent for understanding AI at a more fundamental level and provides practical insights on how to use AI and data effectively. Finally, I suggest Martin Kleppmann’s Designing Data-Intensive Applications for more experienced engineers and technical professionals. You’ll get the most up-to-date information available in this book, along with useful advice on how to create data applications and develop your strategy and architecture.
Three Essentials for an Effective Data Architecture
You may create a data architecture that can support AI applications that provide ROI by adhering to a few key criteria. Consider the following as compass points you can use to gauge your progress when creating, structuring, and organising data:
The guiding principle when creating and refining your data architecture is to always keep in mind the business goal you are aiming to achieve. I advise paying close attention to your company’s short-term objectives and adjusting your data approach accordingly.
Find ways to leverage data to support your business strategy, such as if your goal is to generate $30M in revenue by year’s end. It doesn’t have to be overwhelming; just divide the bigger goal into smaller goals and focus on those.
Designing for Rapid Value Creation: Setting a defined goal is important, but the final product must always be flexible enough to respond to shifting business requirements. You should build with the possibility that small-scale projects will expand to become multi-channel in mind. In the long run, fixed modelling and fixed regulations will only produce additional labour.
Any architecture you create should be able to accommodate more data as it becomes available and use that data to further the most recent objectives of your business. Additionally, I advise automating as much as you can. This will enable you to quickly and repeatedly impact your business over time using your data strategy.
Automate this procedure from the beginning, for instance, if you know you must deliver monthly reporting. In this manner, the first month will be the only time you devote to it. The effect will then be constantly effective and favourable.
Developing Successful Tests:
It’s critical to understand whether your data architecture is operating efficiently if you want to stay on the correct road. Data architecture is effective when it can enable AI and provide useful, pertinent data to every individual working for the company. Your data strategy will be more suitable for its intended use and more suitable for the future if you stick close to these boundaries.
The Future of Data Architecture: New Developments to Be Aware Of
While these guiding principles are a fantastic beginning for technical leaders and teams to start, it’s also crucial to avoid becoming accustomed to a particular method of operation. Otherwise, companies run the danger of passing on chances that could ultimately result in even higher value. Instead, IT leaders must always stay abreast of the new technologies that can improve their work and produce better results for their company:
Processing is becoming more affordable thanks to advancements that are already being made. This is crucial because many of the cutting-edge technologies currently under development require so much computing power that they are merely theoretical. One such example are neural networks. We will have access to increasingly sophisticated methods of problem solving as the requisite degree of computer power becomes more attainable.
For instance, every machine learning model needs to be trained by a data scientist. However, there is potential to create models that can instruct other models in the future. This is still just a theory, but as processing power becomes more widely available, innovation like this will undoubtedly increase.
Bundled Tools: In addition, we’re in a stage right now when the majority of technology available can only do one thing well. This is especially true when it comes to apps or software that can reduce time to value for AI. Storage, machine learning providers, API deployment, and quality control are just a few of the unbundled productionizing AI tools.
Businesses today run the danger of wasting valuable time trying to determine which tools they need and how to integrate them. But as time goes on, technology that can address various data architectural use cases and databases tailored for supporting AI applications are gradually becoming available.
Businesses will be able to implement AI more quickly thanks to these more comprehensive services. It resembles what we’ve observed in the fintech industry. Before eventually merging to provide bundled solutions, businesses initially concentrated on being the best in one core area.
Data warehouses versus data marts:
Looking further into the future, it seems safe to say that data lakes will surpass all other investments in the AI and data stack for all businesses. Organizations will benefit from using data lakes to better understand forecasts and how to put them into action. I believe data marts will become more and more valuable in the future.
Every team in a company receives the same data from marts in a manner they can use. The Marketing and Finance teams, for instance, see the same data presented in recognisable metrics and, more importantly, in a format they can use. More than only facts, dimensions, and hierarchies will be included in the upcoming generation of data marts. They will aid in decision-making inside particular areas, not just slice and dice data.
Businesses must keep up with technological advancements if they don’t want to fall behind and be left behind. That calls for tech executives to maintain communication with their groups and give them the freedom to suggest fresh ideas.