For those companies that can own it, Artificial intelligence (AI) promises cost savings, competitive advantage and a foothold in the industry the future of business. But while the adoption rate of AI keep increasing, the level of investment often goes beyond for monetary returns. To succeed with AI, you’ll want the right data architecture. This article tells you how.
Currently, only 26% AI initiatives is being put into mass production with an organization. Unfortunately, this means that many companies spend a lot of time implementing AI without seeing a tangible ROI.
All companies must act like a technology company
Meanwhile, in a world where every company must act like a technology company to stay ahead, there is increasing pressure on engineering teams and Engineering and IT leaders to harness data. for trade growth. Especially as spending on cloud storage increases, businesses desire to improve efficiency and maximize ROI from costly data to store. But unfortunately, they don’t have time.
To meet this need for rapid results, a mapping data architecture can no longer span months without a defined goal. At the same time, the focus on standard data cleaning or Business Intelligence (BI) reporting is on a declining trend.
Technology leaders must build data architectures with AI at the forefront of their goals.
Do it differently – they’ll retrofit it themselves later. In today’s businesses, data architecture should aim for a defined outcome — and that outcome should include AI applications with clear benefits to the end user. This is the key to setting up your business for future success, even if you’re not (yet) ready for AI.
Starting from zero? Get started with data best practices
Data architecture requires knowledge. There are many tools out there and how you put them together is dictated by your business and what you need to achieve. The starting point is always to review the literature to understand what has worked for similar businesses, as well as drill down into the tools you’re considering and their use cases.
Microsoft has a good repository for data models, along with a lot of documentation on data best practices. There are also some great books that can help you develop a more strategic, business-minded approach to data architecture.
Prediction machine by Ajay Agarwal, Joshua Gans and Avi Goldfarb are ideal for understanding AI at a more fundamental level, with functional insights into how to use AI and data to run efficiently. Finally, for more seasoned engineers and technical professionals, I recommend Design data-intensive applications by Martin Kleppmann. This book will provide you with the latest thinking in the field, with helpful guidance on how to build data applications, architectures, and strategies.
Three fundamentals to a successful data architecture
Some core principles will help you design data architectures capable of powering AI applications that deliver ROI. Think of the following as compass points to check in with yourself whenever you’re building, formatting, and organizing data:
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Building Towards Goals:
Always paying attention to the business outcomes you’re aiming for when building and evolving your data architecture is a rule of thumb. In particular, I recommend looking at your company’s short-term goals and adjusting your data strategy accordingly.
For example, if your business strategy is to achieve $30 million in sales by year-end, figure out how you can use data to drive this. No need to be discouraged: break your more important goal into smaller goals and work towards them.
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Design to create value quickly:
While setting a clear goal is important, the final solution should always be agile enough to adapt to changing business needs. For example, small-scale projects can grow to become omnichannel, and you need to be aware of that. Fixed model and fixed rules will only create more work.
Any architecture you design must be able to hold more data as it becomes available and leverage that data for your company’s latest goals. I also recommend automating as much as you can. This will help you create valuable business impact with your data strategy quickly and iteratively over time.
For example, automate this process from the start if you know you need to submit monthly reports. That way, you’ll only make time for it for the first month. From there, the impact will always be effective and positive.
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Know How to Test Success:
To keep yourself on track, it’s essential to know if your data architecture is working properly. Data architecture works when it can (1) enable AI and (2) deliver usable, relevant data to every employee in the enterprise. Following these railings will help ensure your data strategy is fit for purpose and fit for the future.
The future of data architecture: Innovations to know
While these key principles are a great starting place for technical leaders and teams, it’s important not to get stuck in one way of doing things. Otherwise, businesses run the risk of missing out on opportunities that can deliver even greater value in the long run. Instead, technology leaders must constantly be integrated into new technologies coming to market that can enhance their work and deliver better results for their businesses:
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Cheaper processing:
We’ve seen improvements that make processing more cost-effective. This is important because many advanced technologies are being developed that require such high levels of computing power that they only exist in theory. Neural networks are a prime example. But as the level of computing power required becomes more feasible, we will gain access to more complex ways of solving problems.
For example, a data scientist has to train every machine learning model. But in the future, there is potential to build models that can train other models. Of course, this is still just a theory, but we will certainly see innovation like this accelerate as processing power becomes more accessible.
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Included tools:
Also, when it comes to applications or software that can reduce AI’s time to value, we’re at a stage where most existing technology can only do one thing well. The tools needed to produce AI — like storage, machine learning providers, API implementations, and quality control — are not pooled.
Now, businesses run the risk of wasting valuable time simply figuring out what tools they need and how to integrate them. But technology is slowly emerging that can help solve many data architecture use cases, as well as specialized databases to power AI applications.
These more bundled services will help businesses get AI into production faster. It is similar to what we have seen in the fintech space. Companies initially focused on being the best in a core competency before merging to create accompanying solutions.
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Data Marts vs. Data Warehouse:
Looking further into the future, it seems safe to predict that data lakes will become the most important AI and data warehouse investment for all organizations. Data lakes will help organizations understand predictions and how best to implement those insights. I see data mart becoming more and more valuable for the future.
Marts makes the same data available to every team in the business in a format they can understand. For example, Marketing and Finance teams see the same data presented in familiar metrics and – most importantly – a format they can use. The new generation of data will be much more than dimensions, facts, and hierarchies. They will not only cut and find information – but will support decision making in specific departments.
Inference
As technology continues to advance, it is important for businesses to keep up with the pace, or else they will fall behind. That means technology leaders stay connected to their teams and enable them to bring new innovations to the table.
Even as a company’s data architecture and AI applications grow stronger, it’s essential to take the time to experiment, learn, and (eventually) innovate.
Image Credit: by Polina Zimmerman; Bark; Thank you!
