Data in the age of AI: a step-by-step guide
Did you know you lose 12 hours a week searching for data? In the age of AI, data silos are holding back businesses. This article shows you how a modern solution like data lakes can centralize your information and help you make smarter decisions.
How we extract, process, and use data is changing rapidly. In uncertain times, data solutions need to start small, be tested early — and scale when ready.
Organizations have been facing an uphill data battle for years.
Many have had to deal with aging legacy systems and platforms. Some — as many as 98%, according to recent FromAssembly research — are still relying on manual data entry, which takes time, effort, and is prone to human error.
Even organizations that consider themselves digitally transformed may nevertheless be swimming in unorganized data, producing no actionable insights.
And with the rise of AI, there are even more challenges — and opportunities — on the horizon.
A brave new world of data
One of the biggest recent shifts, when it comes to data, is just how much we can now collect, process, and interpret. And it’s all thanks to AI.
With Machine Learning and LLMs, we can parse through much broader swathes of unstructured, raw data. And in a fraction of the time.
Little wonder, then, that data analysis and analytics is one of the most popular uses for Gen AI. According to a recent Wharton Human-AI Research survey, 62% of business leaders are already using it for exactly this purpose.
AI is evolving so quickly, however, that we can’t know what we’ll be doing with our data in the next few years. The potential is as enormous as it is unpredictable.
That’s why it’s risky to bet on a single, expensive, and prepackaged data solution for your business.
Our approach: customized, gradual, scalable
At our Santex LAB, we believe in bespoke AI-powered solutions. Because we know that one size truly does not fit all — especially not now.
But that’s only part of the equation. It’s not just about finding solutions that fit; it’s about finding solutions that grow with our clients.
In our current, fast-changing landscape, what they’ll need six months from now is not necessarily what they think they need right now.
So, what do we offer our clients? Let’s break down our five-phase journey, from vision to scaled AI solution:
Envisioning: We align AI strategy with corporate goals, and measure how prepared our client is across strategy, data, talent, and culture.
Discovery and feasibility analysis: We identify opportunities, prioritize high-value use cases, and test data availability, tech stack fit, and ROI assumptions.
AI sandbox: We build a disposable prototype in weeks, not months. And create custom AI demos to rapidly validate business opportunities and test solutions with impact.
MVP launch: We ship an end-to-end, production-ready solution.
Scale the solution: Our solutions aim to optimize and scale processes across our client’s business.
By rapidly prototyping and testing ideas, we accelerate time-to-market. And by validating solutions before full-scale implementation, our clients save on up-front costs and reduce risks in the long term.
In uncertain times, you need flexibility and adaptability. You need solutions that break new ground while still operating within your existing ecosystem and tech stack.
That’s why fast and nimble is the order of the day. Prototype quickly, test frequently, scale confidently.
Case study: transforming data analysis for a fast food operator
This was the approach we took for one of our recent success stories: we worked alongside one of the biggest fast food operators in the world to modernize their data infrastructure.
They were being weighed down by disjointed internal tools, time-consuming manual processes, and inconsistent data sets. This meant they didn’t have access to reliable, real-time performance metrics.
How did we tackle the problem? From more than one angle.
We implemented Snowflake as a robust data warehouse, integrated AWS S3 for data lake storage, set up real-time dashboards in Tableau, unified and automated data collection across their operations, developed Machine Learning models for predictive sales insights, and more.
The results? 40% better quality data, 80% less manual labor, and 20% fewer infrastructure costs.
Letting data work for you
Across your supply chain, every step is generating data: internal process data, IoT data, sales data, employee data, logistics data, procurement data…
As AI becomes more powerful, new ways to process, analyze, and visualize all this data are emerging. Data that seems useless today may be crucial tomorrow.
That’s why it’s essential to future-proof your data infrastructure.
But it’s not about finding a single, rigid solution. Instead, it’s about adopting a flexible mindset and being ready to pivot when the need arises.
It’s about starting small, then moving firmly and consistently towards your long-term goals.
