Fueling AI Insights in Mexico: Data Lakes and Cloud Warehouses

In response to the ever-growing volume of data, enterprises are proactively pursuing strategies for the efficient management and analysis of data to optimize operational efficiency and facilitate decision-making. Two compelling solutions on the horizon are Data Lakes and Cloud Warehouses, aided by the power of AI, which provide organizations with the capacity to securely store, govern, and analyze extensive datasets. Nevertheless, Mexican enterprises face the intricate challenge of integrating these systems into existing infrastructures in an efficient way that enhances their processes and, therefore, their value. 

 To fully capitalize on their data-driven transformations, companies are increasingly relying on emerging technologies like data lakes and data warehouses (64%), predictive analytics (63%), and machine learning (51%), according to a Denodo survey. Currently, organizations are applying a combination of both to leverage the strengths of each, ensuring a secure, end-to-end system for storage, processing, and faster time to data-driven insights, according to Microsoft.

While data lakes and data warehouses are similar in that they both store and process data, they both have distinct characteristics. A data lake is versatile and flexible, capturing relational and non-relational data from diverse sources like business applications, mobile apps, IoT devices, and social media, without the need to structure the data beforehand. This adaptability suits industries like telecommunication, healthcare, and manufacturing, where data formats are diverse and constantly evolving, according to Microsoft.

Meanwhile, data warehouses only operate in a relational way. Unlike data lakes, data warehouses store treated and transformed data with a defined purpose, often used to source analytic or operational reporting. In this case, data warehouses can benefit industries in need of historical analysis and predictive analytics, such as finance, retail, and the automotive industry.

"A data lake serves as a comprehensive repository intended for later analysis and needs the expertise of a dedicated team to process and extract value for the organization. In contrast, data warehouses house data that is pre-structured and processed, readily available for immediate utilization,” says Diego Sánchez, Business Director, Mabe. 

If a company decides to opt for a data warehouse, it will be able to undertake smaller, more agile projects with quicker execution. On the other hand, data lakes are better suited for the execution of larger, more robust projects that promise greater value to the company, albeit with a slower pace, he added.  

The implementation of an AI solution needs a well-defined strategy to enhance data processing, particularly during the model's training phase. As such, it is of paramount importance for the specialized team to engage in meticulous data curation to ensure that the model effectively aligns with the business's strategic objectives, said Itzul Giron, Founder and CEO, Knowsy AI. This is meant to safeguard against the generation of irrelevant data, ensuring the model's utility in the context of informed decision-making. 

“Data curation, the process of refining data to ensure its accuracy and usability, forms the bedrock of data science. It serves as the essential precursor to extracting precise insights and building accurate models, thus empowering data-driven decision-making,” said Roberto Juarez, Sr. Systems Engineer, Nutanix. To implement these solutions accurately, experts recommend hiring an in-house data scientist who truly understands the business model and the leader’s vision to inform data governance and streamline the integration of the chosen AI model. 

Guidance holds paramount significance given the propensity of data stored in cloud warehouses and data lakes to descend into chaos and lose utility without a clear strategy. At present, most companies are grappling with gaps in expertise needed to realize their data-driven ambitions. Without them, companies lack the capabilities to build data architectures capable of generating the desired insights needed to optimize business decisions. These qualifiers are meant to precede the implementation of AI tools for data processing.

“It is crucial to assess whether AI is truly necessary, pinpoint the right moment for integration, and select the most appropriate AI category among the four types: generic AI, AI machine learning, AI deep learning, or GenAI. The significance of determining the type of AI used, lies in the particular emphasis of its pre-trained nature. While many companies currently prefer machine learning or deep learning, fewer have truly explored GenAI,” said José Carlos Huescas, WW HPC and AI Product Manager, Lenovo.