Handling big data – LENDISCORE

"Big data" has become quite the buzzword nowadays. It's hip to discuss innovations, big data, and artificial intelligence. But what's cooler is putting these concepts into action daily. At Lendiscore, our primary focus is credit scoring; some even dub us the alchemists of data science and credit intelligence. But honestly, it's not a title we chase; it's simply what we do. We thrive on innovation, harnessing the power of big data, machine learning, and predictive analytics to paint a clear picture of our customers' financial situations. Data isn't just a part of our work—it's our essence, our lifeblood, and the core of our business. Naturally, it's our favorite topic! Let's dive into a few hot topics in the data universe!

Data accessibility

There has never been as much data on our planet as there is now. We're swimming in it! A while back, everyone was screaming about the importance of big data. Now that we've got it, the real question is: how do we use it? Accessibility starts with collecting data in a structured manner and stashing it away in formats for easy retrieval. Gone are the days of staring at a single Excel workbook and drawing conclusions. Today, it's all about the cloud, servers, databases, data warehouses, and data lakes—places where info is neatly organized and stored.

Data warehouses and data lakes are built specifically for data analytics, and they are separate from data stores used by the system. Such separation of data allows data analysts to run complex processing operations on data without crashing the system. When designing data warehouses and data lakes the key focus is on how to optimize those for quick & efficient data extraction for business insights. Nowadays it’s impossible to handle the large volume of data that we have the old way - you need special technologies to process it. For example, one of the widely used tools for data processing is Spark. 

Now, onto the fun part—testing! In data science, testing is a cycle that's crucial for trustworthy insights. It plays a vital role in mitigating errors, enhancing the accuracy of models, and improving decision-making based on data-driven insights. If you automate testing to the max, you've hit the jackpot. Oh, and don't forget about tracking file changes over time—that's version control. It's like the guardian angel of software development, ensuring multiple hands can work on the same project while keeping tabs on tweaks, boosting teamwork, and safeguarding project integrity. We track not just the latest model changes but also how the product's evolved, what works best, and what's on the horizon.

Monitoring

Effective monitoring keeps systems, models, and apps reliable, high-performing, and secure. It's the eye that spots issues before they snowball, leading to continuous improvements. In data science, monitoring means always checking incoming data quality, sniffing out anomalies or inconsistencies that might mess with model performance. Real-time monitoring isn't just for “data people”; it's a goldmine for businesses, too. For instance, in lending companies (our prime clients), it unveils insights into customer behavior, preferences, and trends. There are many effective business intelligence tools that demonstrate real time data, for example, Tableau dashboards, that can in real time show all the key business metrics (sales, approval rate, cash flows, loan performance, etc.) Business Intelligence developers are working closely with the operational side of the business to understand what insights are needed the most and what is possible to get from the data. When business intelligence developers have a clear understanding of the operational side’s needs, they can develop monitoring dashboards, which later can become the main working tools for the operational side to understand what is happening with the business. Armed with this data, our clients fine-tune their lending products, tweak marketing strategies, and level up customer service. It also allows us to track operational efficiency, mitigate risks, detect fraud, manage portfolios, and perform predictive analysis.

Nowadays, there's a trove of top-notch tools out there that present data superbly—think Shiny and Quarto. Shiny, R’s interactive web app framework, has leveled up big time, handling larger datasets and more users. This scalability's a game-changer for enterprises aiming to deploy data-driven apps at scale. Then there's Quarto, the new kid on the block, a next-gen markdown language. It’s designed to create reproducible data science content across R, Python, and other languages. Quarto's integration with Jupyter Notebooks and other IDEs promises a more unified experience in reporting and documentation.

LLM models

This technology is so versatile that it can be used almost in every sphere of life. LLM or Large Language Model is a type of artificial intelligence model designed to understand and generate human language. These models are based on deep learning techniques and are trained on vast amounts of text data to perform tasks like language generation, translation, summarization, question answering, sentiment analysis, and more. Then saying that LLM models currently is the hottest topic related to AI we are not lying. Companies are just starting to figure out how to use those LLM models for their benefit. One of the more common use cases is assisting in writing code and composing text. But there can be multiple other creative use-cases. In the field of artificial intelligence, LLM models play crucial roles in processing, analyzing, and extracting insights from large volumes of data, making predictions, automating tasks, and assisting in decision-making processes across various industries and applications.

Focus on open-source collaboration

Let's tip our hats to the army of enthusiasts powering open-source tools—stuff we can all use, tweak, and make better. This spirit, alongside testing, is a gift from the IT world to data science. And it rocks! Sharing know-how and ace practices is how this industry grows. International conferences are another goldmine for knowledge-sharing. You can practically hear the buzz of expert chatter about the hottest trends in every room. For example, our team hit up the Posit Conference in Chicago this year. Attending various conferences? That’s on our team's “to do” list every year, and it should be on yours too!