The year is 2023, and Sarah, a data analyst at a leading tech firm, no longer spends hours writing complex SQL queries or sifting through vast datasets. Instead, she simply asks her data product, powered by a Large Language Model (LLM), “What were the sales trends last quarter?” and receives a comprehensive, human-like response. This […]

Imagine Sherlock Holmes, the iconic detective, in the midst of a confounding crime scene. He’s encircled by a constellation of clues—a peculiarly bent poker pipe, a singular set of footprints, and a unique brand of cigarette ash. Each piece of evidence is a fragment of a larger narrative, and it is Holmes’s task to weave […]

In today’s data-driven business landscape, organizations are constantly pressured to make faster, more informed decisions that drive better outcomes.  According to Forbes, 53% of companies use big data analytics to take inform business decisions.  An HBR study points out that companies that use data-driven decision-making are 6% more profitable than those that don’t. However, with […]

Advanced data analytics is a powerful tool for businesses that want to gain insights from their data. Advanced data analytics can provide unprecedented visibility into customer trends and preferences through sophisticated algorithms and technologies. Organizations can use these insights to identify new opportunities or better understand customer behavior. According to a McKinsey study, organizations that […]

International Data Corporation (IDC) predicts that by 2025, the amount of data generated worldwide will reach 163 zettabytes, growing at a CAGR of 44%. Not just that, Gartner predicts that by 2025, AI-driven automation will reduce data preparation time by 95%, enabling organizations to analyze vast amounts of data in real-time. Walmart, the world’s largest […]

Businesses around the world generate massive quantities of data daily in the form of server logs, web analytics, transactional information, and customer data. To effectively process this much information and derive actual value from it, businesses need to consider advanced analytics techniques for decision-making. We already discussed its applications across industries in our previous article. […]

Data is the most important asset for any modern organization, backing most business-critical decisions today. However, fully capturing the potential of the company’s data sources, so that they start yielding impactful business insights, is not a straightforward task and the traditional BI and analytics stack is just not at the level to handle the complex […]

We live in a hyper-digital world, and due to the nearly  infinite number of data sources that surround us, the volume of data generated collectively by individuals, applications and corporations is larger than ever. With such a monumental amount of data to sift through, two core principles have  become increasingly important: Metadata – Make it […]

Anomaly detection refers to the process of analysing data sets to detect unusual patterns and outliers that do not conform to expectations.  It takes on even more importance in a world where enterprises depend heavily on an intricate web of distributed systems. With thousands of potentially important data items to monitor every second, it is […]

We recently had the good fortune of speaking at ValleyML’s AI Expo 2022 earlier this month. This is an annual event that presents a unique combination of AI Technology, researchers, industry thought leaders and prospective buyers of AI/ML technologies in a single event. The 2022 edition promised even more interesting talks and networking opportunities as it spanned four […]

Feature engineering is the process of selecting, interpreting, and transforming structured or unstructured raw data into attributes (features) that can be used to build effective machine learning models which more accurately represent the problem at hand. In this context, a “feature” refers to any quantifiable unique input that may be used in a predictive model, […]

Feature engineering is both a central task in machine learning engineering and is also arguably the most complex task. Data scientists who build models that need to be deployed at large scales, across functional, technical, geographic, demographic and other categories have to reason about how they choose the features for the models. Despite the divergent […]

As the complexity of data and systems that hold data grows, the cost of analysis increases due to time and effort spent in figuring out the feasibility, appropriateness, access, and management of data. We believe that a number of new low-risk and valuable applications can be built through creative application of metadata that can help […]