Large language models (LLMs), built on the transformer architecture of deep learning, are designed to process very high volumes of textual data at a high speed. LLMs also have the power to generate new text and interact with human language innovatively. Training on different types of data, including articles, books, periodicals, and websites, LLMs develop […]
Data Product vs. Data as a Product (DaaP): Understanding the Difference
Data quality (DQ), which ensures that data is fit for business and consumer needs, remains a significant challenge and is growing more complex. According to a dbt Labs 2024 report, 57% of survey respondents identified data quality as a challenging aspect to data preparation, up from 41% in 2022. To address these data quality challenges, companies increasingly […]
Building an Enterprise Data Strategy: What, Why, How
No matter how good you feel about the reliability of your company’s data operations, you can’t help wondering on occasion what the chances are that your firm will be one of the victims of the next data outage, malware scourge, or cyberattack. This is especially the case if your job entails ensuring the availability of your organization’s […]
Women in Data: Meet Enterprise Data Architecture Manager Shannon Hughes
The latest installment in our Q&A series with women leaders in data features Shannon Hughes, enterprise data architecture manager at ExxonMobil’s Central Data Office. (Read our previous Q&A here.) Shannon Hughes became fascinated with data and technology as a kid, inspired by her dad – a technical data analyst for the U.S. Air Force. She now […]
What Is Active Metadata and Why Does It Matter?
Metadata is like the secret sauce of the internet. You type a word or phrase in a search engine, press enter, and the information you’re looking for appears (usually). An action that once seemed “indistinguishable from magic,” to quote the third of Arthur C. Clarke’s three laws, is now as commonplace as heating up leftovers in […]
Choosing a Data Quality Tool: What, Why, How
Data-driven organizations are in a race to collect the information that modern analytics techniques rely on to generate insights and guide business decisions. The ever-growing flow of data into business systems challenges companies to devise new techniques for ensuring the quality of the data as its quantity skyrockets. Data quality tool vendors are rising to […]
A Brief History of Data Ethics
In this digital age, where data is an increasingly integral asset for every organization, the ethical implications surrounding data collection, storage, and usage have become prominent. The evolution and history of data ethics is a complex journey that connects technological advancements with societal values and legal frameworks. Understanding this evolution requires examining key historical milestones and the […]
Six Common Digital Transformation Challenges
During digital transformation, one of the most formidable challenges organizations face is resistance to change and the complexities of effective change management. Human nature often gravitates toward familiarity and routine, making any deviation a source of discomfort. This psychological inertia can stem from various factors including fear of job loss, perceived inadequacy in new skill […]
What Is Data Trust and Why Does It Matter?
A batch processing system fails on the eve of a company’s deadline for monthly reports, threatening the accuracy of its financials. One of the two systems powering the dashboard of a global supply chain company crashes, and a manager overbooks a transport ship because it displays inaccurate data, causing a costly delay in a customer’s […]
Foundations of Forensic Data Analysis
Forensic data analysis involves collecting, modeling, and transforming data to identify and highlight potential risk areas, detect non-standard or fraudulent activities that use data, and set up internal controls and processes to minimize a variety of risks. Data forensics can also be used in instances involving the tracking of phone calls, texts, or emails traveling […]