Understanding the importance of data culture requires recognizing its pivotal role in shaping how organizations operate and innovate. A strong data culture instills a mindset where decisions are driven by data, fostering an environment that values evidence over intuition. This cultural shift enables organizations to harness the full potential of their data assets, leading to […]
The Future of Data Literacy
There was a time when only elite, tech-savvy staff in an organization understood and felt qualified to discuss data-enabled business decisions. These individuals often possessed advanced academic degrees in data science, data engineering, statistics, operations research, and other allied fields and did not speak the language of the ordinary business staff. As a result, there […]
Self-Service Analytics: Pros and Cons
Self-service analytics empowers the non-technical users in an organization. Traditionally, data analysis was the domain of specialized data scientists or IT professionals with skills to manipulate and interpret complex datasets. In self-service analytics, user-friendly tools enable ordinary business users to conduct data analyses without expert knowledge or support. These tools typically feature user-friendly interfaces, drag-and-drop functionalities, and pre-built templates […]
Large Language Models 101
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 […]
Generative AI vs. Traditional AI
Traditional AI, also known as “classical AI,” is known for being rule-based and dependent on stringent programming for its intended output. These techniques revolve around the manipulation of symbols and logical reasoning to perform tasks. Key methodologies include rule-based systems, where knowledge is encoded in the form of “if-then” statements, enabling machines to make decisions […]
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 […]
Data Storytelling 101
Humans are inherently wired for stories. Stories captivate our imaginations, simplify complexity, and provide context – and turn abstract figures into relatable scenarios. Data storytelling takes advantage of this human passion. In data storytelling, the message is conveyed through engaging narratives, making the data insights inspire a high level of trust among the audience. It empowers stakeholders […]
Evaluating Enterprise Data Literacy
Any organization that aims toward complete digital transformation must move toward enterprise data literacy. So, what exactly is data literacy? Gartner defines data literacy as: “The ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied – and the ability to describe the […]
Fundamentals of Descriptive Analytics
In descriptive analytics, data aggregation, and data mining techniques are used to collect and review the historical data of a business to gauge the past performance. The most common example of descriptive analytics is the reports that a user gets from Google Analytics tools. A web server’s summarized performance reports may help the user analyze […]
Data Architecture Best Practices
The term “data architecture” refers to a collection of implementable standards and protocols that govern the collection, storage, preparation, sharing, and distribution of data. This predefined set of standards and protocols is designed to enhance the scope and purpose of data analysis in the busy business environment. One major problem with traditional data management systems […]