What Is Data Literacy?

Data literacy (DL) describes how well an individual or organization understands, works with, analyzes, visualizes, and applies data to reach their goals. The specific context and use case determine what applying data literacy looks like in practice. For example, while reading visualizations on product deliveries provides value, true data literacy involves going further. It involves actively…

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The Future of Insurance: A Business Analyst’s Insight into Emerging Trends and Technologies

The insurance industry is undergoing a revolution, mainly driven by the application of advanced emerging technologies. The application and installation of new technologies enable a better future for our industry, where customers will receive maximum efficiency, security, and flexibility. Here, we address the major technologies and trends that influence this transition, shedding light on their…

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Fundamentals of Data Collaboration

Data collaboration allows organizations to gain insights beyond what their data provides. By sharing information smartly and selectively with partners, companies can uncover new opportunities and insights beyond their internal repository. Moreover, the emergence of large language models (LLMs) applications  – like Chat GPT – and cloud technologies, make this approach more attractive. As businesses become…

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Securing Data in Transit for Analytics Operations

Most enterprises today store and process vast amounts of data from various sources within a centralized repository known as a data warehouse or data lake, where they can analyze it with advanced analytics tools to generate critical business insights.  Modern data warehouse platforms such as Snowflake, AWS Redshift, Azure Synapse Analytics, and IBM Db2 are built with…

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Beyond the Basics: Advanced Tips for Effective Data Extraction

Data extraction is a cornerstone in data analytics, enabling organizations to extract valuable insights from raw data. While basic extraction techniques are fundamental, understanding advanced strategies is crucial for maximizing efficiency and accuracy. This article will explore advanced tips for effective data extraction, shedding light on automation tools, leveraging APIs and web scraping techniques, enhancing…

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How to Become a Data Product Manager

Becoming a data product manager means taking responsibility for the development and management of data products. Broadly speaking, a data product is any software or algorithms that use data to accomplish a goal. The data product manager is a management position, and requires several years of experience within the data industry to be done well. …

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Granularity Is the True Data Advantage

Commerce today runs on data – guiding product development, improving operational efficiency, and personalizing the customer experience. However, many organizations fall into the trap of thinking that more data means more sales, when these two factors aren’t directly correlated. Often, executives will become overzealous in their digital transformations and cut blank checks for data collection,…

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Preparing for La Niña: Adopting Predictive Maintenance Before Hurricane Season

With a La Niña watch issued for the summer, businesses operating in hurricane-prone regions face heightened concerns about the impending storm season. La Niña heavily impacts the wind shear and atmospheric conditions over the Atlantic, where most hurricanes form thanks to its warm waters. It’s rare to go a year without a hurricane hitting some part of…

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The Swashbuckling Guide to Building an Analytics Center of Excellence

Ahoy, data adventurers! Embarking on the thrilling journey of building a successful Analytics Center of Excellence (CoE) is akin to navigating the vast seas of data-driven decision-making. As a seasoned captain who’s steered the ship through the turbulent waters of financial services, retail, CPG, and telecom industries, I’ve charted a course that’s proven its mettle…

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How to Become a Data Engineer

The work of data engineers is extremely technical. They are responsible for designing and maintaining the architecture of data systems, which incorporates concepts ranging from analytic infrastructures to data warehouses. A data engineer needs to have a solid understanding of commonly used scripting languages and is expected to support the steady evolution of improved Data Quality,…

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