It is well documented that organizations are generating more data than ever before. Increasingly, the data that is gaining in both volume and veracity is described as unstructured.
AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it simple and cost-effective for customers to categorize their data, clean it, enrich it, and move it reliably between various data stores.
Exploring data, searching unstructured sources, and finding direct answers to company-specific questions in internal systems are vital but cumbersome tasks for many businesses.
Digital has evolved into distinct branches of methodologies regarded as Digitalization and Digital Transformation, resulting in a Digital Business
Defining metadata for the data owned by the organization is the first step in unleashing the organizational data’s maximum potential.
By formalizing data management practices, it ensures data and digital assets are treated like valuable assets for the insights that they can provide with careful analysis.
Open Banking, also known as Consumer-Directed Finance, promises to be the next frontier in banking. The concept revolves around secure sharing of customer data residing in any financial institution with external parties via APIs.
As AI continues to increase its influence on business decisions, governments are taking steps to ensure that all AI usage is ethical.
In 2021, the Ontario Government mandated that all ministries and provincial agencies have an information governance strategy and framework in place.
Within the global payments industry, there is a continuous demand for operational efficiency and user expectations for fast and value-added services.
AWS is an industry leader in promoting open-source initiatives and alternatives through heavy integration with their existing cloud services.
Data democratization’s focus is bringing users closer to data, breaking down silos, and creating value from the organization’s valuable data.
In this article, we break down the top 6 costs that organizations are currently enduring whilst they have no action plan to clean their data quality.
The ML development lifecycle consists of three key pipelines: data preparation, modeling, and operationalization. MLOps principles aim to standardize and automate each of these areas.
MLOps brings business interest back into the forefront of ML operations, where ML experts and data scientists work through the lens of an organizational interest with clear direction and measurable benchmarks.