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Slowing Down to Go Fast: Applying Technology Impact Assessments to Test Automation Transformation

Every enterprise seeks to gain competitive advantage through the embrace of new, innovative technologies to either transform external customer engagement or unlock internal operational efficiencies. Test automation represents a powerful technology innovation by which product teams can unlock significant software development operational efficiencies. But test automation transformation initiatives come with notable risks that should be considered.

5 Key Data Governance Principles for Effective Data Management

Digitalization has led to more data collection, integral to many industries from healthcare diagnoses to financial transactions. For instance, hospitals use data governance practices to break siloed data and decrease the risk of misdiagnosis or treatment delays. Therefore, managing data to protect its integrity and security leads to high-quality, reliable data that empowers organizations to make informed decisions.

Data Science vs. Data Analytics: Key Differences

Organizations increasingly use data to gain a competitive edge. Two key disciplines have emerged at the forefront of this approach: data science and data analytics. While both fields help you extract insights from data, data analytics focuses more on analyzing historical data to guide decisions in the present. In contrast, data science enables you to create data-driven algorithms to forecast future outcomes. These disciplines differ significantly in their methodologies, tools, and outcomes.

Ensuring Precision: Software Testing for Nanotechnology Innovations in Healthcare and Life Sciences

While AI has garnered the attention of many industries, nanotechnology still has not lost its significance in healthcare and life sciences. Promising research continues to drive the exploration of nanotechnology for health and wellness applications. Yet, an often-overlooked element is the crucial role of software testing in this field.

A Complete Guide to Data Analytics

Data analytics is the science of analyzing raw data to draw conclusions about it. The process involves examining extensive data sets to uncover hidden patterns, correlations, and other insights. With today’s technology, data analytics can go beyond traditional analysis, incorporating artificial intelligence (AI) and machine learning (ML) algorithms that help process information faster than manual methods.

Bringing Financial Services Business Use Cases to Life: Leveraging Data Analytics, ML/AI, and Gen AI

The financial services industry is undergoing a significant transformation, driven by the need for data-driven insights, digital transformation, and compliance with evolving regulations. In this context, Cloudera and TAI Solutions have partnered to help financial services customers accelerate their data-driven transformation, improve customer centricity, ensure compliance with regulations, enhance risk management, and drive innovation.

Unify your data: AI and Analytics in an Open Lakehouse

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission-critical, large-scale data analytics and AI use cases—including enterprise data warehouses. Nearly two years ago, Cloudera announced the general availability of Apache Iceberg in the Cloudera platform, which helps users avoid vendor lock-in and implement an open lakehouse. With an open data lakehouse powered by Apache Iceberg, businesses can better tap into the power of analytics and AI.