Introduction As companies strive to harness the power of data, analytics has become a cornerstone of decision-making. Yet, while the benefits of advanced analytics are undeniable, many organisations grapple with one essential question: should they invest in an in-house analytics solution, or opt for a Data Analytics as a Service (DAaaS) model? In this article,
In today’s fast-paced digital era, data is more than just numbers and charts—it’s the foundation of forward-thinking business strategies. With the rise of Data Analytics as a Service (DAaaS) platforms, companies are able to outsource their data management and analysis needs, ensuring access to advanced analytics without the complexity of building in-house solutions. But now,
In today’s data-driven world, businesses are striving to extract actionable insights from the vast amounts of data they generate. Traditional business intelligence (BI) tools have long helped organisations with data collection and analysis, but a new era of data analytics is emerging: Vertical Data Analytics as a Service (DAaaS). Much like Vertical SaaS (Software as
Introduction: Embracing the Power of Transformation in GBS In today’s rapidly evolving business landscape, Global Business Services (GBS) organizations are at a pivotal moment. No longer just operational support systems, GBS has transformed into a strategic powerhouse driving business outcomes. The evolution is driven by one key ingredient—data analytics. The ability to harness data insights
Survival analytics is a powerful statistical technique used to estimate the time until an event of interest occurs, such as equipment failure, customer churn, or employee attrition. It has applications across multiple industries, including human resources, finance, manufacturing, and telecommunications. AIDOSOL has not only embraced survival analytics but also developed expertise that allows us to
The global attitude toward artificial intelligence (AI) is mixed and widely dependent on geographic, cultural, and socioeconomic factors. While some are excited and optimistic, some are quite worried, stressed out, and skeptical. AI is not new, in fact, the term “artificial intelligence” was coined in 1956. Due to the increased data volumes, advanced algorithms, and
In today’s digital age, data is the lifeblood of businesses, powering decision-making, enhancing customer experiences, and driving innovation. However, with the increasing reliance on data, the risks associated with it have also grown, particularly in the form of data breaches. These breaches not only expose sensitive information but also undermine trust in data analytics—a key
Introduction: Understanding Algorithmic Bias In the rapidly advancing world of artificial intelligence (AI) and machine learning (ML), algorithms play a critical role in decision-making processes across industries. From credit scoring and hiring practices to law enforcement and healthcare, algorithms are increasingly used to predict outcomes, assess risks, and guide significant decisions. However, a growing concern
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