As environmental, social, and governance (ESG) becomes an increasingly important initiative for customers, stakeholders, and regulatory authorities worldwide, finance teams shift their focus to collecting and reporting on ESG data. To accomplish this, they need to identify which information is essential to meeting standards and then develop a system for collecting it from multiple sources. However, the fragmented state of ESG data – and the fact that most of this data is collected manually – complicates the task, demanding additional time and resources to find a resolution.
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New data is generating at a rapid pace, especially unstructured information. There are growing demands for AI-enabled analytics, and business users want fast access to data insights. A modern data architecture should deliver on these trends and more, says a recent survey and report by TDWI Research:
“With data application workloads becoming numerous, varied and complex, organizations need a modernized data architecture that delivers faster and more agile paths to insights.”
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Advanced analytics is not just one particular model of technology, it is a collection of various powerful techniques used to gain actionable insights for better decision-making. In the evolving world of business, data has become the center of decision-making for any organization. Companies nowadays utilize their vast pool of information to extract meaningful insights with the help of advanced analytics rather than relying on their intuition. It is a robust tool, surpassing the limitations of conventional methods with its enhanced capabilities, utilizing modern techniques to uncover patterns, trends and correlations within the data.
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Autonomous finance is a revolutionary approach in the rapidly evolving world of technological and augmented tools that CFOs and heads of financial planning and analysis (FP&A) have at their disposal. Coined by industry analyst Gartner, it refers to self-learning software agents that automate business operations and corporate finances. Autonomous finance is increasingly permeating all aspects of financial management by optimizing and streamlining financial data gathering and analysis.
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Business intelligence (BI) integrates various components, including business analytics, data mining, visualization tools, infrastructure, and established methodologies, to facilitate organizations in making informed, data-driven decisions. In practical terms, modern business intelligence signifies having a holistic perspective of organizational data and leveraging it to instigate positive changes, eradicate inefficiencies, and swiftly adapt to market fluctuations or shifts in supply.

Basically, modern business intelligence solutions focus on giving users easy-to-use tools to analyze data, making sure the data is managed well on reliable platforms, helping non-technical users understand and use the data, and making the process of finding insights faster. The term “business intelligence” originated in 1989, coinciding with the emergence of computer-based decision-making models. Over time, these programs evolved to convert data into actionable insights, eventually culminating in specialized offerings from BI teams, which initially relied heavily on IT-centric service solutions.
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