InformationWeek:Radical Automation of ITSM (March 12th)
IT - Big Data

Traditional analytics has long been the cornerstone of business intelligence. It involves gathering historical data, performing statistical analysis, and drawing conclusions to make informed decisions.

While this approach, which relies on predefined rules and static models, has served organizations well, it has limitations. Traditional analytics excels at providing insights into past performance, but falls short in predicting future trends or prescribing optimal actions and, in a rapidly changing world, this retrospective view can be a significant disadvantage.

Artificial intelligence, on the other hand, represents a quantum leap in the world of data-driven decision-making. Unlike traditional analytics, AI can analyze vast amounts of data in real-time, allowing businesses to detect emerging patterns and trends that would be impossible to identify through traditional means. This predictive capability empowers organizations to proactively respond to market shifts and consumer behavior, staying one step ahead of the competition.


What Is Data Classification? Your Ultimate Guide
Data, Tuesday, February 6th, 2024
Data classification is a component of the data management process in which data is categorized based on various characteristics to reinforce data security, aid regulatory compliance, and enable efficient data management. Data classification helps companies comply with regulations, cut costs, manage risks, and maintain data integrity.

This process typically includes identifying and categorizing data types and implementing security measures accordingly. Generally, data management teams and executives or IT professionals must work together to classify data and ensure its alignment with business policies.

Despite its technical nature, understanding how to perform data classification is a must for organizations, as it is a key element of a comprehensive data governance strategy.


Sony AI Big Data Industry Predictions For 2024
insideBIGDATA, Monday, February 5th, 2024
Our friends over at Sony AI have prepared a special set of compelling technology predictions for the year ahead.

The Sony AI team is comprised of researchers and leaders with backgrounds in deep reinforcement learning, data science, law, privacy and security, and more. They each offer different perspectives on topics related to AI ethics and policy, the use of AI to augment creativity and scientific research, emerging AI training methods, and more. From the company's point of view 2024 should be quite a year! Enjoy these special perspectives from one of our industry's best known movers and shakers.

A new survey of data leaders by Informatica points to data quality as the number one obstacle to implementing generative AI. The number of data management tools that companies are using, as well as the fact that a large fraction of companies are juggling more than 1,000 separate data sources, also are weighing on GenAI initiatives.

According to Informatica's CDO Insights 2024 report, which is based on a survey of 600 data leaders at large companies around the world, 45% of companies have already implemented GenAI in some form, while another 53% plan to implement it (with 36% saying they will do so within two years). That leaves just 2% of firms saying GenAI isn't for them-a remarkably low number for a technology that most people didn't know existed 14 months ago.

10 Leading AI Tools For Data Analysis In 2024
Analytics Insight, Monday, February 5th, 2024
Master data analysis with 2024's top 10 AI tools for precision and efficiency

In the ever-evolving landscape of data analysis, the integration of artificial intelligence (AI) and machine learning (ML) techniques has brought about a significant transformation. As we step into 2024, a plethora of AI tools promise to revolutionize the field, making data analysis more efficient, intuitive, and powerful. Let's explore the top 10 best AI tools for data analysis in 2024 that are set to redefine the way we extract insights from data.

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