WALTHAM, MA, Feb 16, 2023 – Software intelligence company Dynatrace announced that it is extending its platform's Grail data lakehouse beyond logs and business events to deliver new support for metrics, distributed traces, and multicloud topology and dependencies. This expands Grail's ability to store, process, and analyze the enormous volume and variety of data from modern cloud ecosystems while retaining its context and without having to structure or rehydrate it. Dynatrace also unveiled a new user experience for its Software Intelligence Platform, featuring powerful dashboarding capabilities and a visual interface to help drive tighter collaboration between development and business teams. This UX powers Dynatrace Notebooks, a new interactive document capability that allows IT, development, security, and business teams to collaborate using code, text, and rich media to build, evaluate, and share insights from exploratory, causal-AI-based analytics projects. These new capabilities add AI-powered graph analytics for custom queries to the powerful analytics that are already available out of the box with the Dynatrace platform.

In 2017, The Economist declared that data, rather than oil, had become the world's most valuable resource. The refrain has been repeated ever since. Organizations across every industry have been and continue to invest heavily in data and analytics. But like oil, data and analytics have their dark side. According to CIO's State of the CIO 2022 report, 35% of IT leaders say that data and business analytics will drive the most IT investment at their organization this year. And 20% of IT leaders say machine learning/artificial intelligence will drive the most IT investment.

In 2017, The Economist declared that data, rather than oil, had become the world's most valuable resource. The refrain has been repeated ever since. Organizations across every industry have been and continue to invest heavily in data and analytics. But like oil, data and analytics have their dark side. According to IDG's State of the CIO 2021 report, 39% of IT leaders say that data analytics will drive the most IT investment at their organization this year, up from 37% in 2020. Insights gained from analytics and actions driven by machine learning algorithms can give organizations a competitive advantage, but mistakes can be costly in terms of reputation, revenue, or even lives.

In 2017, The Economist declared that data, rather than oil, had become the world's most valuable resource. The refrain has been repeated ever since. Organizations across every industry have been and continue to invest heavily in data and analytics. But like oil, data and analytics have their dark side. According to IDG's State of the CIO 2020 report, 37 percent of IT leaders say that data analytics will drive the most IT investment at their organization this year. Insights gained from analytics and actions driven by machine learning algorithms can give organizations a competitive advantage, but mistakes can be costly in terms of reputation, revenue, or even lives.

National guidance is urgently needed to oversee the police's use of data-driven technology amid concerns it could lead to discrimination, a report has said. The study, published by the Royal United Services Institute (Rusi) on Sunday, said guidelines were required to ensure the use of data analytics, artificial intelligence (AI) and computer algorithms developed "legally and ethically". Forces' expanding use of digital technology to tackle crime was in part driven by funding cuts, the report said. Officers are battling against "information overload" as the volume of data around their work grows, while there is also a perceived need to take a "preventative" rather than "reactive" stance to policing. Such pressures have led forces to develop tools to forecast demand in control centres, "triage" investigations according to their "solvability" and to assess the risks posed by known offenders. Examples of the latter include Hampshire police's domestic violence risk-forecasting model, Durham police's Harm Assessment Risk Tool (Hart) and West Midlands police's draft integrated offender management model.

Our data-centric world is driving many organizations to apply advanced analytics that use artificial intelligence (AI). AI provides intelligent answers to challenging business questions. AI also enables highly personalized user experiences, built when data scientists and analysts learn new information from data that would otherwise go undetected using traditional analytics methods. AI-driven analytics delve more deeply into organizational data, deriving smarter insights that can give businesses a powerful competitive edge. A well-considered data strategy is essential from the start.

Big Data Analytics is a buzz word today and the skills in its related technologies such as artificial intelligence, machine learning, cloud computing and robotics for automation are driving the industry's growth. As per the recent report, the IT companies are laying off their employees in thousands and at the same time looking for these skills. It says, "After thousands were fired by Infosys and Cognisant in the past six months, as many as 200,000 more may be at risk of losing their jobs in the next year as companies seek people with new skillsets, according to recruitment experts." Two decades ago the people who face layoffs today had skills which were similarly valued. I lived through the IT revolution that began in the mid-1990s with the spread of the Internet into business and consequent changes in the way the business was done.

The amount of data available to organizations every day continues to proliferate at a staggering volume. But technologies such as analytics and artificial intelligence (AI) have the potential to help businesses make better use of these massive volumes of data. In an age of collaboration between humans and machines--what we call the "Age of With"1--organizations can gain advantage by designing systems in which humans and machines work together to improve the speed and quality of decision-making. But not every organization is optimizing the opportunities available in the Age of With. Some do little or nothing with data to aid their decision-making. Others carry out analytics projects in pockets of the business.

This article broadly describes the capabilities that constitute an enterprise analytics program or competency. The intention initially, was to provide tips on mitigating challenges encountered in implementing an analytics practice - but that is going to be relegated to a future article. IT projects in general, and analytics projects, in particular, are notoriously unsuccessful or "challenged". Focusing attention on the following short list prior to embarking on an analytics project or enterprise will help mitigate many challenges and obstacles encountered when delivering value through analytics projects. These outcomes are dependent on several factors, and achieving them requires implementing and orchestrating some, or all of the following core capabilities listed below.

In classic comic form, the Fantastic Four of Data Science Project Preparation were originally introduced via cameo in the Data Avengers... Assemble! So if you happen to have that post, hang on to it. It will undoubtedly be worth something some day. In Marvel comics, the Fantastic Four are a 4 person collective, the members of which gained superpowers when exposed to cosmic rays while on a space flight. Reed Richards aka Mr. Fantastic can stretch, reshape, and contort his body in inhuman ways, and is the group leader.