This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Data readiness and governance are critical for AI success Getting these foundational aspects of AI governance in place will be critical to successful adoption, and for unlocking an opportunity that the Tech Council of Australia estimates could contribute $45 billion to $115 billion per year to the Australian economy by 2030.
Canada, China, and the United States are among the countries in which many organizations began their AI journeys early, supported by government initiatives. As an extension of the country’s Vision 2030, the Saudi Data and AI Authority (SDAIA) was established in 2019, followed by the release of the National Strategy for Data and AI in 2020.
Canada, China, and the United States are among the countries in which many organizations began their AI journeys early, supported by government initiatives. As an extension of the country’s Vision 2030, the Saudi Data and AI Authority (SDAIA) was established in 2019, followed by the release of the National Strategy for Data and AI in 2020.
In the coming years, the region will see a surge in investments focused on AI capabilities, spanning areas such as datagovernance, cloud infrastructures, foundation models, and the architecture needed to support these advanced technologies.
Technology leaders want to harness the power of their data to gain intelligence about what their customers want and how they want it. This is why the overall data and analytics (D&A) market is projected to grow astoundingly and expected to jump to $279.3 billion by 2030. Is it wholly and easily auditable?
In Asia, Singapore aims to green 80% of its buildings by 2030 as part of its sustainability initiative. Challenges to overcome to realize the smart city Securing vast networks of interconnected devices necessitates robust cybersecurity protocols and continuous vulnerability assessments to mitigate cyberattacks and data breaches.
The goal is to reach a climate-neutral economy in the EU by 2050, with an intermediate milestone of a 55% reduction in emissions by 2030. Companies in Europe are required to start scope 3 reporting in 2024 with data from 2023, so collecting that data starts now. DataGovernance, Green IT, IT Governance.
Gartner has predicted that by 2030, upwards to 80% of project management work will be automated by artificial intelligence (AI). The project management profession, like many others, faces an emergent threat from artificial intelligence (AI)-based technologies. Project managers are likely to experience a major upheaval during the 2020s.
Experts predict the AI market will grow from $184 billion in 2024 to $826 billion by 2030. And considering the wide range of use cases for AI tools, that’s not much of a surprise.
Synthetic data will be invaluable for avoiding privacy violations in the future, and Gartner predicts that by 2025, synthetic data will enable organizations to avoid 70% of privacy violation sanctions. Gartner predicts that by 2030, synthetic data will completely overshadow real data in AI models. DataGovernance.
Europe’s Digital Decade declaration targets for 2030 outline the digital rights and principles complementing data protection, privacy legislation and other rights. This includes Principle 4, “citizens able to engage and have control over their own data” (including their health data).
trillion to the global economy in 2030, more than the current output of China and India combined.” AI platforms assist with a multitude of tasks ranging from enforcing datagovernance to better workload distribution to the accelerated construction of machine learning models. trillion in value.
The Bureau of Labor Statistics projects the job outlook for data scientists to grow 22% from 2020 to 2030. It is clear that the need for data scientists and experts is not going away. The job AI Specialist, which is closely related, is listed at #1 with 74% annual growth.
In the energy and utilities sector, sustainability goals, such as Saudi Arabias Vision 2030 and UAEs Net Zero 2050, will drive investment in smart grids, renewable energy, and AI-driven energy efficiency solutions. Governments and enterprises will leverage AI for economic diversification, operational efficiency, and enhanced citizen services.
AI pioneer Andrew Ng recently underscored that robust data engineering is foundational to the success of data-centric AI —a strategy that prioritizes data quality over model complexity.
Adversarial attacks, data poisoning and generative AI risks exploit datagovernance and security gaps. Datagovernance gaps. Poor data management can lead to compromised AI integrity. Data poisoning. Corrupt training data leads to inaccurate AI predictions. Lack of data lineage.
Axial: The Strategic Backbone for Bold Ambition 2030 An overview of the Axial program At the heart of AstraZeneca’s Bold Ambition 2030 lies “Axial”, an ambitious initiative to modernize its operations and propel the pharmaceutical giant toward industry leadership.
A decision made with AI based on bad data is still the same bad decision without it. Build on data platform foundations first to enable machine learning Global spend on data platforms is expected to increase at a compound annual growth rate of 14.9% through 2030 and clearly, data quality and trust are driving that investment.
We organize all of the trending information in your field so you don't have to. Join 42,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content