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This article was published as a part of the Data Science Blogathon. Photo by Christina Morillo from Pexels Introduction The current decade is a time of unprecedented growth in data-driven technologies with unlimited opportunities.
For CIOs leading enterprise transformations, portfolio health isnt just an operational indicator its a real-time pulse on time-to-market and resilience in a digital-first economy. In todays digital-first economy, enterprise architecture must also evolve from a control function to an enablement platform.
As such, the data on labor, occupancy, and engagement is extremely meaningful. Here, CIO Patrick Piccininno provides a roadmap of his journey from data with no integration to meaningful dashboards, insights, and a data literate culture. You ’re building an enterprisedata platform for the first time in Sevita’s history.
Research from Gartner, for example, shows that approximately 30% of generative AI (GenAI) will not make it past the proof-of-concept phase by the end of 2025, due to factors including poor data quality, inadequate risk controls, and escalating costs. [1] Reliability and security is paramount.
Demand for data scientists is surging. With the number of available data science roles increasing by a staggering 650% since 2012, organizations are clearly looking for professionals who have the right combination of computer science, modeling, mathematics, and business skills. Collecting and accessing data from outside sources.
In today’s data-driven world, large enterprises are aware of the immense opportunities that data and analytics present. Yet, the true value of these initiatives is in their potential to revolutionize how data is managed and utilized across the enterprise.
This is particularly true with enterprise deployments as the capabilities of existing models, coupled with the complexities of many business workflows, led to slower progress than many expected. Focus on data assets Building on the previous point, a companys data assets as well as its employees will become increasingly valuable in 2025.
To address this, Gartner has recommended treating AI-driven productivity like a portfolio — balancing operational improvements with high-reward, game-changing initiatives that reshape business models. Gartner’s data revealed that 90% of CIOs cite out-of-control costs as a major barrier to achieving AI success.
In enterprises, we’ve seen everything from wholesale adoption to policies that severely restrict or even forbid the use of generative AI. AI users say that AI programming (66%) and data analysis (59%) are the most needed skills. Few nonusers (2%) report that lack of data or data quality is an issue, and only 1.3%
Speaker: Nik Gowing, Brenda Laurel, Sheridan Tatsuno, Archie Kasnet, and Bruce Armstrong Taylor
In this session, participants will see how science data from such sources as NASA and NOAA, combined with local data inputs, can be used to both exponentially improve and accelerate net-zero carbon, climate positive and regenerative outcomes.
I recently saw an informal online survey that asked users which types of data (tabular, text, images, or “other”) are being used in their organization’s analytics applications. The results showed that (among those surveyed) approximately 90% of enterprise analytics applications are being built on tabular data.
TL;DR: Enterprise AI teams are discovering that purely agentic approaches (dynamically chaining LLM calls) dont deliver the reliability needed for production systems. A shift toward structured automation, which separates conversational ability from business logic execution, is needed for enterprise-grade reliability.
AI is clearly making its way across the enterprise, with 49% of respondents expecting that the use of AI will be pervasive across all sectors and business functions. Yet, this has raised some important ethical considerations around data privacy, transparency and data governance.
Accenture reports that the top three sources of technical debt are enterprise applications, AI, and enterprise architecture. These areas are considerable issues, but what about data, security, culture, and addressing areas where past shortcuts are fast becoming todays liabilities?
Programmers may not need to know how to sort, but every programmer needs to understand how to solve problems with divide and conquer, how to use recursion, how to estimate performance, how to operate on a data structure without creating a new copythere are all sorts of techniques and ideas embedded in sorting that a programmer really has to know.
1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. 10) Data Quality Solutions: Key Attributes.
I previously explained that data observability software has become a critical component of data-driven decision-making. Data observability addresses one of the most significant impediments to generating value from data by providing an environment for monitoring the quality and reliability of data on a continual basis.
Noting that companies pursued bold experiments in 2024 driven by generative AI and other emerging technologies, the research and advisory firm predicts a pivot to realizing value. Their top predictions include: Most enterprises fixated on AI ROI will scale back their efforts prematurely.
A sharp rise in enterprise investments in generative AI is poised to reshape business operations, with 68% of companies planning to invest between $50 million and $250 million over the next year, according to KPMGs latest AI Quarterly Pulse Survey. However, only 12% have deployed such tools to date.
From customer service chatbots to marketing teams analyzing call center data, the majority of enterprises—about 90% according to recent data —have begun exploring AI. For companies investing in data science, realizing the return on these investments requires embedding AI deeply into business processes.
in 2025, one of the largest percentage increases in this century, and it’s only partially driven by AI. growth this year, with data center spending increasing by nearly 35% in 2024 in anticipation of generative AI infrastructure needs. Data center spending will increase again by 15.5% trillion, builds on its prediction of an 8.2%
Organizations will always be transforming , whether driven by growth opportunities, a pandemic forcing remote work, a recession prioritizing automation efficiencies, and now how agentic AI is transforming the future of work. 2025 will be the year when generative AI needs to generate value, says Louis Landry, CTO at Teradata.
We may look back at 2024 as the year when LLMs became mainstream, every enterprise SaaS added copilot or virtual assistant capabilities, and many organizations got their first taste of agentic AI. AI at Wharton reports enterprises increased their gen AI investments in 2024 by 2.3
research firm Vanson Bourne to survey 650 global IT, DevOps, and Platform Engineering decision-makers on their enterprise AI strategy. The Nutanix State of Enterprise AI Report highlights AI adoption, challenges, and the future of this transformative technology. AI applications rely heavily on secure data, models, and infrastructure.
A Name That Matches the Moment For years, Clouderas platform has helped the worlds most innovative organizations turn data into action. As the AI landscape evolves from experiments into strategic, enterprise-wide initiatives, its clear that our naming should reflect that shift. This isnt just a new label or even AI washing.
Data is the lifeblood of the modern insurance business. Yet, despite the huge role it plays and the massive amount of data that is collected each day, most insurers struggle when it comes to accessing, analyzing, and driving business decisions from that data. There are lots of reasons for this.
According to AI at Wartons report on navigating gen AIs early years, 72% of enterprises predict gen AI budget growth over the next 12 months but slower increases over the next two to five years. CIOs should speak to sales leaders to identify areas where sales metrics are underperforming and where gen AI-driven improvements can drive revenue.
In a global economy where innovators increasingly win big, too many enterprises are stymied by legacy application systems. 2] The myriad potential of GenAI enables enterprises to simplify coding and facilitate more intelligent and automated system operations. The foundation of the solution is also important.
The company provides industry-specific enterprise software that enhances business performance and operational efficiency. Infor offers applications for enterprise resource planning, supply chain management, customer relationship management and human capital management, among others.
In today’s data-driven world, organizations need real-time access to up-to-date, high-quality data and analysis to keep pace with changing market dynamics and make better strategic decisions. By mining meaningful insights from enterprisedata quickly, they gain a competitive advantage in the market.
Rule 1: Start with an acceptable risk appetite level Once a CIO understands their organizations risk appetite, everything else strategy, innovation, technology selection can align smoothly, says Paola Saibene, principal consultant at enterprise advisory firm Resultant. Cybersecurity must be an all-hands-on-deck endeavor.
Data is the foundation of innovation, agility and competitive advantage in todays digital economy. As technology and business leaders, your strategic initiatives, from AI-powered decision-making to predictive insights and personalized experiences, are all fueled by data. Data quality is no longer a back-office concern.
One of the points that I look at is whether and to what extent the software provider offers out-of-the-box external data useful for forecasting, planning, analysis and evaluation. Enterprises do not operate in a vacuum, and things happening outside an organizations walls directly impact performance.
Task automation platforms initially enabled enterprises to automate repetitive tasks, freeing valuable human resources for more strategic activities. Enterprises that adopt RPA report reductions in process cycle times and operational costs.
Because data management is a key variable for overcoming these challenges, carriers are turning to hybrid cloud solutions, which provide the flexibility and scalability needed to adapt to the evolving landscape 5G enables. From customer service to network management, AI-driven automation will transform the way carriers run their businesses.
Uber no longer offers just rides and deliveries: It’s created a new division hiring out gig workers to help enterprises with some of their AI model development work. Data labeling in particular is a growing market, as companies rely on humans to check out data used to train AI models.
But as enterprises increasingly experience pilot fatigue and pivot toward seeking practical results from their efforts , learnings from these experiments wont be enough the process itself may need to produce more targeted success rates. A lot of efforts are not gen AI, but they are trying to inject some gen AI things into it, he explains.
Schumacher and others believe AI can help companies make data-driven decisions by automating key parts of the strategic planning process. This process involves connecting AI models with observable actions, leveraging data subsequently fed back into the system to complete the feedback loop,” Schumacher said.
In at least one way, it was not different, and that was in the continued development of innovations that are inspired by data. This steady march of data-driven innovation has been a consistent characteristic of each year for at least the past decade.
Despite all the interest in artificial intelligence (AI) and generative AI (GenAI), ISGs Buyers Guide for Data Platforms serves as a reminder of the ongoing importance of product experience functionality to address adaptability, manageability, reliability and usability. This is especially true for mission-critical workloads.
Agentic AI was the big breakthrough technology for gen AI last year, and this year, enterprises will deploy these systems at scale. According to a January KPMG survey of 100 senior executives at large enterprises, 12% of companies are already deploying AI agents, 37% are in pilot stages, and 51% are exploring their use.
And we gave each silo its own system of record to optimize how each group works, but also complicates any future for connecting the enterprise. Data and workflows lived, and still live, disparately within each domain. In this new future of AI, can we really maximize data and insights using disconnected, siloed, dated data?
The market for enterprise applications grew 12% in 2023, to $356 billion, with the top 5 vendors — SAP, Salesforce, Oracle, Microsoft and Intuit — commanding a 21.2% market share between them, according to International Data Corp. With just 0.2% With just 0.2%
By eliminating time-consuming tasks such as data entry, document processing, and report generation, AI allows teams to focus on higher-value, strategic initiatives that fuel innovation. Similarly, in 2017 Equifax suffered a data breach that exposed the personal data of nearly 150 million people.
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