Articles about data analytics

As data demands across organizations intensify they need to scale productivity and enable business users to explore data independently.

Cube is launching an agentic AI analytics platform built on a universal semantic layer which allows it to operate autonomously within enterprise frameworks, automating work while preserving trust, governance, and transparency.

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According to a new study of 750 business and tech leaders, 58 percent say key business decisions are based on inaccurate or inconsistent data most of the time, if not always.

The research from IT consulting and digital services provider SoftServe shows the majority don’t understand the value of their data with 65 percent of all respondents believing no one at their organization understands all the data collected and how to access it.

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Data comes in many forms, it may be structured, it may be unstructured, it could be sensitive or purely statistical. Whatever it looks like you can only benefit from it if you know what you have, where it can be found and how to access it.

We spoke to Brett Hurt, CEO and co-founder of data.world to discuss how enterprises can understand their data and derive the maximum value from it.

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One of the features of AI is its ability to process large volumes of data to identify patterns and make information more accessible.

We spoke to Igor Jablokov, CEO and founder of Pryon, about how enterprises can take advantage of this ability and make better use of their data.

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The increased need for real-time analytics is driven by the rise of the on-demand economy, where consumer expectations for immediate access to products, services, and information are transforming how businesses operate and compete.

We spoke to Kishore Gopalakrishna, co-founder and CEO of StarTree, to discuss the need for real-time data capabilities, the strategic utilization of real-time data to enhance operational efficiency and competitiveness, and the essential technology and operational considerations for building a robust analytics infrastructure.

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Data Analytics & Visualization All-in-One For Dummies collects the essential information on mining, organizing, and communicating data, all in one place.

Clocking in at around 850 pages, this tome of a reference delivers eight books in one, so you can build a solid foundation of knowledge in data wrangling. Data analytics professionals are highly sought after these days, and this book will put you on the path to becoming one.

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Generative AI is making its presence felt in more and more areas, but there are well-founded concerns about the accuracy of information that it provides.

Is it possible to provide the convenience of a large language model AI system, with the logic and accuracy of advanced analytics? Arina Curtis, CEO and co-founder of DataGPT, thinks so. We spoke to her to find out more.

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ChatGPT and other LLMs are designed to train and learn from unstructured data -- namely, text. This has enabled them to support a variety of powerful use cases.

However, these models struggle to analyze structured data, such as numerical and statistical information organized in databases, limiting their potential.

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A new survey of 500 data and IT leaders who manage data workloads of 150 terabytes or more shows that growing volumes of data are increasingly driving business priorities.

The study, from hyperscale data analytics platform Ocient, finds 90 percent of IT and data leaders are planning to remove or replace existing big data and analytics technologies in the next six to 12 months.

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Data is increasingly the lifeblood of businesses, but according to Gartner, poor data management costs organizations an average of $12.9 million a year.

With the launch of a new AI-powered SaaS platform that simplifies organizing and leveraging data without an engineer, Pliable aims to open up data access to businesses.

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Organizations have more data than ever, but unlocking the information it contains in order to make decisions can be a challenge.

The marriage of of real-time analytics and AI with vector search is a potential game changer for any business that has large amounts of data to crunch. We spoke to Rockset CEO and co-founder Venkat Venkataramani to find out more.

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Over the past decade data modeling -- setting up data structures aligned to business requirements -- has tended to take something of a back seat as businesses have rushed to bring products to market.

But we're producing more data than ever and need ways to process it effectively. That's why Satish Jayanthi, CTO and co-founder at Coalesce, believes it's time for data modeling to make a comeback in enterprise strategy. We spoke to him to find out more.

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In difficult economic times businesses need to make decisions quickly and data is a key part of enabling those choices.

But research from analytics cloud platform Alteryx shows enterprises are struggling to make timely, insight-driven decisions because of 'analysis paralysis' caused by issues around ownership of and access to data.

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Today, companies utilize data analytics not only to understand past performance but also to predict future trends and make strategic decisions. However, as the volume and complexity of data continue to grow, many companies find it challenging to manage their data analysis needs in-house. Outsourcing data analytics has emerged as a potential solution to this challenge, and it is expected to become an increasingly popular option.

According to a report by Allied Market Research, the data analytics outsourcing market is projected to see an impressive compound annual growth rate of 29.4 percent between 2017 and 2023. In this article, we will explore the pros and cons of outsourcing data analytics, thereby providing insights to assist businesses in making informed decisions regarding this critical aspect of their operations.

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New research from hybrid data company Cloudera reveals that organizations currently estimate they are not using 33 percent of their data effectively.

The survey 850 IT decision makers (ITDMs) across the EMEA region shows 72 percent of respondents agree that having data sitting across different cloud and on-premises environments makes it complex to extract value from all the data in their organization.

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