Articles about Analytics

Organizations are sitting on troves of information yet struggle to leverage this data for quick decision-making. The challenge isn't just about having data, but working with it in its natural state -- which often includes ‘dirty data' not cleaned of typos or errors.

We spoke to CEO of analytics company WisdomAI, Soham Mazumdar, to find out more about this challenge and how businesses can deal with it.

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A new report finds that 76 percent of enterprises admit they've made business decisions without consulting available data because it was too difficult to access, creating a concerning gap between data availability and data-driven decision-making.

The report from Sisense with research firm UserEvidence shows that although 81 percent of organizations believe they have good or full control of their data, 64 percent acknowledge that they can't reliably access it for decision-making.

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For GenAI to live up to its promise reliable flow of data is key. AI models are only as good as the data pipeline connections bringing in quality data.

Outdated connections mean more hallucinations and untrustworthy results with data engineers hopelessly trying to manually integrate hundreds of AI data feeds. We spoke to Rivery co-founder and CEO Itamar Ben Hemo to discuss why good data pipelines are key to success.

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The app economy is big business. Apple’s App Store ecosystem alone generated a staggering $1.1 trillion in total billings and sales for developers in 2022. But as users demand more relevant and immediate experiences, often driven by AI, developers increasingly need competitive advantages to stand out. 

Real-time analytics, supercharged by generative AI, can provide a critical edge by allowing developers to extract key insights and quickly adapt their apps to reflect changing user expectations. But only 17 percent of enterprises today have the ability to perform real-time analysis on large volumes of data, and adoption remains slow. Meanwhile, even when companies are able to perform real-time analytics, there are several common mistakes that can prevent them from reaping its full benefits:

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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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In a little more than a decade, data analytics has been through several big transformations. First, it became digitized. Second, we witnessed the emergence of 'big data' analytics, driven partly by digitization and partly by massively improved storage and processing capabilities.

Finally, in the last couple of years, analytics has been transformed once again by emerging generative AI models that can analyze data at a previously unseen scale and speed.

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The majority of teams today are contending with too much data which means they struggle to generate meaningful insights from their information, and can become overwhelmed by the sheer volume.

We spoke to CallMiner CMO Eric Williamson who believes sourcing customer feedback from different sources might help solve the problem.

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Although many people fear that artificial intelligence could put their jobs at risk, a new study from Jitterbit shows that many see AI as offering new skills and personal growth opportunities.

Based on a survey by Censuswide of 1,022 full-time office workers in the UK and US, the study looks at how workers really feel about AI and the findings reveal a positive views of working with AI technology in professional settings.

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According to a new report, UK companies are prioritizing AI, with larger expected budget increases than in the US and Germany, and 90 percent considering AI a critical topic.

The report, from analytics database company Exasol, finds UK organizations expect to prioritize AI implementation through larger data and analytics budget increases over the next two to three years in the retail (+48 percent) and healthcare sectors (+100 percent).

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A new AI technology is emerging alongside LLMs -- Large graphical models (LGMs). An LGM is a probabilistic model that uses a graph to represent the conditional dependence structure between a set of random variables.

Organizations can use LGMs to model the likelihood of different outcomes based on many internal and external variables.

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With the emergence of generative AI technology, information management is undergoing rapid transformation.

The productivity of knowledge workers is critically important to the growth and profitability of businesses, and organizations are turning to solutions that help automate mundane, time-consuming tasks. We talked to Antti Nivala, founder and CEO of M-Files, to find out more aboput this new era of information management.

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According to a new report, 73 percent of IT leaders are still struggling to transform data into delivering significant business value.

The survey of 150 UK IT leaders by cloud consultancy Appsbroker & CTS finds 91 percent have a specific mandate from their board or executive team to make their organization more data-driven and data-centric.

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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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Process efficiency and cost-saving are top priorities for life sciences and high-tech executives, with greater emphasis placed on advanced analytics and artificial intelligence (AI) to achieve these priorities according to a new report.

The study from Model N shows three-quarters of executives say their current approach to revenue optimization needs improvement, and survey responses show plans to bolster revenue operations by incorporating advanced analytics (68 percent), AI (59 percent), and robotic process automation (46 percent).

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The data lakehouse has captured the imagination of modern enterprises looking to streamline their architectures, reduce cost and assist in the governance of self-service analytics.

From data mesh support to providing a unified access layer for analytics and data modernisation for the hybrid cloud, it offers plenty of business cases, but many organizations are unsure where to start building one.

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