Dirty data and why it’s a problem for business [Q&A]
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.
Poor data quality is the biggest barrier to AI in insurance
Almost three-quarters of insurance underwriters say fragmented, siloed, and unstructured data -- not technology -- is the main barrier to AI transformation.
New research carried out by Reuters for technology transformation specialist CI&T shows that data fragmentation, unstructured formats, and siloed systems are the real roadblocks to delivering faster, more accurate underwriting and pricing.
The practical approach to building a data mesh [Q&A]
As businesses continue to generate and rely on vast amounts of data, the traditional approach to managing that data is no longer sufficient.
Enter the concept of a data mesh -- a decentralized, domain-driven approach to data architecture that promises to transform how organizations handle and leverage their data. But the question remains: should a business create a data mesh? What value does it add, and what challenges does it help solve?
How data sovereignty is becoming mission critical to enterprises
New research shows that 30 percent of large enterprises have already made the strategic commitment to a sovereign AI and data platform, and 95 percent say it will be mission critical for them within the next three years.
The research by EDB interviewed more than 2,000 senior executives across 13 countries about how they are planning for the agentic AI world. The initial findings show that 30 enterprises a day are making strategic commitments to becoming sovereign AI and data platforms.
76 percent of enterprises make decisions without consulting data because it's too hard to access
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.
Only a fifth of companies confident their data is AI ready
A new survey, of 1,000 purchasing decision makers across the US, UK, France, and DACH (Germany, Austria and Switzerland) regions, reveals that while AI investment is the top spending priority for nearly half of businesses, only one-fifth of surveyed companies feel confident their data is AI-ready.
The research from Nasuni finds 96 per cent of respondents say they face challenges migrating their file data, creating a major roadblock for AI initiatives. There's also a misalignment in investment priorities, while nearly half of respondents cite AI as their top spending priority over the next 18 months, only a third will invest in the necessary cloud data management.
Data integrity and complexity are seen as top challenge for DBAs
A new survey looking at the challenges faced by database administrators (DBAs) shows 35 percent of organizations cite data integration as a top challenge.
Additionally, the study from Quest Software shows 42 percent of organizations report that data migrations are a significant obstacle to overcome, with complications in ensuring database reliability and performance.
What happens when data observability meets unstructured data? [Q&A]
In DevOps and IT circles, the word 'observability' has been bandied about for the past few years. Observability is one of those hot and trendy terms which also means different things to different people.
Yet the goal is generally the same: how can we observe our environment and then proactively and even automatically make fixes to things that aren't working, are anomalous, suspicious and/or could potentially cause a disastrous outcome? Such outcomes could include a network failure, a security breach, a server reaching capacity, or in the unstructured data management world -- something else entirely.
Enterprises using inaccurate data to make business decisions
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.
60 percent of organizations fail to keep pace with AI-driven data changes
According to a new report, 60 percent of respondents claim that their organization is failing to keep pace with data changes resulting from AI demands.
In addition, the study from Immuta shows that traditional data architecture challenges persist, with nearly half of organizations identifying compliance and privacy as primary data concerns, and 64 percent citing significant challenges in providing timely and secure access to data for authorized users.
Why strong data foundations are essential to implementing AI [Q&A]
Lots of organizations are rushing to embrace AI and hoping to deliver business value. But AI is only as good as the data that underpins it.
We spoke to Julian LaNeve, chief technology officer of Astronomer, to discuss why it's important to fix the foundations before implementing AI solutions.
Understanding and taking control of your data ecosystem [Q&A]
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.
The top barriers to AI success and how to overcome them [Q&A]
With the intense interest in AI and its rapid pace of embrace, organizations are under pressure to swiftly evaluate their data architecture. AI architects urgently need solutions that harness AI to boost revenue streams and streamline operational efficiencies, all while navigating the potential barriers to their success.
David Flynn, co-founder and CEO of Hammerspace, a specialist in the use and preservation of unstructured data, recently shed light on the growing complexity IT teams face in managing data pipelines. This complexity is further compounded as organizations integrate LLMs into AI applications, underscoring the significant obstacles to successful AI adoption.
AI impacts on data storage infrastructure
As we've already seen today organizations are struggling with the increasing demands of data infrastructure. Another new report from MinIO highlights how organizations are leveraging object storage for AI, machine learning (ML), and data-intensive workloads.
The survey of over 650 IT leaders 70 percent of enterprise data is in object storage today and this is expected to grow to 75 percent over the next two years.
Enterprises struggle to meet data infrastructure demands
Internal IT teams are being pushed to the brink, balancing the demands of modern data management with limited resources according to a new report.
The study from DataStrike gathered insights from nearly 200 IT leaders and decision-makers, it finds that 55 percent of organizations rely solely on internal IT teams to manage their data infrastructure, with only a small fraction (19 percent) outsourcing these critical functions.
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