Applications
How AI-enabled tools simplify data management
Artificial Intelligence (AI) has truly changed the game when it comes to data management. From my perspective, the integration of AI into these processes has been nothing short of revolutionary. By automating complex tasks, enhancing data quality, and providing insightful analytics, AI has made data management more efficient and effective. AI technologies like machine learning algorithms and natural language processing (NLP) are now essential for handling, processing, and analyzing data. They excel at tasks such as data integration, data cleansing, data classification, and data governance.
Enhancing data quality
One of the biggest challenges organisations face is poor data quality, which, according to Gartner, costs an average of $15 million per year. From my experience, data profiling—analyzing data to understand its structure, content, and quality can be transformative. AI tools elevate this by automatically examining datasets to uncover patterns, anomalies, and inconsistencies. AI-powered data cleaning tools use machine learning algorithms to detect and fix errors, remove duplicates, and resolve inconsistencies, ensuring the data is accurate and complete. These tools also standardize data formats, promoting consistency across various sources. This results in high-quality data that organizations can depend on for their analytics and decision-making processes.
Automating data integration and data pipeline
Data integration, which involves combining data from various sources into a unified view, can be incredibly complex and time-consuming. AI simplifies this process by automating the extraction, transformation, and loading (ETL) of data. AI-driven ETL tools recognize patterns in data and adapt to changes in data sources, minimizing the need for manual intervention. For example, traditional data mapping requires extensive manual effort to match data fields from different sources. AI-powered tools automatically map data fields by understanding the context and relationships between them. Additionally, AI automates various stages of the data pipeline, from data ingestion to transformation and storage, reducing manual intervention, minimizing errors, and accelerating data processing. This end-to-end automation enhances the efficiency and reliability of data pipelines, which I’ve seen make a significant difference in operational productivity.
Facilitating data classification and tagging
One of the most tedious tasks in data management is data classification and tagging. AI-enabled tools have made this much simpler by automating the process, making it easier to locate and retrieve relevant information. These tools use NLP and machine learning algorithms to analyze the content and context of data, assigning appropriate tags and categories. For instance, in the healthcare industry, AI can classify and tag patient records based on diagnosis, treatment, and other criteria. This enables healthcare providers to quickly access necessary information, improving patient care and operational efficiency. In my opinion, this capability is transformative for any industry dealing with large amounts of data.
Strengthening data governance
Data governance is another area where AI has made a significant impact. AI enhances data governance by providing real-time monitoring and enforcement of data policies. AI-driven tools can detect and flag potential data breaches, compliance violations, and other issues, allowing organizations to take immediate action. Additionally, AI helps maintain data lineage, tracking the origin, movement, and transformation of data. This is particularly important for industries with stringent regulatory requirements, such as finance and healthcare. By maintaining a clear record of data lineage, organizations can ensure transparency and accountability in their data management practices. From my perspective, this level of oversight is invaluable.
Industry examples of AI-enabled data management
Looking at real-world examples, it’s clear that AI-enabled tools are making a significant difference across various industries. For instance, JPMorgan Chase uses AI to analyze and categorize vast amounts of financial documents, significantly reducing the time and effort required for manual processing. The Mayo Clinic employs AI to streamline data integration from various sources, enhancing patient care. Walmart uses AI to manage its extensive inventory data, ensuring timely restocking and minimizing stockouts. These examples illustrate how AI can be leveraged to improve efficiency and effectiveness in data management.
In conclusion, AI-enabled tools are transforming data management by automating processes, improving data quality, and providing actionable insights. These advancements not only enhance efficiency but also enable organizations to make more informed decisions based on accurate and reliable data. From my experience, adopting AI in data management is not just beneficial but essential for staying competitive in today’s data-driven world.
The article has been authored by Compunnel Inc’s director of data strategy and data architecture, Bhupendra Kumar Verma.
Applications
Cloud nine in the capital Bharathcloud plugs Delhi into its AI plans
MUMBAI: Bharathcloud is bringing its cloud closer to power. The Hyderabad-based sovereign AI cloud services provider has opened its Delhi office, marking its formal entry into North India and setting the stage for its next phase of growth.
The expansion comes as India’s digital transformation fuels rising demand for AI-ready cloud infrastructure, driven by wider adoption of artificial intelligence, machine learning, the Internet of Things and data-heavy applications. With the new office, Bharathcloud plans to onboard more than 100 employees in 2026, strengthening its workforce to support customers across government, enterprises, MSMEs and social sectors.
The Delhi presence is expected to sharpen the company’s engagement with organisations seeking secure, scalable and cost-efficient cloud platforms that comply with India’s data sovereignty requirements. It also positions Bharathcloud closer to policy, public sector and enterprise decision-makers in the region.
Founded in Hyderabad, Bharathcloud offers AI-ready cloud infrastructure including Kubernetes-as-a-Service, zero-trust security architecture and multi-level data protection frameworks. Its platform supports AI and ML workloads, blockchain application migration from hyperscalers and distributed data management, with an emphasis on reliability, low latency and operational continuity.
“With the Delhi expansion, we are positioning Bharathcloud to engage more closely with AI-driven enterprises and technology hubs in North India,” said Bharathcloud co-founder Rahul Takallapally. He added that the move would help nurture local cloud and AI talent while accelerating the adoption of secure and resilient AI infrastructure across sectors.
The company currently operates in Hyderabad, Bengaluru, Mumbai, Kolkata, Lucknow and Chennai, employing over 200 people and serving more than 1,500 clients across manufacturing, healthcare, financial services, IT and media. Aligned with national initiatives such as Digital India and Make in India, Bharathcloud continues to focus on building indigenous AI-cloud infrastructure to support data localisation and the country’s growing appetite for next-generation digital solutions.
With its Delhi office now live, the company is signalling a clear intent: to make sovereign, AI-ready cloud infrastructure not just an alternative, but a mainstream choice for India’s north as well as its tech capitals.
Applications
Meta forecasts up to $135 billion capex in 2026
CALIFORNIA: Meta Platforms is going all in. The Instagram and Facebook owner has sharply raised its capital expenditure for 2026 to between $115 billion and $135 billion, nearly double last year’s spend, signalling CEO Mark Zuckerberg’s aggressive push toward artificial superintelligence. Investors cheered, sending shares higher, buoyed by robust advertising growth.
Speaking to analysts, Zuckerberg called 2026 a “pivotal year” for Meta, highlighting the focus on delivering highly personalised AI capabilities while reshaping internal operations.
The spend surge is driven by infrastructure costs, higher depreciation from AI data centres, and rising operating expenses linked to compute-intensive workloads. Meta has secured capacity deals with external providers including Alphabet, CoreWeave and Nebius, though capacity constraints are expected through much of the year, according to chief financial officer susan li.
Meta’s fourth-quarter performance underpinned confidence in the strategy. Advertising revenue, still the core engine, jumped 24 per cent year on year to $58.14 billion, up from $46.78 billion a year earlier. Strong ad cash flows helped the company beat earnings expectations and issue a first-quarter revenue forecast of $53.5–$56.5 billion, well above analyst estimates.
Despite the ad boom, capital expenditure surged 49 per cent, contributing to a decline in operating margin as infrastructure costs accelerated. Meta has been able to fund its AI ambitions largely through advertising, which benefits from AI-driven improvements in targeting and campaign automation. New monetisation channels on WhatsApp and Threads, and competition in short-form video via Instagram Reels, have further strengthened the ad engine.
Meta also projected total expenses for 2026 between $162 billion and $169 billion, reflecting infrastructure costs and rising employee compensation as the company hires aggressively for AI roles in a tight talent market.
“2026 will redefine how Meta works as AI reshapes teams and productivity,” zuckerberg said, underscoring the company’s commitment to superintelligence, a theoretical stage where machines outperform humans across a broad range of tasks.
Market watchers said investors appear comfortable with Meta’s high-stakes strategy, noting that generative AI returns may take time, but the company’s advertising cash flows are strong enough to absorb heavy spending. The outlook contrasts with Microsoft, which also ramped up capital expenditure but saw shares fall amid modest cloud growth.
Meta is charging full throttle into 2026, betting big on AI while keeping the ad engine roaring — and the world is watching.
Applications
Spotify paid out over $11bn to music industry in 2025; eyes artist-first push in 2026
SWEDEN: Spotify paid out more than $11bn to the global music industry in 2025, cementing its position as the single largest annual payer to music creators in history and setting the stage for a renewed push to help artists break through in an increasingly crowded market.
“I’m proud to share that, last year alone, Spotify paid out more than $11bn to the music industry,” said Charlie Hellman, head of music at Spotify, in a note published on the Spotify for Artists blog. The figure marks a year-on-year increase of over 10% from 2024, significantly outpacing growth across other industry income streams.
Independent artists and labels accounted for half of all royalties paid out during the year, reinforcing the platform’s growing role as a revenue engine beyond major labels.
“Big, industry-wide numbers can feel abstract,” Hellman said, “but that growth is showing up in tangible ways.” He pointed to a structural shift in music economics, noting that there are now more artists earning over $100,000 a year from Spotify alone than were ever stocked on record-store shelves at the height of the CD era.
Despite what Hellman described as “rampant misinformation about how streaming is working today”, Spotify now contributes roughly 30 per cent of recorded music revenue worldwide. In 2025, Spotify’s payouts grew by more than 10%, while other industry income sources expanded by closer to 4%, making the platform the primary driver of industry revenue growth.
That growth, Hellman said, is ultimately fan-led. More than 750 million people globally now pay for music streaming across all platforms each month. As audiences expanded, Spotify also raised subscription prices. With nearly two-thirds—almost 70%—of its revenue paid back to rightsholders, rising platform revenues translated directly into higher payouts for artists.
“The other third is our fuel,” Hellman said, referring to Spotify’s retained revenue. That capital is reinvested into product innovation designed to convert more listeners into paying subscribers and deepen fan engagement.
The challenge, however, is visibility. With more than 100,000 new songs released every day, emerging artists are competing not only with each other but with the entire recorded history of music. Spotify’s priority for 2026, Hellman said, is helping new artists “cut through the noise and form real connections with fans”.
A key pillar of that strategy is artist storytelling. As artificial intelligence floods the internet with content, Spotify is betting that human context will become more valuable, not less. The platform is expanding features that explain who artists are, what inspires them, and how songs come together.
An upcoming feature, SongDNA, will allow fans to explore the creative networks behind tracks—such as Addison Rae’s collaboration with Luka Kloser and Elvira Anderfjärd—and trace those links into wider catalogues, including Kloser’s work with Ed Sheeran and Anderfjärd’s with Alec Benjamin.
Video is another focus area, with Spotify leaning into authenticity over polish. Live takes, rehearsals and behind-the-scenes studio moments are being positioned as fan-building tools. For pop group Katseye, early backstage Clips on their Countdown Page helped drive momentum ahead of the release of Beautiful Chaos.
Trust and identity protection form the second pillar. Spotify is preparing new systems for artist verification, song credits and identity protection to counter impersonation, scams and low-quality AI-generated content designed to siphon royalties.
“AI is being exploited by bad actors,” Hellman said, adding that protecting authentic creativity is critical to maintaining trust among listeners and rightsholders.
Human editorial curation remains central to Spotify’s discovery engine. While algorithms personalise listening, editorial playlists offer cultural signals that can change careers. Leon Thomas, for example, landed on playlists such as RADAR and RNB X after pitching through Spotify for Artists, reaching listeners in more than 180 countries.
In 2026, Spotify plans to introduce new editorial programmes aimed at sustaining momentum for emerging artists, alongside greater visibility for the editors themselves through video and storytelling.
Live music is the final frontier. Spotify has already helped generate more than $1bn in ticket sales through its partners by matching artists with their most engaged fans. New tools launching in 2026 are designed to convert streams into sold-out rooms.
“You’ve built communities, taken risks, and kept going even when the path felt uncertain,” Hellman said. “It’s our job to make sure Spotify works as hard as you do.”
With unprecedented competition colliding with unprecedented opportunity, Spotify is placing a clear bet: scale alone is not enough. The next phase of streaming, it argues, will be won by those who help artists turn attention into careers.
And in 2026, Spotify wants to be the loudest ally in the room.
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