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Tag: Data Management

  • Synology-Toshiba shake hands, put all their eggs in strategic basket

    Synology-Toshiba shake hands, put all their eggs in strategic basket

    MUMBAI: Ever heard of putting your data safely in a basket? Synology and Toshiba just agreed it’s safer to put their eggs—or rather, bytes—in one very secure basket. Think Batman and Robin, but for enterprise storage solutions. Yes, this duo is here to prove two heads (and a few terabytes) are indeed better than one.

    On 19 March 2025, Synology and Toshiba Electronic Components Taiwan Corporation formalised their long-standing collaboration by signing a memorandum of understanding (MoU). The agreement outlines plans for deepened cooperation, structured intellectual property management, and further market expansion.

    Why the big fuss? Because Synology and Toshiba have already been quietly revolutionising enterprise storage, enhancing system stability, and performance through shared tech initiatives. This MoU isn’t just paperwork—it’s a roadmap to making enterprise data storage cooler (and way more efficient).

    Toshiba’s storage products sales & marketing division general manager, Atsushi Toyama said, “Synology is one of Toshiba’s most significant and long-standing partners in the Asia-Pacific region. This collaboration enables us to leverage our combined expertise to create greater value for our customers.”

    Synology chairman & CEO Philip Wong added, “Toshiba has been a key strategic partner of Synology for years. We look forward to deepening our collaboration and delivering even more advanced storage and data management solutions that exceed our customers’ expectations.”

    So, enterprise customers, rest easy—your storage superheroes just teamed up officially. Batman and Robin? Old news. Synology and Toshiba? Now that’s a dynamic duo worth storing away.

  • How AI-enabled tools simplify data management

    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.

  • HOUM Technology signs Concept PR as its communication partner

    HOUM Technology signs Concept PR as its communication partner

    New Delhi: Tech start-up HOUM Technology has handed over its communication mandate to Concept PR. The PR mandate for Houm Technology includes strategic communications and crisis management.

    Houm is a category-creating global consumer internet product at the convergence of web3.0, private communication and data monetization. It lets each consumer build and own a private place (digital home) on the internet—with a personal domain—that no other person, system or bot (including Houm Technology) can access; enabling the creation of a completely decentralised ecosystem.

    Speaking about the collaboration, HOUM Technology CEO said, “Concept PR was the only agency who could show what Houm should do from a marketing perspective. Their Technology & Startups team impressed us with their belief in our Web 3.0 Product and the understanding of the tech industry at large. I look forward to this partnership and the journey ahead.”

  • Brands to rebalance media spends between online and offline: Kantar

    Brands to rebalance media spends between online and offline: Kantar

    MUMBAI: Many online-first and disruptor brands will be investing in offline modes of advertising, as they seek to “redress the balance between short-term performance marketing and long-term brand building,” reveals the latest Media Predictions 2020 report, released by Kantar. Not just this, multimedia advertisers will make efforts to better integrate their marketing efforts with offline experiences.

    In the report, Kantar expert Duncan Southgate writes, “This rebalancing will take many forms across the marketing landscape, so let’s consider a few different perspectives. For online players this will mean more attempts to bring their brands to life in the real world and build closer connections with consumers.”

    Southgate shares the examples of Mozilla, owner of Firefox browser, which had recently reduced digital marketing spend by 10 per cent and shifted more dollars to offline marketing efforts including events and content marketing.

    “Brands like eBay are rebalancing their media spend in favour of brand-building traditional channels. This makes sense, as we know from our CrossMedia database that digital media spend is far less likely to be cost-effective for brands where digital exceeds over half of the budget.”

    With the trend to getting back to reality taking shape in 2020, it is expected that a slowdown in the global digital advertising growth. However, with 31 percent of the marketers struggling to integrate their media and non-media touchpoints, it will be important for them to break down down the silos to achieve the desired growth.

    Other important trends highlighted in the report included an upsurge in ‘voice search’ thus heralding a new age of audio advertising.

    “We expect podcasts to be one of the fastest growing channels for ad spend: according to our Getting Media Right 2019 study, 63% of marketers say they plan to increase spend in podcast advertising over the next 12 months,” writes Kantar North America’s Heather O’Shea.

    Another trend shaping up in the year, with many browsers disabling cookies that were a prime source of data collection for digital advertisers, and countries taking stand for data privacy, would be an influx of direct integrations between publishers and measurement partners, which will enable true cross-publisher measurement for the first time.

    “What is certain is that campaign measurement will become ever more complex. Marketers will need to future-proof their measurement frameworks and reduce their reliance on cookies for tracking. And many will turn to third-party measurement providers like Kantar to help them navigate the evolving media landscape,” shares Jane Ostler.

    Campaign measurements will surely get tougher during the year, therefore prompting the marketers to seek for new and innovative ways to keep a tab of their ROI.