Articles about Data

Data security has become a top concern for businesses across all industries. As organizations accumulate and leverage vast amounts of data to drive decision-making, the need to safeguard that information from both internal and external threats is more important than ever.

For companies managing sensitive customer information, intellectual property, or proprietary business insights, data security is no longer a negotiable priority -- it’s a critical component in strengthening your overall security strategy.

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With a rapidly developing threat landscape, an increase in high-profile data breaches, the introduction of new legislation, and customer tolerance for poor data handling at an all-time low, the stakes are high for companies to have robust cybersecurity in place. However, despite their best efforts, companies are often found to not be doing enough to protect their assets.

Often, this is due to a case of ‘too much, too fast’. As businesses invest in new technologies, their day-to-day operations are being supported by ever more complex and fragmented technology platforms. At the same time, the amount of customer data available to them is growing and constantly streaming in, and bad actors are consistently launching more sophisticated attacks. Meanwhile, leaders are not fully aware of or own responsibility for their cybersecurity plans. As the digital world evolves with new threats and regulations, business leaders must recognize the importance of data protection. If they do not, they cannot adequately protect their customer's data and are in danger of losing their trust and even their continued existence in business.

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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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Data Science Handbook offers a hands-on experience on various algorithms and popular techniques used in real-time in data science to all researchers working in various domains.

Data Science is one of the leading research-driven areas in the modern era. It is having a critical role in healthcare, engineering, education, mechatronics, and medical robotics. Building models and working with data is not value-neutral. We choose the problems with which we work, make assumptions in these models, and decide on metrics and algorithms for the problems. The data scientist identifies the problem which can be solved with data and expert tools of modeling and coding.

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AI growth and adoption in the UK are surging, with the market valued at more than £16.8 billion and expected to reach £801.6 billion in the next decade. Approximately 15 percent of UK businesses are already using AI technologies such as data management and analysis, natural language processing, machine learning, and computer vision. And across the pond in the US, AI is expected to contribute a significant 21 percent net increase to US GDP by 2030, showcasing its substantial impact on the economy.

Growth in any new technology is never without its challenges. For AI, these include ensuring data privacy, addressing ethical concerns, and navigating the complexity of integrating with existing IT infrastructure. Data quality is central to resolving these challenges. To be useful, the data used for AI must be high-quality, well-structured, and from trusted sources. These properties are the foundation for all AI models and determine their effectiveness and reliability.

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Often described as the lifeblood of an organization, data drives business operations and decision-making. But while the raw data itself is valuable, it’s the intelligence and insights that can be gleaned from it that truly fuel innovation and growth. This vital intelligence is the foundation on which organizations build long-term strategies, optimise processes, and identify new opportunities.  

However, with IoT and AI creating volumes of data at an unprecedented rate, it has come to a point where many large enterprises have data lakes and warehouses overflowing with untapped potential. 

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Fresh out of college and with just a year of self-study behind him, Cory Althoff was offered a dream first job as a software engineer for a well-known tech company, but he quickly found himself overwhelmed by the amount of things he needed to know, but hadn’t learned yet. This experience combined with his personal journey learning to program inspired his widely praised guide, The Self-Taught Programmer.

Now Cory's back with another guide for the self-taught community of learners focusing on the foundations of computer science.

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AI will generate 10 percent of all new data in 2025, according to Gartner. This statistic has significant ramifications for business leaders in the digital age.

First, it hints at another substantial development: Overall data generation will skyrocket alongside advanced AI and machine learning (ML) tools. Statista predicts that humans will create, process and consume 180 zettabytes of data in 2025, up nearly 300 percent since 2020. This prediction foreshadows worsening data sprawl, a problem wherein organizations have more data than they can process or understand.

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Businesses are constantly seeking ways to enhance their profit margins, and smart promotional tactics can play a crucial role in achieving this goal. Promotions are not merely about attracting attention; they are strategic tools that, when executed effectively, can drive sales, increase customer loyalty, and strengthen brand presence.

By understanding key elements such as data utilization, compelling offers, and strategic timing, businesses can optimize their promotional efforts. This article explores essential considerations for leveraging data, crafting attractive incentives, and timing promotions to maximize impact. Through thoughtful planning and execution, businesses can turn promotional activities into powerful drivers of profit and growth.

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Although governments are issuing new guidelines for businesses to toughen up their cyber protection, cyberattacks remain a major risk, only growing in sophistication with advancements in AI.

With the continued integration of AI into systems, recognizing the threat that dataset poisoning presents is also an emerging concern. We spoke to Andy Swift, cyber security assurance technical director at Six Degrees to discuss the latest threats and how businesses can respond.

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Maintaining a secure cloud environment is one of the most important responsibilities of any CISO today, given that over 50 percent of all cyberattacks now originate in the cloud. However, this is a daunting task, as security must now be balanced against other priorities such as maintaining agile operations and the need to innovate.

Organizations today are racing to accelerate their cloud adoption due to the need for greater scalability and cost-efficiency. It has, therefore, become a critical business strategy to ensure efficiency, accessibility, and sustainability in operations. As a result, cloud investments are soaring across the board. Gartner predicts that end-user spending on public cloud services will reach $679 billion by the end of this year and exceed $1 trillion by 2027.

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The data flood gates have opened wide for clinical trial research. In fact, the amount of data gathered may be more akin to a tsunami or a monsoon. For decades, researchers struggled with a lack of data available in clinical trials; however, they may have received more than they asked for. Research shows that the biopharmaceutical industry generates up to a trillion gigabytes of data annually and clinical trials, one of the principal contributors to these data points, generate an average of up to 3 million data points per trial. This influx of sources can make it challenging to discern relevant from superfluous information, complicating analysis and delaying critical decision-making.

An increase in decentralization paired with expanded collection methods in clinical trials have increased access to and accumulation of data. Information gathered from remote monitoring devices, electronic health records (EHRs), laboratory tests, surveys and questionnaires and third-party databases, all contribute to the data challenge in clinical trials. In reality, the number of touchpoints across clinical trials, from sponsors to clinical research organizations (CROs) to site staff, combined with the complexity and disparity of data sources leads to challenges in ensuring data quality.

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In the healthcare industry, surgical capacity management is one of the biggest issues organizations face. Hospitals and surgery centers must be efficient in handling their resources. The margins are too small for waste, and there are too many patients in need of care. Data, particularly real-time data, is an essential asset. But it is only useful if the pieces fit together, solving a puzzle of coordinating schedules, operating room availability, and resource allocation, while ensuring immediate access to patient data for perioperative teams.

Data management demands are significant, complex, and dynamic. Because each patient is unique, anything can happen in an operating room (OR) at any moment. As such, real-time data capture is crucial for surgical workflows. When surgical teams have all the information they need in real time, they can make rapid decisions that not only maximize OR utilization and minimize delays but also enhance overall patient care and safety.

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Businesses around the globe are using new technologies to change the world. But this wouldn’t be possible without the use of sensitive data such as Personal Identifiable Information (PII) and Protected Health Information (PHI) to drive advancements in personalization and sophistication. However, if companies are using data that typically is associated with medical records and insurance claims, this bodes the question, is personal data secure?

It is possible to balance data privacy with gleaning the value of the information through a data modernization strategy that enhances and accelerates digital transformation efforts.

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Synthetic data is artificial material that was not generated by natural life events. As such, it can be created by computer programs and AI tools that use different techniques, with generative adversarial networks and diffusion models being among the most popular and effective today. Synthetic data may come in many forms, but images and textual information are currently the most feasible options.

If you are interested in AI and ML developments, you have probably heard the term already -- “sanitized” synthetic data is a recent hype in the AI training field that, it is believed, might solve pressing data privacy and ownership challenges posed by real data. However, it all sounds like sunshine and rainbows only until you stop and consider the fact that AI algorithms used to generate synthetic data still need to be trained on real data -- the very obstacle they offer to remove.

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