12 Big data analytics trends to watch for 2024<\/h2>\n
Companies able to harness the potential of data, distinguish the most promising solutions among all the big data industry trends, and adopt the hottest technologies in this domain will enjoy countless advantages and take the lead in the competitive market.<\/p>\n
1. Infonomics<\/h3>\n
According to Doug Laney, former VP of Gartner, infonomics, one of the latest trends in big data, is \u201cthe theory, study and discipline of asserting economic significance to information. It strives to apply both economic and asset management principles and practices to the valuation, handling and deployment of information assets.\u201d<\/p>\n
In simpler terms, infonomics treats data as a commodity-like substance. After all, if data can substantially improve forecasting results and therefore boost sales or minimize losses; if it can help target the right consumer cohorts with the right products; and if it can improve public safety \u2014 why shouldn\u2019t it be treated as a valuable resource, just like rare metals or fossil fuels?<\/p>\n
Infonomics model: measure, manage, and monetize information<\/p>\n
In the future, data will be gaining more and more market traction as an object of trade and exchange, and the fuel powering the rapidly growing industries of data science and ML engineering. Even today, big data is something that many global businesses simply won\u2019t survive without, which means that business leaders should be treating their big data strategies with all seriousness.<\/p>\n Some examples of data being sold as a product can be drawn from world-renowned sources of business intelligence, such as NielsenIQ<\/a>, Acxiom<\/a>, and, more recently, Dawex<\/a>, an innovative global data exchange marketplace. See how Intellias helped a Fortune 500 retail chain implement a robust big data solution for data visualization and anomaly detection.<\/p>\n In a world getting increasingly dependent on data and data-driven decisions, trends in big data analytics and the overall success of big data initiatives will be governed by DataOps<\/a>, an emerging operational framework and a set of best practices in the big data space.<\/p>\n The DataOps cyclic process<\/b><\/p>\n Those who say that DataOps is essentially DevOps for data are right in that DataOps can do as much good for data science as DevOps has done for development. However, it\u2019s a much wider notion, despite the apparent semantic similarity. IBM, for instance, defines<\/a> DataOps, as \u201cthe orchestration of people, process, and technology to deliver trusted, high-quality data to data citizens fast\u201d.<\/p>\n Similarly to DevOps, which does not consist of continuous integration and continuous delivery only, DataOps as one of the key big data analytics trends is more of a philosophy than a set of delivery approaches. This fusion of architectural approaches, cultural elements, agile practices, lean production techniques, statistical process control (SPC), and good old DevOps strives to achieve the following:<\/p>\n Global spending on IoT was estimated at $805.7 billion in 2023<\/a> and is expected to grow even more over the years. This technology, combined with AI and 5G, changes the way things work in the world. It promotes interconnectivity, ensuring various devices work smoothly within a single large network.<\/p>\n Some real-life applications of IoT include:<\/p>\n This is one of the big data trends that is already becoming part of our daily lives, so you can expect to see it everywhere, from manufacturing to marketing.<\/p>\n One of the latest trends in big data management is the usage of generative AI. Artificial intelligence is capable of automating data processing by 90%<\/a>, significantly reducing manual workload and allowing engineers to focus on more important matters.<\/p>\n As one of the top-tier big data analytics trends, it is used in the following ways:<\/p>\n It\u2019s also necessary to note the NLP capabilities in one of these big data trends. The rise of various GPT models ensures AI can generate comprehensive reports and summaries from raw data. It easily interprets queries and supports professionals in their work, so it\u2019s definitely a step into the future.<\/p>\n Predictive analytics is one of those trends in big data analytics that are frequently discussed and used by Google, IBM, and other tech giants. Type a search query and you\u2019ll get a search recommendation, buy a product online and you\u2019ll get suggestions on \u201cwhat comes best with it\u201d. It\u2019s already everywhere.<\/p>\n Source: Aberdeen<\/a><\/em><\/p>\n Some of the leading applications of predictive big data trends are:<\/p>\n Predictive big data trends support companies with deeper insights and better predictions. This helps them monetize all opportunities by making data-driven decisions.<\/p>\n One of the greatest challenges that adopters of big data technologies will face is how to deal with disparate and siloed data sources. And their attempts to solve this problem leads to the appearance of new big data industry trends.<\/p>\n Every large organization operates multiple systems scattered across departments, production facilities, branches, and geographies. Each system may potentially have a unique data storage format and a set of security requirements, thus creating a need for complex ETL manipulations.<\/p>\n The success of any digital transformation will depend heavily on the ability to centralize data processing and storage, create company-wide data pipelines, and implement universally accessible data analysis tools.<\/p>\n The key hurdles for resolving interconnectivity issues will include the following:<\/p>\n These operational challenges can only be solved by means of tight cooperation between a company\u2019s business and technology stakeholders, as well as an in-house or hired team of professional data engineers<\/a> and data analysts.<\/p>\n Chief Data Officers (CDOs) are among the latest trends in big data analytics. They help organizations use data as a valuable resource. In 2022, around 27% of companies<\/a> hired a CDO to support their strategies in big data engineering<\/a>.<\/p>\n Source: Dataversity<\/a><\/em><\/p>\n A chief data officer supports your company with:<\/p>\n CDOs generally help companies with immense tech expertise and proficiency with modern technologies, allowing them to maximize their data\u2019s value. Hiring a chief data officer is a must for data-driven organizations.<\/p>\n A data fabric is an architectural approach created to optimize data management and access across disparate systems and environments. This includes on-premises, cloud, and hybrid setups. It provides a centralized data management framework that integrates various data sources. Source: Spiceworks<\/a><\/em><\/p>\n Data fabrics are among the big data technology trends due to their benefits:<\/p>\n They are commonly used in the telecom sector. You can learn more about big data solutions for telecom<\/a> in our previous blog posts written by the engineers at Intellias.<\/p>\n A data mesh is a decentralized data architecture approach that treats data as a product, decentralizing the ownership and management of data to domain-specific teams. This contrasts with traditional centralized data management approaches. Source: Kellton<\/a><\/em><\/p>\n A data mesh is one of the future trends in big data analytics due to its:<\/p>\n Using a data mesh architecture is essential for telecom operators due to its principles. They allow operators to make informed decisions with accurate responses from analytics systems. Read more about big data in the telecom sector<\/a> in our previous articles.<\/p>\n Data governance is the process of making sure data is available, usable, and secure in an organization. It includes policies, processes, and standards that make sure data is used properly.<\/p>\n Here are some issues covered by this trend:<\/p>\n According to Harvard Business Review<\/a>, only 3% of data in companies meets basic quality standards. One of the steps to fix this issue and improve the quality is data governance. Its market size reached $3.27 billion in 2024<\/a>, so data governance has proven to be a popular and effective approach.<\/p>\n The notion of big data future trends is inseparable from quantum computing. As the amount of data generated by computer systems keeps growing exponentially, it will inevitably come into conflict with the limitations of today\u2019s hardware approaching its physical limits, as per Moore\u2019s law. Dramatic performance improvements will require a \u201cquantum leap\u201d in the raw processing power of future CPUs, and quantum computing will be the answer and another mighty solution out of all spectrum of latest trends in big data.<\/p>\n Quantum computing may still be in the making, but its future potential is not to be underestimated. Major players like IBM and Google, as well as a number of high-tech startups, have spotted its potential amongst other trends in big data analytics and are already making steady progress in this area. Once mature enough and commercialized, the technology will be put to good use by large enterprises and science labs around the world to tap into the vast array of data that remains untouched and unprocessed today.<\/p>\n As hardware manufacturers push the envelope to harness those cubits, software companies like Microsoft are laying a foundation for the future of big data science by developing corresponding frameworks and online platforms \u2014 check out Azure Quantum<\/a>, for example.<\/p>\n In the world of big data, cybersecurity<\/a> is an equally big deal, outlining another tendency within big data future trends. Data analysis systems can be deployed in or collect data from such areas as finance, healthcare, insurance, and many others \u2014\u202fall rife with confidential personal and business information. Compromising this data may have severe ramifications and pose major risks to affected individuals and companies.<\/p>\n At the same time, security measures cannot be implemented exclusively at the storage level. Big data systems have complex architectures and consist of multiple distributed components and data sources, which makes the enforcement of security policies a challenging, never-ending process.<\/p>\n Given the current technologies and big data analytics trends, the following potential security-related issues should be taken into account:<\/p>\n Companies that are just starting to follow trends in big data analytics and think of the adoption of big data technologies may be concerned about having little control over sensitive data that\u2019s stored and processed in public clouds using third-party tools. In this case, a multi-cloud strategy<\/a> can help maintain a healthy balance between security and operational efficiency.<\/p>\n According to Fortune Business Insights, the big data analytics market was valued at $307.51 billion in 2023<\/a> and is projected to reach $924.39 billion by 2032. This means the industry is booming and it\u2019s going to be a giant trend in the next decade. The revenue forecast is even bigger, anticipating $103 billion in 2027<\/a>.<\/p>\n Source: Statista<\/a><\/em><\/p>\n Let\u2019s check out some other statistics in the big data industry:<\/p>\n As you can see, big data is a core element in many activities. It\u2019s one of the greatest factors in increasing your company\u2019s income with data-driven decisions and solutions.<\/p>\n Our big data engineers have an extensive background in the industry from past years, so they have a clear vision of its future growth. Considering the current trends and use cases, here\u2019s what we can expect over the following years:<\/p>\n These are only some of the insights in the big data industry. Many other trends will emerge with the rapid development of AI and ML, bringing us a few steps closer to the future with each day.<\/p>\n Intellias has been in the market for more than 20 years. The company collaborates with AWS, Google, and Microsoft to deliver cutting-edge solutions using their products. You\u2019ll get end-to-end services that ensure your project is supported by a long-term partner who knows the industry inside out.<\/p>\n Here are our leading data engineering case studies:<\/p>\n Advanced Big Data Analytics Platform for a National Telecom Provider<\/a>.<\/strong><\/p>\n AWS Migration Services for Seamless Big Data Analytics in Telecom<\/a>.<\/strong><\/p>\n Location Big Data Analytics for Enhancing Business Intelligence<\/a>.<\/strong><\/p>\n Global services such as Google Search and Facebook rely on hundreds of internal services and components based on big data, AL\/ML, and deep learning \u2014 and most users don\u2019t know that data is the driving force behind the magic they love. Big data has made its way into business, and it continues to make strides, from personalized recommendations on smartphones to infrastructure management of smart cities.<\/p>\n Partnering with Intellias empowers your company to unlock the full potential of your data. Our team has the experience to create and implement robust big-data solutions that incorporate the latest trends. We guide you through defining your business objectives, selecting the optimal tech stack, and ensuring seamless implementation with 24\/7 support. Contact us<\/a> today to modernize your data strategy and strengthen your business with our advanced solutions and expertise.<\/p>\n![\"Top](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2020\/12\/big-data-trends_pic_2.jpg\")
\nSource: Gartner<\/a><\/em><\/p>\n
\nDataOps<\/h2>\n
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\nSource: Ryan Gross, Medium<\/a><\/em><\/p>\n\n
3. Internet of Things<\/h3>\n
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4. Generative AI in data management<\/h3>\n
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5. Predictive analytics<\/h3>\n
![\"Predictive](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2020\/12\/Big-Data-Future-Trends-Infographic-1.jpg\")
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6. Interconnectivity<\/h3>\n
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7. Chief Data Officers<\/h3>\n
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8. Data fabric<\/h3>\n
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9. Data mesh<\/h3>\n
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10. Data governance<\/h3>\n
![\"Data](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2020\/12\/Big-Data-Future-Trends-Infographic-7.jpg\")
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11. Quantum computing<\/h3>\n
12. Data security<\/h3>\n
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\nSource: Shaveta Jain, Researchgate<\/a><\/em><\/p>\nBig data market forecasts<\/h2>\n
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Future outlook and predictions<\/h2>\n
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The Intellias experience<\/h2>\n
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Conclusion<\/h2>\n