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For our 6th annual Insight Jam LIVE!: Strategies for AI Impact<\/a>. Solutions Review editors sourced this resource guide of data analytics predictions for 2025 from Insight Jam<\/a>, its community of enterprise tech and AI builders, implementors, and experts. Join Insight Jam free for exclusive expert insights<\/a> and much more.<\/strong><\/em><\/p>\n As we look ahead to 2025, data analytics continues to stand at the forefront of enterprise transformation, driving innovation, efficiency, and strategic decision-making. With the convergence of artificial intelligence, machine learning, and advanced analytics, the landscape is evolving faster than ever before. Staying competitive means understanding not just where the industry is today, but where it\u2019s headed next.<\/p>\n This curation highlights expert predictions from our dynamic enterprise tech and AI community\u2014builders, implementors, and visionaries who are redefining what\u2019s possible with data analytics. These professionals bring unique perspectives on the trends, technologies, and challenges that will shape the way organizations unlock value from their data in the coming year.<\/p>\n From the rise of real-time analytics to the ethical implications of AI-driven insights, these forward-looking perspectives offer actionable guidance for staying ahead in 2025. Explore what\u2019s next in data analytics through the lens of those who are building and implementing the tools and strategies that are transforming enterprises worldwide.<\/p>\n Low Code Tools Will Streamline Compliance<\/strong><\/em><\/p>\n Industries like healthcare and finance, where compliance with strict regulatory standards is critical, often face extended development timelines due to the rigorous testing required. However, the growing adoption of low code tools is poised to revolutionize the time needed to adhere to these standards. Low code platforms not only accelerate app development but also ensure that applications are built in alignment with legal and regulatory requirements. By integrating industry best practices into the development process, low code solutions will streamline compliance, enabling faster delivery of secure, compliant apps without sacrificing quality or oversight.<\/p>\n Training and data synthesis will help break through the scaling problem<\/strong><\/em><\/p>\n<\/div>\n We need new ideas to move forward on the path of AI scaling laws. I see three main ways to do this: One is to improve model architectures, although I don’t expect major breakthroughs here. This has been tried a lot, and while I expect more progress in the new year, there is still a lot of steam in transformer-like architectures. Another solution is to improve optimization. Clearly, there is a lot of room to make AI training more energy and data efficient. The current approach is still very basic and consumes a lot of energy. An interesting analogy is the human brain, which consumes about 20 watts of power.<\/p>\n By the age of 20, this adds up to a total energy consumption of 3.5 MWh (3.5 megawatt hours). This is over 17,000 times less power consumption than training some of the most popular AI models out there! Better optimization algorithms can unlock huge efficiency gains, which is an under-explored area of research. This area is critical and will continue to be through 2025, although breakthroughs may come later.In the short run, creating more data seems like the most promising approach to further push AI scaling laws. While naive approaches to use synthetic data can hurt AI quality, with careful execution, cleverly leveraging this wealth of feedback can boost AI model performance in a wide range of tasks.<\/p>\n AI will transform real-world IT management<\/strong><\/em><\/p>\n<\/div>\n In 2025, artificial intelligence (AI) and machine learning (ML)-based capabilities will further transform the IT support function, and lead to tangible benefits for real-world IT management at production-scale. In other words, we have all been hearing about the promise of AI, but 2025 will mark AI technologies graduating from the lab and POC environment to solve real-world problems. As IT professionals increasingly leverage AI-driven, automation to handle routine tasks, tools and technology platforms will become smarter and more advanced, emulating human expertise in basic to intermediate support and management functions.<\/p>\n For instance, Level 1 IT support and helpdesk roles will be increasingly augmented by AI agents and capabilities, while human workers can focus their time on more complex and value-added activities. Additionally, we can expect to see manual runbook execution and knowledge search replaced by automation and autonomous responses. This will allow for increased automated workflow capabilities with response and remediation actions generated by LLMs.<\/p>\n Smarter Inventory Optimization with Data<\/strong><\/em><\/p>\n<\/div>\n Predictive analytics in inventory management has become essential for minimizing waste and ensuring product availability. With data-driven insights, retailers can forecast demand accurately, preventing overstock and stock outs, which are costly mistakes in retail. According to the National Retail Federation, stock outs and overstocks cost retailers around $1.75 trillion globally. However, with the potential of predictive analytics to reduce these losses by 10-15 percent, it’s clear that data-driven inventory management is a game-changer for retailers looking to improve operational efficiency.<\/p>\n For example, a heavy machinery client can predict when each customer will need to replace a machine or perform maintenance, allowing them to proactively sell used equipment, identify buyers, and close deals. Additionally, they can incentivize maintenance and parts purchases at the right time while predicting upsells and revenue opportunities, further maximizing their business impact.<\/p>\n As we approach 2025, minimizing waste and ensuring product availability is critical for today\u2019s retailers to stay competitive. Data-driven insights empower businesses to anticipate customer needs, prevent costly mistakes like stock outs or overstocks, and create a smoother, more personalized shopping experience. This approach improves efficiency, strengthens customer trust, and opens up growth opportunities.<\/p>\n 2025 will mark the shift from \u201cbig data\u201d to \u201csmall data\u201d as organizations focus on quality over quantity<\/strong><\/em><\/p>\n The past few years have seen a rise in data volumes, but 2025 will bring the focus from \u201cbig data\u201d to \u201csmall data.\u201d We\u2019re already seeing this mindset shift with large language models giving way to small language models. Organizations are realizing they don\u2019t need to bring all their data to solve a problem or complete an initiative \u2013 they need to bring the right data. The overwhelming abundance of data, often referred to as the \u201cdata swamp,\u201d has made it harder to extract meaningful insights. By focusing on more targeted, higher-quality data\u2013 or the \u201cdata pond\u201d\u2013 organizations can ensure data trust and precision. This shift towards smaller, more relevant data will help speed up analysis timelines, get more people using data, and drive greater ROI from data investments.<\/p>\n AI investments will shift from cost-cutting to driving real customer impact<\/strong><\/em><\/p>\n The last two years have largely been about \u201cdoing more with less,\u201d with companies focusing on cost reduction, simplification, and technology rationalizations. But in 2025, the focus will shift toward outcomes and re-accelerating growth. After exploring the capabilities of new technologies, especially AI, businesses are now looking to make investments that actually drive value. It\u2019s no longer just about using AI for the sake of technology\u2013 it\u2019s about using it to deliver what customers want, how and when they want it. At its core, AI is just software. While it can be incredibly powerful, it\u2019s only valuable when it solves real customer problems. More organizations are recognizing this shift and focusing on the right investments that deliver tangible impact.<\/p>\n AI-powered predictive analytics will evolve, driving timely decision making for businesses<\/strong><\/em><\/p>\n<\/div>\n Right now, AI’s capabilities in predictive analytics are still mediocre, with machine learning falling short of delivering the deep insights businesses need. While AI today mainly identifies trends, significant advancements will begin to emerge in 2025 and beyond. Over the coming years, AI will continue to evolve to provide more accurate, preemptive decision-making support, empowering organizations to act on business practices proactively and in real time, rather than giving counsel based on older context.<\/p>\n Proprietary data will become an AI differentiator in 2025<\/em><\/strong><\/p>\n<\/div>\n Generalized AI models offered a competitive advantage for those who were the first to adopt them, but implementing the tech has become a prerequisite for competing in today\u2019s marketplace. In other words, AI is no longer a differentiator, but the way that it\u2019s used certainly is. Companies need to keep their \u2018value wedge\u2019 (or their differences from the wider industry in the ways they do business) central to their AI strategies.<\/p>\n Training models on proprietary historical data attunes them to a specific organization\u2019s nuances, yielding hyper-focused outputs and predictive analytics that are far more likely to serve business goals than blanket advice. If data is king, context is its crown, and there\u2019s no better way to validate AI outputs than keeping its training environment airtight and focused entirely on your company.<\/p>\n The Demise of Traditional BI: API-First and GenAI Integrate Analytics into Every App<\/strong><\/em><\/p>\n<\/div>\n In 2025, traditional BI tools will become obsolete, as API-first architectures and GenAI seamlessly embed real-time analytics into every application. Data insights will flow directly into CRMs, productivity platforms, and customer tools, empowering employees at all levels to make data-driven decisions instantly\u2014no technical expertise needed. Companies that embrace this shift will unlock unprecedented productivity and customer experiences, leaving static dashboards and siloed systems in the dust.<\/p>\n Data Literacy Becomes a Mass Movement\u2014Empowered by Composable Apps<\/strong><\/em><\/p>\n<\/div>\n In 2025, a mass data literacy movement will take hold, driven by composable apps that seamlessly integrate real-time analytics into everyday experiences. Consumers will actively engage with data on energy usage, shopping habits, and sustainability through intuitive, user-friendly platforms. Companies that simplify data reporting and empower users will thrive, while those relying on opaque, complex reports will face a consumer backlash demanding transparency.<\/p>\n The Semantic Layer Becomes the Enabler for LLMs in Enterprises<\/strong><\/em><\/p>\n<\/div>\n In 2025, the Semantic Layer will become the crucial enabler for LLMs in enterprises, acting as a bridge between internal data and LLMs to deliver precise, contextually relevant insights. By unifying enterprise data with global knowledge, this integration will revolutionize decision-making and productivity, making GenAI indispensable. Companies that embrace this convergence will dominate in innovation and customer experience, leaving competitors behind.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n The Future of Business Intelligence<\/strong><\/em><\/p>\n<\/div>\n In 2025, business intelligence will evolve from traditional reporting and become a central force in strategic decision-making, enabling businesses to anticipate trends and respond to opportunities with greater agility. As organizations increasingly prioritize data-driven cultures, the demand for real-time, actionable insights will intensify, making BI an essential tool for gaining a competitive edge. AI-powered analytics will take BI to the next level, allowing for more accurate forecasting and automated decision-making.<\/p>\n Additionally, the expansion of self-service BI tools will empower non-technical users to explore and analyze data independently, while embedded analytics will integrate these insights directly into operational systems, ensuring data-driven decisions are seamlessly woven into daily workflows. This shift will not only enhance productivity but also enable organizations to uncover deeper insights from their data, driving innovation and growth.<\/p>\n<\/div>\n<\/div>\n<\/div>\n Implementing AI Will be a Top Priority in 2025<\/strong><\/em><\/p>\n For our 6th annual Insight Jam LIVE!: Strategies for AI Impact. Solutions Review editors sourced this resource guide of data analytics predictions for 2025 from Insight Jam, its community of enterprise tech and AI builders, implementors, and experts. Join Insight Jam free for exclusive expert insights and much more. As we look ahead to 2025, […]<\/p>\n","protected":false},"author":23,"featured_media":10027,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[4],"tags":[],"acf":[],"yoast_head":"\nData Analytics Predictions from Experts for 2025<\/strong><\/h2>\n
\nJJ McGuigan, Infragistics<\/strong><\/h4>\n
Stefan Meskan, DeepL<\/strong><\/h4>\n
\n\n\n\nStefan Meskan, Dufrain<\/strong><\/h4>\n
\n\n\n\n\nCharles Guimont, O2 Commerce<\/strong><\/h4>\n
\n\n\n\n\n\nFrancois Ajenstat, Amplitude<\/strong><\/h4>\n
\n\n\n\nJ-M Erlendson, Software AG<\/strong><\/h4>\n
\n\n\n\n\n\n\n\n\n\nAriel Katz, Sisense<\/strong><\/h4>\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nCasey Ciniello, Infragistics<\/strong><\/h4>\n
\n\n\n\n\n\n\n\n\n\n\n\nBy 2025, generative AI will become more integrated into technology, including content creation, software development, and automated decision-making. The shift towards AI will be a top priority and present transformative challenges in 2025, including workforce concerns about job security and resistance among employees hesitant to embrace AI-driven interactions. Traditional mentoring and learning pathways could be disrupted, resulting in limited development op ortunities for junior staff and leaving a critical gap in skill-building and career growth.<\/div>\n<\/div>\nTo address these challenges, we must adopt a proactive approach for collaboration between human employees and AI tools, emphasizing the unique skills that humans bring to the table, such as creativity, critical thinking, and emotional intelligence. By fostering an environment where employees view AI as a partner rather than a replacement, organizations can alleviate fears and enhance morale.<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"