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        Matt Aslett's Analyst Perspectives

        Enterprises are embracing the potential for artificial intelligence (AI) to deliver improvements in productivity and efficiency. As they move from initial pilots and trial projects to deployment into production at scale, many are realizing the importance of agile and responsive data processes, as well as tools and platforms that facilitate data management, with the goal of improving trust in the data used to fuel analytics and AI. This has led to increased attention on the role of data...

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        Topics: data operations, AI & Machine Learning, Analytics & Data

        The emergence of generative artificial intelligence (GenAI) has significant implications at all levels of the technology stack, not least analytics and data products, which serve to support the development, training and deployment of GenAI models, and also stand to benefit from the advances in automation enabled by GenAI. The intersection of analytics and data and GenAI was a significant focus of the recent Google Cloud Next ’24 event. My colleague David Menninger has already outlined the key...

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        Topics: Analytics, natural language processing, AI & Machine Learning, data platforms, Analytics & Data, Generative AI

        I recently wrote about the development, testing and deployment of data pipelines as a fundamental accelerator of data-driven strategies as well as the importance of data orchestration to accelerate analytics and artificial intelligence. As I explained in the recent Data Observability Buyers Guide, data observability software is also a critical aspect of data-driven decision-making. Data observability addresses one of the most significant impediments to generating value from data by providing an...

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        Topics: Analytics, Data Ops, data operations, AI & Machine Learning, Analytics & Data, Generative AI, Machine Learning Operations

        I previously wrote about the potential for rapid adoption of the data lakehouse concept as enterprises combined the benefits of data lakes based on low-cost cloud object storage with the structured data processing functionality normally associated with data warehousing. By layering support for table formats, metadata management and transactional updates and deletes as well as query engine and data orchestration functionality on top of low-cost storage of both structured and unstructured data,...

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        Topics: Analytics, data platforms, Analytics & Data

        Many organizations have adopted DataOps to apply agile development, DevOps and lean manufacturing processes to the development, testing, deployment and orchestration of data integration and processing pipelines. The most likely ultimate outcome of these pipelines is the analytics reports and dashboards enterprises rely on to make business decisions.

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        Topics: Analytics, Analytics & Data, Data Intelligence

        Analytics software is used by business analysts and decision-makers to facilitate the generation of insights from data. It encompasses business intelligence and decision intelligence software, including reports and dashboards as well as embedded analytics and the development of intelligent applications infused with the results of analytic processes. Analytics software enables enterprises to improve business outcomes by operating more efficiently, accelerating product development and enhancing...

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        Topics: Analytics, AI, Analytics & Data, Generative AI

        I recently wrote about the development, testing and deployment of data pipelines as a fundamental accelerator of data-driven strategies. As I explained in the 2023 Data Orchestration Buyers Guide, today’s analytics environments require agile data pipelines that can traverse multiple data-processing locations and evolve with business needs.

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        Topics: Analytics, data operations, AI & Machine Learning, data platforms, Analytics & Data, Generative AI, Data Intelligence

        I previously explained how master data management helps provide trust in data, making it one of the most significant aspects of an enterprise’s strategic approach to data management. More recently, I discussed how it has a role to play in accelerating data democratization as part of data intelligence initiatives. Along with data quality, MDM enables organizations to ensure data is accurate, complete and consistent to fulfill operational business objectives. While it is an established and mature...

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        Topics: Product Information Management, Operations & Supply Chain, Analytics & Data, Sustainability Management, Data Intelligence

        I wrote recently about the role that data intelligence has in enabling enterprises to facilitate data democratization and the delivery of data as a product. Data intelligence provides a holistic view of how, when, and why data is produced and consumed across an enterprise, and by whom. This information can be used by data teams toensure business users and data analysts are provided with self-service access to data that is pertinent to their roles and requirements. Delivering data as a product...

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        Topics: Analytics, Data Ops, data operations, AI & Machine Learning, data platforms, Analytics & Data, GenAI, Data Intelligence

        The development, testing and deployment of data pipelines is a fundamental accelerator of data-driven strategies, enabling enterprises to extract data from the operational applications and data platforms designed to run the business and load, integrate and transform it into the analytic data platforms and tools used to analyze the business. As I explained in our recent Data Pipelines Buyers Guide, data pipelines are essential to generating intelligence from data. Healthy data pipelines are...

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        Topics: Analytics, AI, data operations, AI & Machine Learning, data platforms, Analytics & Data, Data Intelligence
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