Data

2023 – The year to deploy Machine Learning and AI in your data center?

2023 – The year to deploy Machine Learning and AI in your data center? Can you use Machine Learning and AI to balance the impossible – increased data center workloads v energy reduction / ESG targets? Today’s data centers face a challenge that, initially, looks like it’s almost impossible to resolve. Operations have never been busier, and analyst projections suggest workload levels are only going to increase over the next five years – with some projecting compound annual growth rates of as much as 21% through 2025. At the same time, critical facilities such as data centers are coming under increased pressure to reduce energy consumption – particularly as the reality of corporate ESG programmes and net zero commitments start to bite. Download this White Paper to explain the options

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Value of System of System approach to drive real-time, intelligent operations

Data, Information Technology, Uncategorized /

Value of System of System approach to drive real-time, intelligent operations Historically, businesses have used data warehousing to reveal patterns, improve procurement and delivery, and pinpoint areas for improvement. Such methods are very reactive in nature and the answers come slowly, only after pouring over reams of historical data. Operational decision making requires real-time information that cuts across silos to answer questions. To thrive, industrial businesses and municipal operations need to implement digital technologies that unify operational data and provide holistic and real-time access to the information needed to drive efficiency and agility. Read this blog to learn more about AVEVA’s System of Systems approach to drive real-time, intelligent operations.

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Plan to Win

Data, Information Technology /

Plan to Win Business agility is now a determining factor for success in today’s data-driven, fast-paced world. To think fast and move first, companies must embrace a new model or risk being left behind. Plan to Win reflects the collected experience and wisdom gained from working with thousands of companies, the insights of CFOs and FP&A practitioners, and our partners and other thought leaders, as well as research on how planning impacts business performance. Available in both print and digital formats, the book sets forth a clear vision for where strategic business planning is headed and the impact it can have on organizations. The practices detailed here are a guide for businesses to chart their futures in this ever-changing, digital world. Our special thanks to the customers and partners of Adaptive Insights, who every day prove themselves to be the most influential and steadfast change agents of all.

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7 Data-Driven Ways to Attract and Retain Subscribers

Data, Information Technology /

7 Data-Driven Ways to Attract and Retain Subscribers There’s no dearth of audience data for publishers to use. The worldwide total of data created in 2018 was 33 zettabytes – that’s 33 trillion gigabytes – and Statista reports that this is nothing compared to the forecasts for the next decade or so. 1 Publishers and media companies won’t be able to (or need to) access and use the many zettabytes of data created each year, but the amount of data they do have access to is incredible. Subscriber demographics – plus information on who opens, clicks on and purchases what – can be leveraged to boost audience engagement.

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5 Steps To Successful Data Governance

Data, Information Technology /

5 Steps To Successful Data Governance Gartner defines data governance as “the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption and control of data and analytics.”5 However, data governance can focus too much on rules about what people can and can’t do with data. The goal should be to empower people to fix problems and find opportunities. When executed well, data governance can provide the following rewards for improving how your organization handles data.

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5 Best Practices For Data Warehouse Development

Data, Information Technology /

5 Best Practices For Data Warehouse Development Cloud data warehouse technology has revolutionized how businesses store, access, and analyze data. Whether your organization is creating a new data warehouse from scratch or re-engineering a legacy warehouse system to take advantage of new capabilities, a handful of guidelines and best practices will help ensure your project’s success. Some of those best practices may seem obvious, but all too often, businesses fail to spend time up front setting and documenting these decision points, resulting in headaches and inefficiency down the road. In this ebook, we outline five recommendations for putting structure around your data strategy and getting alignment across your business, so the data warehouse you create meets both current and future business needs. These best practices for data warehouse development will increase the chance that all business stakeholders will derive greater value from the data warehouse you create, as well as lay the groundwork for a data warehouse that can grow and adapt as your business needs change

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Six Compelling Reasons To Modernize Your Data Warehouse

Data, Information Technology /

Six Compelling Reasons To Modernize Your Data Warehouse Today, data can be your most valuable asset. But that’s only the case if your organization can extract deep business insights from it. Like crude oil, your data is much more valuable when processed and used to drive your business forward. This is where conventional data warehouses come up short. They can’t handle the volume, complexity, and variety of data companies generate today. Nor can they simultaneously satisfy various departments’ need to access and analyze data in real time A modern, cloud-built “as a service” data warehouse enables you to do things you just can’t do with a legacy on-premises or “cloud[1]washed” data warehouse, including: • Perform advanced data analytics across all user profiles and contextual data sources • Integrate data management and data storage, including governance and quality

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