How to ensure that AI and analytics projects don't fail?
2023 is a year of escalating economic crisis and climate risks, so the need for data-driven insights to drive efficiency, resilience and other key initiatives will be a top priority for businesses in 2023. Many businesses have been trying to adopt advanced analytics and artificial intelligence to meet this need. Now, they must turn proof of concept into return on investment.
Many businesses are making huge strides, investing a lot of talent and the right software. However, many enterprises’ AI and analytics projects fail because they don’t have the right underlying technologies in place to support AI and advanced analytics workloads. Some businesses rely on outdated legacy hardware systems, while others are hampered by the cost and control issues that come with leveraging the public cloud. Most businesses are so seduced by the power of AI software tools that they fail to choose the right hardware.
As the pace of innovation accelerates in these areas, now is the time for technology leaders to evaluate what they need to successfully leverage artificial intelligence and analytics projects.
Enterprises need to build suitable infrastructure
In a survey of more than 2,000 business leaders, the research firm IDC found that more and more respondents realize that artificial intelligence systems require Running on purpose-built infrastructure delivers real value. In fact, many respondents cited the lack of proper infrastructure as the main reason why AI projects fail. IDC noted that factors hindering the move to AI-centric infrastructure are concerns about cost and strategy, as well as the complexity of existing data environments and infrastructure.
While industry experts agree that deploying new platforms is difficult for enterprises, there are ways to optimize the value of AI and analytics projects, with fundamental considerations including compute power, memory architecture, and data processing, storage, and security. sex.
The key is data
According to a survey report recently released by Harvard Business Review magazine, data availability is a key performance indicator for companies that successfully deploy artificial intelligence and analytics. . In short, successful business leaders have democratized their companies’ data—making it accessible to employees, capturing data from customers and suppliers, and sharing it with others. Processing data is the key to core technology and hardware. Here's what to consider:
Getting data: To be able to analyze more data faster, enterprises need to do it faster with high-performance servers and AI-friendly chips, whether CPUs or GPUs. processing. Modern computing infrastructure is designed to improve business agility and time to market by supporting workloads such as database and analytics, artificial intelligence and machine learning, high-performance computing, and more.
Store data: Many businesses have large amounts of data to gather actionable insights, but they need a secure and flexible place to store it. The most innovative unstructured data storage solutions are flexible and primarily designed to achieve reliability at scale without sacrificing performance. Modern object storage solutions deliver performance, scalability, resiliency and compatibility on a globally distributed architecture to support enterprise workloads such as cloud native, archiving, IoT, artificial intelligence and big data analytics.
Protect your data: Cyber threats are everywhere, whether at the edge, on-premises or in the cloud. An enterprise's data, applications and critical systems must be protected. Many business leaders are looking for a trustworthy infrastructure that can operate with maximum flexibility and business agility without compromising security. They want to adopt a zero-trust architecture to embed security capabilities in storage, server, hyperconverged, network and data protection solutions enterprise-wide.
Moving Data: As the landscape of data generation changes and data traffic patterns become more complex, surging demand requires most enterprises to re-evaluate their networks. In order for data to flow seamlessly, they must have the right network system in place. However, traditional proprietary networks often lack scalability, proven cloud-based solutions, and automation, while open source solutions can be costly and inflexible. Open networking meets the challenge by empowering the modern enterprise with software choice, ecosystem integration and automation from edge to core to cloud platforms.
Access to data: The development and deployment of artificial intelligence technology increasingly occurs on powerful and efficient workstations. These purpose-built systems enable teams to work smarter and faster with AI and analytics at all stages of AI development and increasingly during deployment because they enable edge inference. To give employees access to the data they need, businesses will need to move away from siled, rigid and expensive legacy systems and toward new solutions that enable speed, scalability and confidence in analytics and artificial intelligence. Data Lakehouse supports business intelligence, analytics, real-time data applications, data science, and machine learning in one place, providing fast and easy access to trusted data for data scientists, business analysts, and others who need data to drive business value. Direct access to functionality.
Focus on Results
Analytics and artificial intelligence promise to drive better business insights from data warehouses, data flows, and data lakes. But companies first need to assess their ability to develop and successfully deploy AI or analytics projects. Most enterprises need to modernize critical infrastructure and hardware to be able to support AI development and deployment from edge to data center to cloud platforms. Businesses that do this will find their data and applications to be force multipliers. Along the way, they will implement upgrades to ensure data is secure and accessible to achieve IT and business goals for years to come.
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