New 2023 report reveals future prospects of artificial intelligence

Google Cloud has released the 2023 Data and Artificial Intelligence Trends Report, which looks at 5 key trends surrounding data and artificial intelligence strategies. The report notes that consumer demands, market conditions, and new artificial intelligence (AI) and machine learning (ML) technologies are all evolving, and increased data complexity is creating a different landscape than a year ago.

The study conducted by IDC surveyed more than 800 global enterprise organizations, asking them to name the challenges they face when using data. The biggest challenges, benefits gained from data and AI cloud solutions, and how they will use these solutions.

Trend 1: Static data is outdated; the era of unified data cloud is here. The report states that by 2026, 7PB of data will be generated globally every second. Currently, only 10% of the data is original data, and the remaining 90% is replicated data. These siled data stores do the organization no good. Google Cloud says it needs a better way to store, manage, analyze and apply this data. The report explores how unified cloud can be the solution as it provides common infrastructure for databases, data warehouses and data lakes, streaming, business intelligence (BI), artificial intelligence (AI) and machine learning (ML).

Andi Gutmans, general manager and vice president of database engineering at Google Cloud, said that a unified data cloud can integrate data and insights into transformative digital experience applications and better decision-making. “So users can get the right information when they need it to achieve the best results.”

Trend 2: Opendataecosystems allow data to move freely between platforms, helping enterprises avoid data lock-in and silos. Pre-built open source services and applications such as PostgreSQL, Kafka, TensorFlow, PyTorch, Presto, JanusGraph, and Apache projects help speed development and reduce costs. On the other hand, the report points out that these open standards and open architectures can also perform operations such as data analysis locally, helping to reduce data movement expenses.

Trend 3: According to data from Google Cloud, We are on the edge of the artificial intelligence tipping point, can’t Separate the management data cloud and the AI ​​cloud. Applications powered by artificial intelligence are solving more problems and gaining unprecedented insights from data. June Yang, vice president of cloud artificial intelligence and industry solutions at Google Cloud, said data scientists, analysts, developers and ML creators are now working closely together and they want a single interface to access tools in a unified portal. Data and insights. The report noted that 80% of organizations said embedded support for AI/ML model execution made them more likely to choose a specific data cloud platform.

Additionally, pre-trained models and low-code training methods are helping enterprises achieve their AI and ML project goals, making “citizen” data scientists possible. The report found that 81% of organizations said having more “citizen” data scientists would significantly improve their ability to apply advanced analytics to more projects.

Trend 4: Enterprises are rethinking BI. Google Cloud says they are abandoning the traditional, dashboard-centric model in favor of an action-centric BI paradigm. In this paradigm, insights are delivered to more people in more environments to support more types of workflows. BI and analytics can help identify potential trends, data anomalies and potential issues, and 87% of organizations want their BI software to support the development and deployment of predictive models. The trend of embedding BI and analytics into enterprise applications is also on the rise, as businesses look to reach wider internal audiences and improve customer-facing applications.

Trend 5: Data risk management is emerging. Companies are making sense of their unknown data to improve security, governance and trust. As more and more data, both unstructured and structured, is collected, understanding exactly what data is being collected is critical to understanding how to protect it and maintain compliance. Manually finding, scanning, and classifying each data set to determine risk is a challenge, especially for use cases like companies using customer chat applications, where sensitive information may end up in chat transcripts.

Google Cloud says that understanding all your data and understanding your data intake pipelines and storage silos is the most critical step in data risk management. Next comes the classification, where many organizations are using ML and business automation tools. Implementing automated controls can help reduce risk when storing and sharing data. For example, if a customer will provide sensitive data, an automated process can redact the sensitive information before storing it. Google Cloud predicts that by 2027, 66% of large enterprises will have made significant investments in data control plane technologies that can measure the risk inherent in data and reduce it through security and filtering.

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