What you need to know
Data can be structured, like entries in a database with predefined formats, or unstructured, like free-flowing text and multimedia content. Data is essential to optimize modern technology such as artificial intelligence (AI) and generative AI systems, which are swiftly transforming the way we work and do business. That’s where your proprietary data and unique processes become key to maximizing the value you get from AI and driving business reinvention.
Data and AI are closely connected: AI systems use data to learn, predict and create content. The cloud is essential as it offers the scalable infrastructure needed to handle large data volumes and run AI models effectively. This combination improves organizational abilities, enabling advanced data services and management.
A well-planned data strategy is crucial for maximizing the benefits of data and AI. It involves aligning data activities with business goals and ensuring the organization can manage and utilize data effectively, without being hindered by its volume or complexity.
What is the difference between traditional data and big data?
Traditional data generally consists of smaller, structured data sets that can be efficiently handled and analyzed using standard database management tools. In contrast, big data refers to vast, complex data sets that are challenging to process and analyze. These require sophisticated approaches, including AI and cloud technologies, to manage effectively.
What's the magic behind data modernization?
Data modernization requires upgrading systems to use newer technologies like cloud computing and AI. This update helps organizations improve data quality, speed up insights and make better decisions by breaking down barriers between different sets of data.
How does data empower organizations?
Data helps organizations by improving their efficiency, flexibility and ability to innovate. Using data effectively, along with AI and cloud technologies, aids in daily decision-making and supports the creation of new products and services. This gives organizations a competitive advantage in the market.
Challenges and limitations
Managing data presents several challenges, including protecting privacy and security, integrating data from different sources and maintaining data quality. As the amount and type of data increases, organizations also need to expand their infrastructure and capabilities, which can require significant resources.
of CXOs say data readiness is the top challenge with applying generative AI.
Why is there so much buzz surrounding data?
The excitement around data stems from its power to drive a competitive edge. The sheer amount and types of enterprise data have grown exponentially over the past years. Now, with generative AI, companies are sitting on a goldmine of potential value. With the right data strategy and readiness, companies can unlock new opportunities to innovate and differentiate.
By 2025, the world is expected to generate 7 petabytes of data per second, up from 2.7 petabytes per second in 2021.
Data terms to know
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Data management
The process of handling and organizing the data an organization creates and collects.
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Big data
Extremely large data sets that can be analyzed to uncover patterns, trends and connections.
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Data services
Tools that help with handling, analyzing and visualizing data.
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Unstructured data
Refers to information that lacks a specific format or organization, such as free-flowing text or video.
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Structured data
Refers to information organized in a predefined format, allowing easy access, processing and analysis by automated systems.
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Synthetic data
Artificial data that is created by computer programs or simulations, used to train machine learning models.
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Data governance
Management of the availability, usability, integrity and security of the data used in an organization.
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Data modernization
Upgrading data systems to newer, more efficient platforms.
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Data foundation
The infrastructure (frameworks, capabilities, tools and services) required to efficiently and effectively store, process, manage and serve data.
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Data Supply Chain
The methods of productizing data so that an organization’s people can consume and use it with gen AI tools. A data supply chain is founded on a secure, cloud-based infrastructure.
What else?
In the age of generative AI, understanding the relationship between data, AI and the cloud is crucial for modern businesses. Proper data management helps turn big data and unstructured data into valuable insights. Generative AI can create synthetic data to fill gaps and support data strategies. This includes making sure data is ready, managing data migration and updating systems with strong data platforms. Treating data as a product and investing in a solid data foundation helps companies improve their data processes, promote collaboration and drive business value.