How Enterprises Can Accelerate Data Initiatives and Drive Growth in 2025

As we look ahead to 2025, the majority of organizations understand the importance of using data to unlock intelligent insights. However, what many leaders aren’t so immediately aware of is how essential data acceleration is to maintaining a competitive edge. This is because organizations are grappling with a higher number of data sources, massive volumes, and complex queries. On one hand, this is a major positive—these rich data sources are the key to uncovering valuable insights that you can directly leverage to support intelligent business decisions. Yet it also means that processing and analyzing these data sources is more time-consuming, acting as an immediate barrier to innovation and better decision-making. That’s why implementing data acceleration tools is essential. By putting the right tools in place that can handle these massive datasets, organizations don’t need to be fearful of growing their volumes or adding more sources to the mix. In turn, it ensures that IT budgets can use resources wisely and efficiently, moving through the system to unlock insights that deliver meaningful results for the business. Through strategic infrastructure planning, tool selection, and GPU-accelerated solutions, businesses can optimize their processing, drive faster insights, and make decisions that actually grow their companies. Let’s take a closer look at how accelerating processing speed impacts business growth, the factors affecting speed, and the key steps for implementing acceleration effectively. The correlation between processing speeds and business growth Most companies today strive for data-driven models. Further, most leaders are aware that in today’s cutthroat markets, time is always of the essence. Insight that promises to boost customer sales with a refined new product won’t deliver much value if your competitors get there months ahead of you. This means improving your processing speeds has a direct correlation to business growth, offering a major competitive advantage. McKinsey’s “The Data-Driven Enterprise of 2025” put it perfectly when it noted that “Rather than defaulting to solving problems by developing lengthy—sometimes multiyear—road maps, people are empowered to ask how innovative data techniques could resolve challenges in hours, days or weeks.” With this, businesses can fine-tune personalization, predict market trends, and quickly test new models or solutions – all of which drive growth and uncover breakthroughs. The ability to boost processing speeds is a fundamental goal, but to harness the full potential, businesses should adopt a comprehensive approach – such as the “Data Acceleration Flywheel” – that ensures they can actually extract and use their data insights for growth. Implementing the Data Acceleration Flywheel The Data Acceleration Flywheel aims to move away from the idea that initiatives are standalone projects, and towards a mindset that sees it as a complete lifecycle: a continuous process that maximizes speed, accuracy, and efficiency at every stage. The model recognizes a number of unique yet interdependent stages that transform raw data into actionable insights that drive strategic decisions, operational efficiency, and customer satisfaction. Let’s walk through each stage of the Flywheel and explore actionable strategies for success. Collect: The first stage focuses on feeding the system with multiple data sources that relate to key business processes such as customer service or internal supply chain operations. The range of digital platforms in use today mean that everything from market trends, product metrics, and financial systems can be used as an input, but it;s important to ensure quality and consistency across these. Store: Next, creating an acceleration flywheel needs to ensure that these sources are being stored in an efficient and affordable way. A balance of cloud, on-premise, or private cloud options helps to ensure accessibility, security, and scalability that supports long-term growth and allows your organization to process these rich data sources as needed. Ingest: Although many assume that collecting data in and of itself is all that’s needed to deliver actionable insights down the line, data consolidation and preparation is a key stage that can’t be overlooked. By properly preparing through normalization and cleaning for reliable analysis, as ingestion quality directly impacts analytics performance. The ingestion step is crucial; it can determine the success or failure of the entire data pipeline. Compute: Once data has been consolidated and prepared, it’s time to start uncovering business insights. The volume of this information often means that conventional CPU-only systems often struggle with large data volumes, whereas advanced GPU-powered technologies like SQream Blue provide the computational strength to significantly reduce query processing times and lower costs by using GPU multi-core processing and distributed computing models. Growth: With rapid data processing taken care of, one’s organization is ready to implement these insights to drive strategic value, improve operations, enhance customer experience, and maintain a competitive edge. Take action: Once the system is up and running, it’s important to keep looking for ways to accelerate the process at every stage, from collection to growth. A cohesive data acceleration strategy helps organizations spot opportunities to leverage new technologies and processes that enhance speed, scalability, and data quality. The Data Acceleration Flywheel ensures that data is collected, processed, and applied efficiently, creating a self-reinforcing cycle of data-driven growth. Key ways to maintain a cohesive data acceleration strategy As mentioned, the data acceleration flywheels aim to not only move organizations towards insights more quickly but also instill a new mindset that looks to identify and improve on speeds continuously. The first way to do so is by measuring processing speeds. This is crucial to understand the potential bottlenecks in your system. Here, it’s important to set up a comprehensive system of metrics that measure processing speed continuously. Look at things like the time it takes your system to respond to individual data queries, the volume of data processed within a set timeframe, the execution time that tasks have from input to competition, and data latency which is the delay in receiving and processing data, especially important for real-time analytics. While measuring processing speeds is the first step, it’s also important to recognize the underlying factors that could contribute to delays. For example, high-performance systems, especially those utilizing GPUs, are faster at handling parallel computations compared to traditional CPU-based systems. In addition, larger and more complex data sets require more processing power, which can slow down workflows without the right technology. Data architectures also have a major impact on overall processing speeds. Efficient data pipelines and integration between systems minimize delays. Achieving success with data acceleration A strategic approach is important to effectively implement data acceleration. First, begin by identifying bottlenecks in your data pipeline, examining processing speeds, and recognizing any underperforming areas within your data ecosystem. Next, look for solutions that offer GPU-powered acceleration, which is known for drastically improving processing times even for massive datasets. This technology can handle petabyte-scale processing, providing valuable insights rapidly at lower costs than CPU-based systems. With this place, ensure your setup is compatible with the likes of Apache Airflow and Prefect, supporting standard connectors (ODBC, JDBC) to streamline data workflows. Finally, continuously monitor the performance and regularly adjust your approach to ensure continued efficiency and address any emerging bottlenecks. Accelerating data processing in 2025 is no longer a “nice to have,” but rather a necessity. As datasets grow larger and more complex, businesses must be able to harness these massive volumes to drive advancement, operational efficiency, and better, streamlined decision-making. By adopting data acceleration tools and strategies, businesses can transform data into a powerful growth engine rather than a bottleneck. Solutions that leverage advanced technologies like GPU-powered processing help reduce query times, optimize resources, and ensure scalability, making it possible to meet any demands that come your way. Ultimately, embracing data acceleration is about more than just speed; it’s about ensuring that initiatives seamlessly integrate into the organization’s fabric. This enables stakeholders to act decisively and confidently, translating raw data into strategic value that sustains long-term success. Article by Allison Foster, Content Marketing Manager at SQream As we look ahead to 2025, the majority of organizations understand the importance of using data to unlock intelligent insights. However, what many leaders aren’t so immediately aware of is how essential data acceleration is to maintaining a competitive edge. This is because organizations are grappling with a higher number of data sources, massive volumes, and complex queries. On one hand, this is a major positive—these rich data sources are the key to uncovering valuable insights that you can directly leverage to support intelligent business decisions. Yet it also means that processing and analyzing these data sources is more time-consuming, acting as an immediate barrier to innovation and better decision-making. That’s why implementing data acceleration tools is essential. By putting the right tools in place that can handle these massive datasets, organizations don’t need to be fearful of growing their volumes or adding more sources to the mix. In turn, it ensures that IT budgets can use resources wisely and efficiently, moving through the system to unlock insights that deliver meaningful results for the business. Through strategic infrastructure planning, tool selection, and GPU-accelerated solutions, businesses can optimize their processing, drive faster insights, and make decisions that actually grow their companies. actually Let’s take a closer look at how accelerating processing speed impacts business growth, the factors affecting speed, and the key steps for implementing acceleration effectively. The correlation between processing speeds and business growth The correlation between processing speeds and business growth Most companies today strive for data-driven models. Further, most leaders are aware that in today’s cutthroat markets, time is always of the essence. Insight that promises to boost customer sales with a refined new product won’t deliver much value if your competitors get there months ahead of you. This means improving your processing speeds has a direct correlation to business growth, offering a major competitive advantage. McKinsey’s “ The Data-Driven Enterprise of 2025 ” put it perfectly when it noted that “Rather than defaulting to solving problems by developing lengthy—sometimes multiyear—road maps, people are empowered to ask how innovative data techniques could resolve challenges in hours, days or weeks.” The Data-Driven Enterprise of 2025 With this, businesses can fine-tune personalization, predict market trends, and quickly test new models or solutions – all of which drive growth and uncover breakthroughs. The ability to boost processing speeds is a fundamental goal, but to harness the full potential, businesses should adopt a comprehensive approach – such as the “Data Acceleration Flywheel” – that ensures they can actually extract and use their data insights for growth. actually Implementing the Data Acceleration Flywheel Implementing the Data Acceleration Flywheel The Data Acceleration Flywheel aims to move away from the idea that initiatives are standalone projects, and towards a mindset that sees it as a complete lifecycle: a continuous process that maximizes speed, accuracy, and efficiency at every stage. The model recognizes a number of unique yet interdependent stages that transform raw data into actionable insights that drive strategic decisions, operational efficiency, and customer satisfaction. Let’s walk through each stage of the Flywheel and explore actionable strategies for success. Collect: The first stage focuses on feeding the system with multiple data sources that relate to key business processes such as customer service or internal supply chain operations. The range of digital platforms in use today mean that everything from market trends, product metrics, and financial systems can be used as an input, but it;s important to ensure quality and consistency across these. Collect: The first stage focuses on feeding the system with multiple data sources that relate to key business processes such as customer service or internal supply chain operations. The range of digital platforms in use today mean that everything from market trends, product metrics, and financial systems can be used as an input, but it;s important to ensure quality and consistency across these. Collect: Store: Next, creating an acceleration flywheel needs to ensure that these sources are being stored in an efficient and affordable way. A balance of cloud, on-premise, or private cloud options helps to ensure accessibility, security, and scalability that supports long-term growth and allows your organization to process these rich data sources as needed. Store: Next, creating an acceleration flywheel needs to ensure that these sources are being stored in an efficient and affordable way. A balance of cloud, on-premise, or private cloud options helps to ensure accessibility, security, and scalability that supports long-term growth and allows your organization to process these rich data sources as needed. Store: Ingest: Although many assume that collecting data in and of itself is all that’s needed to deliver actionable insights down the line, data consolidation and preparation is a key stage that can’t be overlooked. By properly preparing through normalization and cleaning for reliable analysis, as ingestion quality directly impacts analytics performance. The ingestion step is crucial; it can determine the success or failure of the entire data pipeline. Ingest: Although many assume that collecting data in and of itself is all that’s needed to deliver actionable insights down the line, data consolidation and preparation is a key stage that can’t be overlooked. By properly preparing through normalization and cleaning for reliable analysis, as ingestion quality directly impacts analytics performance. The ingestion step is crucial; it can determine the success or failure of the entire data pipeline. Ingest: Compute: Once data has been consolidated and prepared, it’s time to start uncovering business insights. The volume of this information often means that conventional CPU-only systems often struggle with large data volumes, whereas advanced GPU-powered technologies like SQream Blue provide the computational strength to significantly reduce query processing times and lower costs by using GPU multi-core processing and distributed computing models. Compute: Once data has been consolidated and prepared, it’s time to start uncovering business insights. The volume of this information often means that conventional CPU-only systems often struggle with large data volumes, whereas advanced GPU-powered technologies like SQream Blue provide the computational strength to significantly reduce query processing times and lower costs by using GPU multi-core processing and distributed computing models. Compute: Growth: With rapid data processing taken care of, one’s organization is ready to implement these insights to drive strategic value, improve operations, enhance customer experience, and maintain a competitive edge. Growth: With rapid data processing taken care of, one’s organization is ready to implement these insights to drive strategic value, improve operations, enhance customer experience, and maintain a competitive edge. Growth: Take action: Once the system is up and running, it’s important to keep looking for ways to accelerate the process at every stage, from collection to growth. A cohesive data acceleration strategy helps organizations spot opportunities to leverage new technologies and processes that enhance speed, scalability, and data quality. Take action: Once the system is up and running, it’s important to keep looking for ways to accelerate the process at every stage, from collection to growth. A cohesive data acceleration strategy helps organizations spot opportunities to leverage new technologies and processes that enhance speed, scalability, and data quality. Take action: The Data Acceleration Flywheel ensures that data is collected, processed, and applied efficiently, creating a self-reinforcing cycle of data-driven growth. Key ways to maintain a cohesive data acceleration strategy Key ways to maintain a cohesive data acceleration strategy As mentioned, the data acceleration flywheels aim to not only move organizations towards insights more quickly but also instill a new mindset that looks to identify and improve on speeds continuously. The first way to do so is by measuring processing speeds. This is crucial to understand the potential bottlenecks in your system. Here, it’s important to set up a comprehensive system of metrics that measure processing speed continuously. Look at things like the time it takes your system to respond to individual data queries, the volume of data processed within a set timeframe, the execution time that tasks have from input to competition, and data latency which is the delay in receiving and processing data, especially important for real-time analytics. While measuring processing speeds is the first step, it’s also important to recognize the underlying factors that could contribute to delays. For example, high-performance systems, especially those utilizing GPUs, are faster at handling parallel computations compared to traditional CPU-based systems. In addition, larger and more complex data sets require more processing power, which can slow down workflows without the right technology. Data architectures also have a major impact on overall processing speeds. Efficient data pipelines and integration between systems minimize delays. Achieving success with data acceleration Achieving success with data acceleration A strategic approach is important to effectively implement data acceleration. First, begin by identifying bottlenecks in your data pipeline, examining processing speeds, and recognizing any underperforming areas within your data ecosystem. Next, look for solutions that offer GPU-powered acceleration, which is known for drastically improving processing times even for massive datasets. This technology can handle petabyte-scale processing, providing valuable insights rapidly at lower costs than CPU-based systems. With this place, ensure your setup is compatible with the likes of Apache Airflow and Prefect, supporting standard connectors (ODBC, JDBC) to streamline data workflows. Finally, continuously monitor the performance and regularly adjust your approach to ensure continued efficiency and address any emerging bottlenecks. Accelerating data processing in 2025 is no longer a “nice to have,” but rather a necessity. As datasets grow larger and more complex, businesses must be able to harness these massive volumes to drive advancement, operational efficiency, and better, streamlined decision-making. By adopting data acceleration tools and strategies, businesses can transform data into a powerful growth engine rather than a bottleneck. Solutions that leverage advanced technologies like GPU-powered processing help reduce query times, optimize resources, and ensure scalability, making it possible to meet any demands that come your way. Ultimately, embracing data acceleration is about more than just speed; it’s about ensuring that initiatives seamlessly integrate into the organization’s fabric. This enables stakeholders to act decisively and confidently, translating raw data into strategic value that sustains long-term success. Article by Allison Foster, Content Marketing Manager at SQream Article by Allison Foster, Content Marketing Manager at SQream Allison Foster, SQream SQream