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Is Your Enterprise Fully Maximizing the Value of Its Available Data?

Jack Mardack
Actian

Modern enterprises are generating data at an unprecedented rate but aren't taking advantage of all the data available to them in order to drive real-time, actionable insights. According to a recent study commissioned by Actian, titled Actian Datacast 2019: Hybrid Data Trends Snapshot, more than half (54%) of enterprises today are unable to efficiently manage nor effectively use data to drive decision-making.

Data is constantly in motion — it's being generated, harnessed and analyzed in real-time. Gone are the days of data at rest and stagnant data lakes — enterprises need to consume data rather than just store it and ensure all of the data accessible to them is leveraged. Businesses that leverage more of their data sooner and more frequently to generate actionable insights will outpace competitors who are less agile.

Competition between enterprises using the data-driven insights available to them will establish new winners and losers in every business — however, many businesses are throwing away the insights they aren't able to unlock due to time, money or resource constraints. The survey found that 84% of enterprises would deploy more data if it were cheaper and easier to do and 50% of these businesses say data complexity issues due to siloed applications are a top barrier to entry for accessing data and gaining effective real-time insights. When businesses take the necessary steps to fully leverage their data, such as implementing modern IT infrastructure, they become agile, competitive and able to provide a superior experience to their customers.

Only 25% of Enterprises with Access to the Data They Need, Have the Freshness or Recency of Data They Desire

In addition to fully harnessing and analyzing available data, the speed at which this is performed is critical. Enterprises need to pursue data architecture that will enable all their unique data-related ambitions to be processed in real-time. This means being able to bring analytics capabilities to all the places where their data already lives and enjoy the highest levels of query performance across the totality of their data (even hundreds of terabytes) is becoming a data architecture prerequisite.

As AI and machine learning become more actively involved in defining user experience, the lines are blurring between traditionally separate transactional databases and data warehouses used for analytics. Thus, the role of "real-time" data in the enterprise goes beyond internal reporting and actionable insights and is beginning to shape user experience. User experience innovation has already become the most disruptive force in business history, with many upstart software companies devouring their incumbent competitors.

In the near future, many more enterprises will leverage data to differentiate and win with superior customer experience. Data-driven insights derived from fresh and available data are crucial to execute on this strategy.

Only 34% of Enterprises Using Data to Drive Decision-Making Are Using it to Drive Breakthrough Insights and Innovations vs. Business as Usual Operational Reporting

For many enterprises, data is being used for business-as-usual purposes, not to transform the business or provide competitive advantages, as it has the potential to do. While business-as-usual operations keep enterprises running from day-to-day, limiting data to operational reporting tasks means missing a key piece of the data puzzle — new insights that lead to awareness of products, markets, consumer trends, strategy and more. Data is being generated in the enterprise that is not being put to good, strategic use, and the risks of missing out on these opportunities pose serious and immediate risks to enterprises. Gaps in the system take engineers weeks or even months to bring forth something actionable for a company's wider team to pursue, rather than the real-time insight needed for the current pace of business.

Maximizing data for a more strategic future

Enterprises are increasingly demonstrating a strategic business need for hybrid data-based insight, enabling a data-driven process to store, access and analyze data wherever the business need is and wherever compliance requirements demand — both on-premise and across multiple clouds. Enterprises equipped with data management architecture that can deliver these capabilities and help them access actionable insights from the full set of fresh data available to them in real-time will be poised to outpace competitors and fully maximize on their data and opportunities in the market.

Jack Mardack is VP of Marketing at Actian

Hot Topics

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

Is Your Enterprise Fully Maximizing the Value of Its Available Data?

Jack Mardack
Actian

Modern enterprises are generating data at an unprecedented rate but aren't taking advantage of all the data available to them in order to drive real-time, actionable insights. According to a recent study commissioned by Actian, titled Actian Datacast 2019: Hybrid Data Trends Snapshot, more than half (54%) of enterprises today are unable to efficiently manage nor effectively use data to drive decision-making.

Data is constantly in motion — it's being generated, harnessed and analyzed in real-time. Gone are the days of data at rest and stagnant data lakes — enterprises need to consume data rather than just store it and ensure all of the data accessible to them is leveraged. Businesses that leverage more of their data sooner and more frequently to generate actionable insights will outpace competitors who are less agile.

Competition between enterprises using the data-driven insights available to them will establish new winners and losers in every business — however, many businesses are throwing away the insights they aren't able to unlock due to time, money or resource constraints. The survey found that 84% of enterprises would deploy more data if it were cheaper and easier to do and 50% of these businesses say data complexity issues due to siloed applications are a top barrier to entry for accessing data and gaining effective real-time insights. When businesses take the necessary steps to fully leverage their data, such as implementing modern IT infrastructure, they become agile, competitive and able to provide a superior experience to their customers.

Only 25% of Enterprises with Access to the Data They Need, Have the Freshness or Recency of Data They Desire

In addition to fully harnessing and analyzing available data, the speed at which this is performed is critical. Enterprises need to pursue data architecture that will enable all their unique data-related ambitions to be processed in real-time. This means being able to bring analytics capabilities to all the places where their data already lives and enjoy the highest levels of query performance across the totality of their data (even hundreds of terabytes) is becoming a data architecture prerequisite.

As AI and machine learning become more actively involved in defining user experience, the lines are blurring between traditionally separate transactional databases and data warehouses used for analytics. Thus, the role of "real-time" data in the enterprise goes beyond internal reporting and actionable insights and is beginning to shape user experience. User experience innovation has already become the most disruptive force in business history, with many upstart software companies devouring their incumbent competitors.

In the near future, many more enterprises will leverage data to differentiate and win with superior customer experience. Data-driven insights derived from fresh and available data are crucial to execute on this strategy.

Only 34% of Enterprises Using Data to Drive Decision-Making Are Using it to Drive Breakthrough Insights and Innovations vs. Business as Usual Operational Reporting

For many enterprises, data is being used for business-as-usual purposes, not to transform the business or provide competitive advantages, as it has the potential to do. While business-as-usual operations keep enterprises running from day-to-day, limiting data to operational reporting tasks means missing a key piece of the data puzzle — new insights that lead to awareness of products, markets, consumer trends, strategy and more. Data is being generated in the enterprise that is not being put to good, strategic use, and the risks of missing out on these opportunities pose serious and immediate risks to enterprises. Gaps in the system take engineers weeks or even months to bring forth something actionable for a company's wider team to pursue, rather than the real-time insight needed for the current pace of business.

Maximizing data for a more strategic future

Enterprises are increasingly demonstrating a strategic business need for hybrid data-based insight, enabling a data-driven process to store, access and analyze data wherever the business need is and wherever compliance requirements demand — both on-premise and across multiple clouds. Enterprises equipped with data management architecture that can deliver these capabilities and help them access actionable insights from the full set of fresh data available to them in real-time will be poised to outpace competitors and fully maximize on their data and opportunities in the market.

Jack Mardack is VP of Marketing at Actian

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.