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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

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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

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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

The Latest

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...