3 Reasons Most Enterprises Aren't Ready For Advanced Analytics Strategies

Dan Ortega

"Data, data everywhere, and not a drop to drink." All businesses are fully aware of how much data they're swimming through on a daily basis. And because its buzzy and trendy, most of these businesses are looking to do more with their data, striving to implement cool sounding technologies like machine learning and predictive analytics.

How many, exactly? 41% of executives in a recent 451 Research survey of advanced analytics are looking to begin implementing applications such as Machine Learning or Predictive Modeling in the next 12 months, and an additional 14% plan to do so in the next 24.

And why shouldn't they? These sophisticated programs are highly efficient and represent the future of many different verticals supported by the technology industry.

Yet as enterprises and their leadership see these initiatives on the horizon, a startling number are overlooking a crucial factor that could make or break the success of these investments: the quality of their own data. With some enterprises curating up to 200 disparate data sources, ensuring data quality is no easy task. But getting it right can literally make the difference between a very public crash 'n' burn, or being the standard that everyone tries to emulate.

Here are three reasons why the average enterprise isn't properly prepared for an advanced analytics strategy.

Reason 1: Medieval Methods for Managing Data Quality

According to the survey, 37% of enterprises employ a manual data cleansing process. Given current data volumes, manually cleaning something isn't so 1990s, it's actually more like 1500s. Many of these enterprises are starting to look towards algorithmic automation – but how can they successfully automate advanced processes when their back-end data quality checks remain manual?

44.5% of respondents are in a reactive mode, meaning they only deal with their data quality when it becomes a problem … that they notice (and by the way, their customers noticed way before they did).

The majority of respondents (65%) acknowledge up to 50% of business value can be lost to poor data quality – think that number is going to decrease when the number of initiatives that rely on clean data increases?

Reason 2: Businesses Don't Know The Exact Quality of Their Data

Because of these current Data Quality Management "strategies", IT departments and C-suite executives have a lack of faith in the actual quality of their data.

Over half (57%) of respondents in this survey were "somewhat confident", "unaware", or "less than confident" in the state of their data. Not exactly a resounding endorsement.

This feeling is compounded by the dependency on manual effort to drive remediation in many enterprises' data quality process. Manual entry was the leading cause of poor data quality, also coming in at 57%.

To be fair, you can't blame employees for making mistakes in data entry or processing, but you can blame their management for not providing them with the right tools to handle the volume of data they face every day.

Reason 3: The Stream of Data Today is About to Become a Tsunami

If proper preparations aren't undertaken right now with the relatively manageable amount of data that currently exists, it will be not just be harder, it will be impossible to get a handle on it at the rate that data sources and volumes will continue to expand over the next 3-5 years.

95% of survey respondents acknowledge they expect data to increase (the other 5% presumably in businesses that won't be around in five years).

70% expect data volumes to grow by 70%, while nearly all of the remaining 30% expect it to grow by more than 75%. Chances are, all of them are underestimating what's headed in their direction.

The problems faced by the enterprise today are significant, but can be managed if IT executives deal with the data quality issue now. Tools and technologies are available to ensure viable data quality, which becomes the foundation for growth and value-add, but the choice to act now or quickly get buried is in our collective face, and requires immediate action.

Dan Ortega is VP of Marketing at Blazent.

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3 Reasons Most Enterprises Aren't Ready For Advanced Analytics Strategies

Dan Ortega

"Data, data everywhere, and not a drop to drink." All businesses are fully aware of how much data they're swimming through on a daily basis. And because its buzzy and trendy, most of these businesses are looking to do more with their data, striving to implement cool sounding technologies like machine learning and predictive analytics.

How many, exactly? 41% of executives in a recent 451 Research survey of advanced analytics are looking to begin implementing applications such as Machine Learning or Predictive Modeling in the next 12 months, and an additional 14% plan to do so in the next 24.

And why shouldn't they? These sophisticated programs are highly efficient and represent the future of many different verticals supported by the technology industry.

Yet as enterprises and their leadership see these initiatives on the horizon, a startling number are overlooking a crucial factor that could make or break the success of these investments: the quality of their own data. With some enterprises curating up to 200 disparate data sources, ensuring data quality is no easy task. But getting it right can literally make the difference between a very public crash 'n' burn, or being the standard that everyone tries to emulate.

Here are three reasons why the average enterprise isn't properly prepared for an advanced analytics strategy.

Reason 1: Medieval Methods for Managing Data Quality

According to the survey, 37% of enterprises employ a manual data cleansing process. Given current data volumes, manually cleaning something isn't so 1990s, it's actually more like 1500s. Many of these enterprises are starting to look towards algorithmic automation – but how can they successfully automate advanced processes when their back-end data quality checks remain manual?

44.5% of respondents are in a reactive mode, meaning they only deal with their data quality when it becomes a problem … that they notice (and by the way, their customers noticed way before they did).

The majority of respondents (65%) acknowledge up to 50% of business value can be lost to poor data quality – think that number is going to decrease when the number of initiatives that rely on clean data increases?

Reason 2: Businesses Don't Know The Exact Quality of Their Data

Because of these current Data Quality Management "strategies", IT departments and C-suite executives have a lack of faith in the actual quality of their data.

Over half (57%) of respondents in this survey were "somewhat confident", "unaware", or "less than confident" in the state of their data. Not exactly a resounding endorsement.

This feeling is compounded by the dependency on manual effort to drive remediation in many enterprises' data quality process. Manual entry was the leading cause of poor data quality, also coming in at 57%.

To be fair, you can't blame employees for making mistakes in data entry or processing, but you can blame their management for not providing them with the right tools to handle the volume of data they face every day.

Reason 3: The Stream of Data Today is About to Become a Tsunami

If proper preparations aren't undertaken right now with the relatively manageable amount of data that currently exists, it will be not just be harder, it will be impossible to get a handle on it at the rate that data sources and volumes will continue to expand over the next 3-5 years.

95% of survey respondents acknowledge they expect data to increase (the other 5% presumably in businesses that won't be around in five years).

70% expect data volumes to grow by 70%, while nearly all of the remaining 30% expect it to grow by more than 75%. Chances are, all of them are underestimating what's headed in their direction.

The problems faced by the enterprise today are significant, but can be managed if IT executives deal with the data quality issue now. Tools and technologies are available to ensure viable data quality, which becomes the foundation for growth and value-add, but the choice to act now or quickly get buried is in our collective face, and requires immediate action.

Dan Ortega is VP of Marketing at Blazent.

Related Links

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The Latest

Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

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 ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

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 ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

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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 ...

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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 ...

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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 ...

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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.