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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What's Blocking Scalable AI?

For all the attention AI receives in corporate slide decks and strategic roadmaps, many businesses are struggling to translate that ambition into something that holds up at scale. At least, that's the picture that emerged from a recent Forrester study commissioned by Tines ...

Not All Networks Are Built for the Edge

From smart factories and autonomous vehicles to real-time analytics and intelligent building systems, the demand for instant, local data processing is exploding. To meet these needs, organizations are leaning into edge computing. The promise? Faster performance, reduced latency and less strain on centralized infrastructure. But there's a catch: Not every network is ready to support edge deployments ...

Why Observability Is the Missing Piece in Your Business Growth

Every digital customer interaction, every cloud deployment, and every AI model depends on the same foundation: the ability to see, understand, and act on data in real time ... Recent data from Splunk confirms that 74% of the business leaders believe observability is essential to monitoring critical business processes, and 66% feel it's key to understanding user journeys. Because while the unknown is inevitable, observability makes it manageable. Let's explore why ...

Gartner: Regular AI System Assessments Triple the Likelihood of High GenAI Value

Organizations that perform regular audits and assessments of AI system performance and compliance are over three times more likely to achieve high GenAI value than organizations that do not, according to a survey by Gartner ...

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Artificial intelligence is no longer a future investment. It's already embedded in how we work — whether through copilots in productivity apps, real-time transcription tools in meetings, or machine learning models fueling analytics and personalization. But while enterprise adoption accelerates, there's one critical area many leaders have yet to examine: Can your network actually support AI at the speed your users expect? ...

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In today's fast-paced AI landscape, CIOs, IT leaders, and engineers are constantly challenged to manage increasingly complex and interconnected systems. The sheer scale and velocity of data generated by modern infrastructure can be overwhelming, making it difficult to maintain uptime, prevent outages, and create a seamless customer experience. This complexity is magnified by the industry's shift towards agentic AI ...

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