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BECOMING DATA-DRIVEN
Key challenges and considerations
More data than ever is being generated and stored
On-premises tools and legacy data stores can't meet today's demands,
organizations need new data stores that can scale and grow as business needs
change—whether from the gigabytes and terabytes handled today or the
petabytes and exabytes that will be managed in the future.
Data siloed across multiple sources creates productivity
and cost inefficiencies
Modern organizations need to easily access and analyze diverse types of data,
including log files, clickstreams, voice, and video. However, these wide-ranging
data types are typically stored in silos across multiple data stores. To extract
intelligence, organizations must break down these silos to unify all types of
data. This important optimization of costs and operations is transforming
the infrastructure from a source of complexity and expense to an engine of
value creation.
The current state of decision making is unsustainable
Gartner reports that 65 percent of decisions made today are more complex
(involving more stakeholders or choices) than they were five years ago.
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To
make better and faster decisions, organizations need the ability to perform
analytics and machine learning (ML) operations in an agile, cost-effective
way —using optimal tools and performance to scale for each use case.
Organizations can no longer waste precious time constantly redeploying and
reconfiguring infrastructure to scale performance and capacity.
Analytics and machine learning adoption is still impeded
by a lack of skills and inertia
Many businesses are struggling to make progress with scaling analytics and ML
tools. Gartner finds that organizations investing in AI moved just 54 percent of
their AI proof-of-concept pilots into production.
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A continued lack of data and
ML skills and quantity or quality of data to train on are just some of the issues
slowing progress in this important area. Still, the need to help business users
leverage data-driven decision making is growing.
Trying to maintain data governance is a full-time job
Traditional data architectures require risky, complicated management
procedures because data is accessed from so many places. Granting, tracking,
auditing, and removing employee access—while simultaneously remaining
in compliance with a growing number of regulations—is a full-time job.
Automating these mandatory data governance tasks frees modern teams to
shift their focus back to innovation.
Data is increasingly difficult to secure
There was a time when IT teams chose between making their architectures fast
or making them secure. Now, they need to deliver both. Meanwhile, security
attacks increased by 31 percent from 2020 to 2021, according to Accenture's
State of Cybersecurity Resilience 2021 report, while average attacks per
organization increased from 206 to 270 year over year.
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How can organizations
maximize privacy and security?
2
"How to make better business decisions," Gartner, October 20, 2021
3
"Half of AI models never make it to production," EnterpriseAI, August 23, 2022
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"How aligning security and the business creates cyber resilence," Accenture, 2021
