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What’s Holding Back Operational Analytics: 10 Key Challenges Facing Enterprises

Operational analytics is mission-critical for modern enterprises, enabling them to make data-driven decisions that power growth. Increasingly, all line of business teams rely on its insights, with a range of use cases offering benefits across the organization, from managing inventory to marketing personalization and detecting fraud.

The demand for operational analytics is high, but it’s harder than ever to implement successfully. Modern enterprises are battling complex data ecosystems that are constantly evolving as data architectures become increasingly fluid. Without an agile data infrastructure, harnessing operational analytics remains a pipe dream for many businesses.

Organizations can accelerate their operational analytics journey with a data backbone that sets them up for success. We’ve just released a new ebook to help you do just that and explain how to overcome ten common data integration friction challenges businesses face in getting operational analytics projects off the ground.

Do these challenges sound familiar?

Gathering meaningful data – Data is more complex and dynamic than ever and impossible to manage and sift through manually to “feed” into analytics tools leading to inaccurate or outdated insights.
Choosing the right tools – Organizations lack the infrastructure and tooling to deliver analytics-ready data. Instead, teams are relying on a variety of hand-coded tools, point solutions, and legacy systems.
Visualizing data end-to-end – Organizations don’t have the observability required to see their data infrastructure end-to-end, which makes it impossible to manage data sets and pipelines.
Scaling – As organizations deal with more data than ever, teams are grappling with a scaling challenge that makes analyzing and creating meaningful reports increasingly difficult as data piles up.
Low-quality data – Inaccurate data or problems with change data capture result in the delivery of low-quality data that doesn’t factor in real-time changes and insights.
Data from multiple sources – Data is stored in siloes across multiple systems with inconsistent formats. And IT teams struggle with unknown data sets created by employees without notifying IT.
Budget limitations – Data analytics is expensive. Without large enough budgets, IT often has to turn to quick fixes rather than taking a long-term view of future-proofing data infrastructure.
Data inaccessibility – Organizations would like to make data accessible to the people who need it across the business but lack the controls to ensure that they still adhere to security, governance, and compliance.
Lack of data culture and skills – In many organizations, only skilled coders and data professionals have access to data, shutting out the line of business users who need access the most.
Lack of data security – Organizations are dealing with more data than ever, which means they have more assets to protect. To minimize risk, they need to step up security control.

Choosing the right tools and infrastructure to enable operational analytics has become a “sink or swim” moment. Organizations can’t unleash their true potential unless they tackle these drivers of data integration friction.

Are you an IT or data professional responsible for managing data infrastructure? To learn how to future-proof your organization’s infrastructure and unlock the value of operational analytics, download your eBook here: Five Data Principles for Ensuring Effective Operational Analytics.

The post What’s Holding Back Operational Analytics: 10 Key Challenges Facing Enterprises appeared first on StreamSets.

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