The rapid pace of innovation in the database landscape is causing IT leaders to entirely re-evaluate their data environment strategies. From Hadoop, to NoSQL, to Cloud data assets – the expectation for IT groups to stay current with both technologies and approaches has never been higher. The unfortunate truth is that while many CIOs and IT Directors work to integrate new technologies into their organizations, they miss the bigger pitfall – forgetting the role self-service analytics also plays in shaping the modern data environment.
This paper focuses on three shifts within the modern data environment that Information Technology leaders everywhere are starting to recognize as critical to the success of their overall organization’s ability to become data driven.
Concepts covered in this material include:
- The emergence of Hadoop and NoSQL and what that means for the traditional enterprise data warehouse
- Understanding how to evaluate cloud data assets
- Embracing self-service analytics as part of data platform strategy
We've also pulled out the first several pages of the whitepaper for you to read. Download the PDF on the right to read the rest.
The Problem Isn't Different. Just Harder.
Providing organizations with reliable data for better decision-making is an undertaking that has not fundamentally changed in decades. Despite massive technology advances and new tactics, the IT organization managing data
infrastructure today still has the same overall mission: moving data from its
moment of creation and making it accessible and understandable by decisionmakers
at the moment of need.
However, while the objective has stayed the same, the obstacles to successfully
create and maintain a source of analytical truth within a business have become
exponentially more difficult.
Perhaps the biggest hurdle in recent years within the modern data environment
has been new sources of data that generate unprecedented amounts of output,
often with very little (if any) structure. From clickstreams, server logs, and social
media sources to machine and sensor readings, the onslaught of data from these
channels has been overwhelming—literally. From an economic and performance
point of view, traditional enterprise data warehouses (EDWs) simply cannot keep
up with this data tidal wave.
This has sparked a complete re-think of data capture and analysis strategies and
given rise to a new generation of data storage solutions aimed at schema-less
capture, hardware scalability, and the moving of compute capability closer to (if
not on top of) data stores themselves.
Though still young by relational database standards, these newer, non-relational
solutions have gained serious traction in recent years and matured rapidly to
support some of the largest and most complex corporate enterprises in the world.
While this has been done largely as a means to complement existing enterprise
data warehouse infrastructures, it never the less creates a more complex data
ecosystem for IT to manage.
Adding to the hurdles IT must overcome in the ongoing mission to maintain
a healthy data environment is the availability of data from cloud applications.
Many organizations hold cloud solutions such as Google Analytics, Salesforce,
Netsuite, Zendesk, and others as core parts of their infrastructure. The data
they generate is critical to organizational reporting. Integrating data from these
cloud solutions and making it accessible to the company has become a standard
requirement for IT.
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