The enterprise data warehouse challenge: How to choose the best solution for your organization

By Noah Kuttler,

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There are good reasons why enterprise data warehouse implementations are so daunting to CIOs. Not surprisingly, they’re some of the same reasons why data warehouse developers are so highly compensated today.

There’s the challenge of unifying diverse and scattered data. If your organization is like most, your data — both semi-structured and unstructured — is spread across all kinds of platforms. The average organization manages 33 unique data sources.

High and rising expectations for analytics performance further stress the requirements of today’s data warehouse systems. Many analytics workloads ­— fraud prevention, logistics, stock prediction and social analytics — require real-time or unattended execution, or, in some cases, both.

While cloud solutions may offer attractive savings, they are only one part of a balanced strategy. For example, if much of your data resides in-house, you’ll want to process that data where it resides for optimal performance and efficiency. At the same time, you’ll want to be able to include any data that’s on the cloud and have it available to your data scientists.

Many challenges, one solution

To meet their analytics demands, many organizations are turning from dedicated enterprise data warehouse implementations to unified data analytics solutions that combine data warehouse and analytics functionality with built-in data science tools.

These plug-and-play solutions integrate processing, memory, storage, data warehouse technology, and a self-service front end – all pre-configured for the organization’s data environment, and fine-tuned to meet to meet or exceed the organization’s analytics performance demands. Install them and load them with data, and they can start cranking out decision-driving answers and insights within hours – and keep those answers and insights coming with minimal ongoing administration. 

If such a solution sounds like the answer to your analytics challenge, you can make the best possible investment by shopping for the following characteristics:

  • Broad data unification capabilities. The solution should be able to take structured and unstructured data in all kinds of formats, from all kinds of sources — existing data warehouses and data marts, Hadoop, business intelligence systems, etc. — and analyze that data wherever it resides.
  • Embedded machine learning. Machine learning enables unattended analytics processing that continually improves and optimizes itself to support better-informed and faster decision making over time. Like many organizations, yours may be grappling with how to infuse machine learning into various aspects of your business. Look for a solution equipped to handle all of the analytics you already need to run, plus more advanced machine learning analytics, all while eliminating multiple systems and the inevitable implementation headache.
  • Full support for a hybrid data management infrastructure. Any solution you choose should work seamlessly across cloud and on-premises data, and allow the use of the same tools and skills to process workloads on the cloud or on-premises as needed with a path to the cloud when your strategy requires it.
  • Mission-critical availability and robust scalability. Your organization’s need for, use of, and reliance on analytics is only going to increase. Make sure any solution you choose can support the user, data and processing demands for the foreseeable future.
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