Data Warehouses and In-Memory Technologies: Myths and Reality

Data Warehouses and In-Memory Technologies: Myths and Reality

Data Warehouses and In-Memory Technologies: Myths and Reality
In-memory technology can open doors to a new world of analytics possibilities — but make sure your information value chain is strong enough to support the investment.

Analytical solutions have transformed organizations across every industry. From aspirational companies that are starting their analytics journey to mature enterprises that provide competitive advantage leveraging analytics, investments in this space are ever growing. Surely, “The price of light is less than the cost of darkness” — investments in analytics technology, data, and people are critical in transforming businesses. At the same time, to secure these investments, a well-thought out business value case accompanied by a comprehensive data and analytics strategy is required to achieve the business value.

Many software and service providers promise quick results and assure that they will fulfill ambitious long-term goals. One such promise is in-memory technology to analyze big data, improved business performance, decreased time to value of analytics projects, and reduced complexity in layered data architectures. In-memory technology can open doors to a number of business value cases to foster analytics transformation enabled through big data, real-time reporting, etc.

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However, in most other business value cases, the technology is not an imperative but only an expensive choice. It is neither a nostrum nor a fundamental requirement for analytics solutions. More importantly, these investments demand a stronger information value chain to leverage the faster processing and optimization capabilities, and a clear enterprise data warehouse architecture and management process to sustain the benefits.

I drew few interesting considerations from my personal experiences.

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