Enterprise data has changed. It is growing in volume, variety and velocity. Have enterprise data warehouses (EDWs) changed as much? Not really. They continue to be rigid and hard to change.This means the Chief Analytics Officer (CAO), a role that is now taking root in large organizations - thanks to the attention data is drawing - has a tough ask: manage the data, extract actionable insights and make them available to business, all in real time. And by the way, if traditional EDWs don’t help, find a way and keep budgets under control.
The solution lies in Data Discovery Platforms. When architected meticulously using commodity hardware on cloud that leverages open source technologies and agile methodologies, Data Discovery Platforms are quick to develop, easy to use, reduce time to insights and cost way less than traditional BI systems. Throw in sophisticated map reduce code and we have a platform that can ensure speed to analytics, sending traditional EDWs on their way to history (well, almost).
The real problem is not the data or the analytics. It is the availability of people, processes and technological skills to meet the requirements of a Data Discovery Platform. Only the most hardened CAO would think of taking on this challenge singlehandedly. The simpler and faster route to acquiring the capabilities to manage sophisticated, real-time enterprise data is to engage a partner who can provide an end-to-end analytics service covering platform, analytics, domain expertise and visualization. What we have here then, is Analytics-as-a-Service. Its impact can be immediate and profound. Business users can rely on IT to provide the data and the transformation dictated by functional needs, without long, interminable waits.
How does this become possible? Actually, Analytics-as-a-Service calls for practical, down-to-earth thinking that goes beyond cutting-edge technology. Enterprises must marry the analytics platform with an understanding of business needs. Hence establishing a new operating model(see figure 1) between business and IT can go a long way in accurately identifying and understanding data that matters to the enterprise there by ensuring faster time to insights.