BIG DATA

A very relevant POST. This is why we focus on delivering business value rather than abstract theories like machine learning or arbitrary algorithms on big data. In many companies today, projects get started based on technology or buzz words and while these efforts are important from an R&D point of view, they are not substitutes for proper evaluation of business benefits from big data projects.

I just returned from a field visit meeting key executives at one of our leading customer site. Talk of a global account with hundreds of sales, field engineers, product specialist, support and services people scattered across all geographies around the world supporting their blue chip accounts. And what do they use as ONE singular Big Data app to get the REAL truth from mining machine / log data from their installed base every day – nothing else but Glassbeam!

With the buzz around Big-Data it is important to keep in mind what can be done with the data. For those of us who have read or watched Money Ball it is apparent that you can use insights derived from data and combine that with your intuition to make better decisions.

Glassbeam gives you the tools to make such decisions in your business, based on unfiltered machine data.

Most progressive support organizations are now moving to leverage Big data to become proactive. They want to put behind the days when support teams were always behind the curve, with the customer knowing about problems much before support knows about it. Further its takes days or weeks for support teams to understand what is going on based on logs uploaded.

The Q4, 2011 issue of MCKINSEY QUARTERLY focused exclusively on big data and its implications for various industries moving forward (Free registration required). Makes for a very interesting read!

Some key takeaways:

– Industries with the most potential for reaping benefits from big data include Finance, Insurance, Transportation and Warehousing, and Health Care.

Simply put is stands for Semiotic Parsing Language. But, more importantly it is the core of our technology.

SPL is an intuitive language that describes the structure and semantics of a class of documents. For semi-structured data, as is typical with our customers, it means we can utilize the structure inherent in the data and reflecting it in a high-performance and highly normalized data warehouse.

A great article from INFORMATION WEEK cogently summarizes the problems many organizations face with capturing and harnessing big data.

We hear the same refrain from customers and prospects — of data not being available easily; and even if it’s available it being non-reliable, non-centralized and non-structured. All making it harder to assimilate and analyze.