Blogs

ABCS of log file analytics

Nov 21, 2012

A. Aggregate data from all log files – All log files are not the same – Most people think of sys logs when they think of log files – no thats not all. Logs from product and software companies are bundles containing many files each of different types and formats. Some are time series data of events but others can be stats, session information, usage metrics, configuration etc.

Machine data benefits sales and service

GLASSBEAM
Sep 25, 2012

Most people think of sysadmins and IT when they think log files. We at Glassbeam think sales operations and services! Why? Because machine data provides the truth and nothing but the truth about your customers and how they use your products. This information is extremely valuable to enterprises.

How can sales people benefit from log data?

Big data and dsl ( domain specific language )

GLASSBEAM
Oct 06, 2012

DOMAIN SPECIFIC LANGUAGES have been around for a long time – a great example of a DSL is SQL for RDBMS. A DSL is differentiated from a general-purpose language such as C, Java or Python since a DSL is geared towards a specific domain.

What is the value of big data?

GLASSBEAM
Sep 16, 2012

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.

Big data and dsl ( domain specific language )

GLASSBEAM
Sep 10, 2012

Domain Specific Languages have been around for a long time – a great example of a DSL is SQL for RDBMS. A DSL is differentiated from a general-purpose language such as C, Java or Python since a DSL is geared towards a specific domain.

Business impact of machine data analytics in a fortune 100 account

GLASSBEAM
Sep 05, 2012

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!

Loading log data for analytics

GLASSBEAM
Aug 25, 2012

We all have situations where we wished we could instantly analyze data in unstructured formats. For example machine log data or web log data, or log data from applications. Over the years tools for analyzing weblogs (for example Google analytics) and most recently some machine data search tools( Splunk) have made the task easier. However there are files with multi-structured data with each section having multiple formats. Further in many enterprises, log data need to be combined with other structured data to make sense.

Machine data and customer intelligence

GLASSBEAM
Aug 15, 2012

Game companies like Disney and Zynga perfected the art and science of analytics by collecting log data from games, as they are played, and using the data to better understand their users. They gained a huge competitive edge by using the log data to tailor their products. They gained valuable knowledge on how the product was being used by their customers, pinpoint performance issues preventing the users from loading a game etc.

Moneyball for sales and service

GLASSBEAM
Jul 10, 2012

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.

Leveraging machine data to reduce support costs

GLASSBEAM
Jul 22, 2012

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.

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