Thursday, April 25, 2013

Maximizing the value of machine data

There are many ways to analyze machine logs and machine data. Most companies start out with simple manual/semi-automated ways to understand logs. The least sophisticated way is for individual users who are responsible for support or system administration to use standard windows or Unix tools to search for strings, find interesting sections withing logs etc. Tools and applications that enable sophisticated search on logs have empowered sysadmins and dramatically increased their productivity.

But machine data contains a lot of strategic value - not just "searchable" events. Simple tools and search based tools and applications are extremely useful for fast troubleshooting needs but when an enterprise is looking to strategically analyze machine log data a multi-dimensional application that can not only search but structure the data for deeper analytics is imperative.

With Glassbeam, companies can realize immediate value for a specific user or a department using Glassbeam search. At the same time, companies can realize higher strategic value by using Glassbeam's tools and applications to parse and model unstructured data for deeper analytics.The initial setup time compared to generic log tools is higher but the longer term value through Glassbeam far out weighs the costs.

Contact us for a complete ROI for your specific needs.

Monday, April 15, 2013

Rise of the machines

While most of the attention in analytics and Big Data has been around human generated content on social media or the web, machine data is fast becoming the growth engine for true Big Data. 
An IDC report pegs machine data to represent 42 percent of all data by 2020, versus just 11 percent in 2005 and that is huge! Splunk has focused on searching the data these machines generate and Glassbeam has focused on deeper analysis by enabling text parsing from the machine logs through its patent pending language and platform. Companies like Cisco, EMC, IBM and Hitachi are already looking at enabling applications in this market. Glassbeam has been working with IBM, Hitachi and EMC for over 18 months  on collecting and analyzing machine data for their internal use as well as new business models based on this machine data for better serving their customers.  Companies like Polycom and Aruba are also leveraging machine data for various use cases with the Glassbeam cloud.

Cisco CEO John Chambers has earmarked machine data and M2M as one of his company's 'big bets' and capable of giving a client a real-time view of its entire operating environment.
We are very excited to be at the forefront of this huge wave. Glassbeam's next generation  cloud based platform can collect, transform and analyze large streams as well as complex multi-structured machine data.

Friday, April 12, 2013

Machine data and the Internet of things


It was interesting to see a recent announcement where two companies have teamed up to provide machine data analytics solution to the market. They should thank me for free press by posting this link here :-) However, I want to thank them for increasing the awareness in the market for such an incredible solution, an area that Glassbeam team knows really well for last few years since the day we launched our solution to the market (in 2009).  The only catch, and a big one, is that approach mentioned in this new announcement is largely professional services driven. In contrast, Glassbeam is cloud based product driven solution and was founded on the premise that any product company will have a huge challenge in assimilating intelligence from its machines by looking at the log data.  

Our core IP of Semiotic Parsing Language (SPL) came about from this very need to handle the complexity of multiple log formats that keep pouring in at increasing frequency and need to be parsed almost at near- real time to make information accessible across different business units (product marketing, management, engineering, sales, services and support).  As a result, SPL innovation from Glassbeam today provides almost 10x process efficiency in creating and maintaining an on-going machine data analytics project inside a product organization.  Proof is in the pudding where in last few years, we have successfully deployed this cloud solution with some of our blue chip accounts such as IBM, EMC, HDS, Aruba networks, Polycom, Fusion IO, etc.  See this earlier blogpost where I talked about the use cases from one of the leading companies that has been an avid user of our cloud based applications 

Another key point to highlight here is the fact that “building” such a solution in-house, even with help from leading technology and service companies, is a monumental undertaking for many product companies whose core competency is to build their core products and not build internal business applications as adhoc IT projects for business needs.  And I consider a machine data analytics as a new class of Big Data business applications whose core input is log data from a product manufacturers’ installed base.  It is quite easy for these companies to underestimate the complexity, size and on-going investments needed to sustain efforts in collecting, storing, parsing, analyzing, and reporting this intelligent output from machines into their support, engineering, marketing, sales, and services organizations.  See this video on our take on “build vs buy” for just this solution of machine data analytics. What you will see in this video is really a true story that we watched unfold at a leading storage company over a number of years.  This story is not uncommon and therefore a gentle reminder to anyone who is thinking of building this solution on-house.  Here is the link... enjoy!

Puneet

Monday, April 8, 2013

CEO column - Q1 2013

This is our first week of the second quarter for 2013.  We finished Q1 on a high note by renewing all current customer contracts such as with IBM, Aruba etc, as well as acquiring new customers across wireless and medical device verticals.  We are also very close to signing up a leading cloud provider as a customer in Q2.  These new wins prove the power of Glassbeam as a core technology that can make sense out of any kind of machine data.  Use cases for all these new wins are still centered around the solution pillars of providing valuable information to support and IT organizations for better troubleshooting and operational intelligence, as well as giving product and engineering organizations better insights into their products' usage in the installed base. 

We also reached critical milestones on product front in Q1.  Glassbeam Search was launched in Feb at the Strata Big Data conference. With this new product, we are now able to dramatically show faster time-to-value to our customers who are interested in analyzing raw log data. The search roadmap is being executed on short sprints and our customers will be seeing updates often.

Finally, 451Group released a report on Glassbeam in March. It captured salient points of our new product roadmap along with recent market traction and organizational updates.  As you read this report, you will see that there is lots going on, and that is super exciting in this burgeoning machine data analytics market. Here is 451Group take on Glassbeam (borrowing their summary here):

"Glassbeam has a well-thought-out roadmap, which should add fresh legs to its business by widening its addressable audience. Of course, the downside is that it will expose the startup to a wider range of competitors, including vendors in the log management and analysis sector. Nonetheless, we continue to think Glassbeam has a strong story to tell when it comes to multi source data analytics, which is a prescient issue in the 'big data' world. Furthermore, we welcome the real-time analytic engine as we continue to believe that machine-to-machine data needs to be analyzed in real time to have an actual impact on the devices that generate
the information."

Overall, a great quarter, many more things to do in near future, so stay tuned for more updates.

Puneet