How BI tools like Splunk are crucial to any organization.

Tools like Splunk are a fantastic example of what real time results can do for you. It literally allows you to drill down to the packet level of what is happening when, where and by whom in your organization. In the age of big data, log management is becoming an absolute necessity, as developers, operations, and, yes, even senior management have to deal with and process huge amounts of machine-generated data. Many organizations have turned to Splunk, a pioneer in the space, to help manage the rising tide of log data – but Splunk can get really, really expensive, FAST.

While there still is not a single, all purpose alternative to Splunk that is as robust and stable, there are several tools that can be used to replicate much of its functionality. In fact SysAdmin Brad Lhotsky documented his quest to build his own central log management system using only open source software.

Of course, his blog entry contains much deeper technical insight, but at the high level, he broke his solution down into three components: Log centralization (rsyslog), log management (logstash/Kibana) and log visualization (Graphite).

Rsyslog was tapped for log centralization over similarly popular alternative syslog-ng because the former offers guaranteed delivery and encrypted transfer in the open source edition – two features that Lhotsky says are becoming of increased importance to regulatory compliance auditors. With rsyslog, Lhotsky was able to build a reliable way to transport event logs from Unix hosts to a central repository.

This is where Lhotsky starts entering Splunk’s territory, calling the company “the 1,000 lb Gorilla in the room.” But in lieu of Splunk, Lhotsky writes that he took the MongoDB-powered Graylog2 for a test drive before settling on logstash. Graylog2 is great, he says, but suggests that its ElasticSearch indexing scheme is “broken,” and if you have to keep a large amount of logs around for compliance reasons, you’re going to take a performance hit. Lhotsky goes so far as to speculate that it’s because Graylog2 only implemented ElasticSearch for, well, search fairly late in the game.

On the other side of the coin, logstash also uses ElasticSearch, but with far more of a focus on scalability, inputs, filter and outputs. The cost, Lhotsky writes, is a polished front-end. Enter Kibana, a PHP front-end for logstash that takes the ElasticSearch indexes and adds a front-end for search and analysis, making the whole platform a lot more usable.

“Kibana fills the gap with the Logstash interface so perfectly. It doesn’t give me everything I’d get with Splunk, but I’ve just touched the functionality I can extract with Logstash,” as Lhotsky puts it.

Finally, he suggests the popular Graphite for data visualization and graphing all the log data you’ve now collected.

As Lhotsky says, this just how he tried to match Splunk-like functionality with open source tools, and it’s still a work in progress.

Below is a video from the CEO of Splunk explaining why his product is unchallenged in the space, and Just What exactly Splunk is.

“Why Splunk?”

Godfrey Sullivan, Chairman and CEO of Splunk, gives you the essential overview of Splunk. Your machine data contains a definitive record of all user transactions, customer behavior, machine behavior, security threats, system health, fraudulent activity and more. Splunk can help you take this machine data and make business sense of it. We call this operational intelligence. Learn how Splunk can help turn silos of machine data into actionable insights for IT and the business.

ROI for Big Data and Analytics

I had the honor to be on The Interop Conference Big Data Panel in Las Vegas yesterday.  The panel was composed of friends from Cloudera, Datameer, Aryaka and it was part of an all-day workshop led by Big Data celebrity and evangelist Chris Taylor.  The focus of the discussion was “The Future of Data.”  The audience was composed of very savvy technical leaders from diverse industries from financial services to retail to universities.

Debates like these can sometimes derail into sales pitches and friendly remarks.  This time, though, the discussion, brilliantly orchestrated by Matt Marshall, Founder and CEO of VentureBeat, turned into a very passionate exchange on the key themes challenging our industry.  We got so excited that Matt and Chris let us go over time to engage with the audience.
By the time we were done, I realized we hadn’t touched on a key theme Matt was interested in: Big Data and ROI.  Many of you will be asked to justify the investment you’re making in Big Data Analytics technology.  ROI is key term you’ll hear.  It stands for “Return On Investment”.

Sure, there are many ways to justify technology investments and firms like Gartner and Forrester have built such models.
I’ll tell you this though.  Companies that are trying to look at Data Analytics as a tactical budget item are in trouble.  Think about it this way: do you have to justify the return on investment of your financial department?  Probably not.  Why?  Because you need it to better run your business, safeguard yourself from exposure and spot opportunities before it’s too late – in short, to run your business better.

The same goes of Big Data and Analytics.  Data Analytics will have immediate and long term return on investment on your culture, your processes and your bottom line.  Now, you do want to use the most appropriate technology so you can avoid burning money on the wrong things…but that’s a different question.  We happen to believe we have the most effective option for Terabyte-range Data Analytics problems.

If you are still running into ROI discussion issues – try this tactic: figure out the cost of a “wrong decision”.  Meaning – what happens when your company, your executive team and/or your front-line employees execute the wrong moves because they didn’t have the right insights?

A customer of ours recently evaluated that the wrong “first move” could cost $50,000 in straight cost or lost opportunity.  And that’s the first move.  Unfortunately, “wrong first moves” rarely happen in isolation and the bill can quickly increase in an uncontrollable manner.  How is that for an ROI?

-Source: B.Runor

5 Myths for Social Business Intelligence

5 Social Business Intelligence Myths Holding Businesses Back

With significant advancements in both technology and methodology surrounding the ability to filter, classify and structure the daily flood of open social commentary at a scale never before possible, several one-time truths related to social business intelligence (SBI) have now evolved into myths. For many organizations, these myths are serving as gating factors to achieving the actionable, strategic SBI to enhance their decision-making and drive overall strategy and innovation.Let’s dispel some of these major SBI myths.

  • Myth 1: Basic “buzz” on our business is the best we can get from social media.For years, companies have relied on basic “buzz,” or the general sentiment consumers have on a business, brand or product. Essentially, this answers the simple question as to whether consumers like something or not. With questionable accuracy and a lack of related actionable insight related to “buzz,” many companies have thrown up their hands assuming that this “thumbs up / thumbs down” gauge is the best they can do when it comes to SBI. Many of these same companies are realizing that understanding whether they would win a popularity contest does not strategically aid their organizations to effectively make decisions, solve problems, set strategy or drive innovation.Today, advanced streaming big data processing solutions are able to filter, classify, structure and report on the continual flood of open social commentary across the Internet in real-time. For example, ListenLogic’s ( streaming big data platform processes over one billion operations per second, delivering real-time answers to incredibly complex questions across massive amounts of streaming open social commentary. This is revolutionizing the valuable insight businesses immediately gain from social media and online channels, allowing companies to receive and leverage genuine, unbiased, timely consumer intelligence on their markets, products, services and competitors through the unsolicited opinions of millions of consumers. Today it’s no longer about the simplistic and misleading concept of “buzz” or “sentiment,” rather it’s about deep understanding and actionable insight to facilitate better decision-making and enhanced strategy. To gain true value out of an SBI solution leading corporations are going  way beyond “buzz.” And with advanced technology it’s now possible.
  • Myth 2: SBI is a “nice to have” for our business.This is actually an understandable stance for companies who believe in the first myth above; that extracting simple “buzz” is the best their organization can hope for from open social commentary. However, given the technology advancements that now deliver deep, real-time, actionable insight on valuable aspects like consumer personas and decision points, path-to-purchase, competitive shifts and influencer analysis, this is now a dangerous myth resulting in some businesses missing a huge strategic competitive advantage; an advantage that in many cases their competitors are already adopting.Pioneering businesses are exploiting this advantage to understand their consumers, markets, products, services and competitors better than ever before. They realize that superficially “monitoring” a snippet or sample of the conversation rather than deeply surveying the entire market discussion places their organization at a major disadvantage. Companies investing in this strategic insight are able to leverage these market truths to take strategic or tactical action in order to gain tremendous advantages in growing and protecting their business, immediately transforming advanced SBI into a necessity rather than a “nice to have.”
  • Myth 3: Boolean keyword search is the only solution available.Although they can be highly specific in the results they provide, Boolean keyword search technology is very limiting in what it delivers, typically providing only 10 percent of the relevant results out there and 90 percent noise (irrelevant results or spam). Yes, if specific keywords like “Chevrolet,” “Sonic” and “service” are used in a search they will find the owner who posts “Having a service issue with my Chevrolet Sonic.” However, most consumers don’t speak and certainly don’t post, message, chat or tweet this way. Plus, if the keyword “Sonic” is run alone it will result in a massive amount of irrelevant content on everything from fast food to video games to broadband services.What this approach won’t find is the owner who posts “Heard the grinding again this AM for the third time.” In this example there is no specific reference to a car or a make or model or “service,” rather it provides a concept. Our brains understand that this is referring to a car and that the poster is having a service issue and is probably at best annoyed and at worst irate. This is parallel processing, which the human brain is capable of. Technology is also capable of this with the use of advanced conceptual models, which recognize how people communicate beyond a select set of keywords. This allows for, as an example, a company to identify irate owners of their products without specifically declaring themselves “irate owners” of their products. This modeling has made Boolean keywords obsolete for accurately discovering conversation concepts from consumers within the complex, unstructured social media universe.
  • Myth 4: Our company has to hire a team of experts to achieve true SBI.With a myriad of self-service, keyword tools available, many companies are realizing that you in fact “get what you pay for” with these first generation solutions. They are also coming to understand that although the cost of the tool may be relatively cheap, the subsequent personnel and quality costs skyrocket financially, particularly when they realize that this approach delivers 90 percent noise and 10 percent signal, missing 90 percent of the relevant signal they’re in search of. What’s worse are the potential reputational and financial costs related to regularly missing key insights.Companies are also realizing that the cost and quality benefits of partnering with an advanced SBI provider with advanced technology and expertise quickly delivers strong ROI related to the level of real-time actionable intelligence they receive to set strategy, make decisions and adjust tactics across their enterprise. Leveraging an advanced Social Listening Intelligence Center like the one pictured below, staffed by expert analysts provides an extension to Marketing, Brand, Sales, Product, Corporate Communication, Risk, Legal and Compliance Teams within the organization.
  • Analysts like these deliver the qualitative “art” of advanced social intelligence which supplements the quantitative “science” of the technology. Both the art and science combined deliver the speed and accuracy businesses require of actionable social business intelligence to gain deep, real-time insight to take immediate action on emerging threats and opportunities.
  • Myth 5: The costs to achieve true SBI is out of our company’s reach.Yes, there are big data platforms that cost in the tens of millions of dollars and require millions more in personnel investment to program and operate. Unfortunately, in most cases, even with this sort of massive investment, these sorts of systems will not give an organization the ability to handle streaming big social data on a real-time basis since they are largely designed to query static data. So, going down this path is largely pointless for the organization looking for real-time, actionable, advanced SBI.Major corporations have achieved advanced social intelligence at a cost of pennies per billion operations per second – without the need to make massive hardware, software or personnel investments like general-purpose data systems require. In fact, leading companies across the pharmaceutical, food and beverage, technology, media and consumer packaged good segments are using this technology today at cost that is typically lower than that of an analyst.

    Given the major competitive advantages true advanced social intelligence delivers, pioneering companies who have overcome these myths are gaining deeper understanding of their consumers, markets, products and competitors to strategically impact their acquisition and retention marketing, promotions, customer service, risk management and product development. All in all they are leveraging a major first-mover competitive advantage which is helping them own markets, drive growth and even protect their operations against risks and threats.

    These organizations are now setting strategies built on market truths extracted from millions of consumers allowing them to make decisions and understand market movements better than ever before.