Here is a fun video courtesy of Forbes regarding Big Data:
So many fads out there there days with cloud, SaaS and of course, Big Data being fads *insert extreme sarcasm here*.
“Data analysis software maker Tableau Software Inc forecast better-than-expected current-quarter revenue after reporting quarterly results that handily beat analysts’ estimates, sending its shares up 16 percent in extended trading.”
Some quick facts from this report:
- License sales of the company’s business intelligence software jumped 93 percent to $58.0 million, with existing customers contributing 66 percent of total license revenue.
- Tableau said it closed 179 sales orders of greater than $100,000 and added more than 1,800 customer accounts in the fourth quarter. The company now has 17,000 customer accounts.
- The company said it expects first-quarter revenue of $61 million to $63 million, beating analysts’ average expectation of $60.3 million, according to Thomson Reuters I/B/E/S.
- The company posted a net income of $11.2 million, or 16 cents per share, in the fourth quarter, compared with a loss of $1.1 million, or 3 cents per share, a year earlier.
- Revenue nearly doubled to $81.5 million.
Here is a look at the one year stock price performance:
While past revenue is never an indication of potential future revenue, stock market investors continue to place a solid bet on Big Data analytics software maker, Splunk. In this article we will explore some reasons investors like Splunk so much, we will outline some potential investment risks and provide some comparison to other companies just to gain a little perspective on what Splunk has achieved in a fairly short amount of time.
What is the simple explanation Splunk from ones’ personal experience?
Splunk is a software product that I personally really enjoy using. It’s easy to use, they offer free trials for various versions and they offer it the way that everyone can simply consume (on-premise or as-a-service). In a nutshell, Splunk allows you a flexible, intuitive way to gather, organize and visualize all your Machine Data. According to their website:
Using Splunk Software, you can:
- Troubleshoot problems and investigate security incidents in minutes
- Monitor your end-to-end infrastructure to avoid service degradation or outages
- Gain real-time visibility and critical insights into customer experience, transactions and behavior
- Make your data accessible, usable and valuable across your organization
Let’s take a look at some of the financial numbers for ticker symbol SPLK (http://finance.yahoo.com/q/ks?s=SPLK+Key+Statistics) at the time of writing this blog (8/30/2013). Note: These numbers will certainly change so it will be interesting to see what happens.
- Yearly Revenue: $218.96 million
- Gross Profit: $177.52 million
- Quarterly Revenue Growth (YoY): 53.8%
- Profit Margin: -14.77%
- Shares Outstanding: 103.81 million
- Current Stock Price: $55.24 per share
- Market Cap Valuation: $5.73 BILLION
Now, some personal commentary on these numbers. First, 53.8% YoY growth is absolutely impressive and this stands out as a key reason why stock market investors continue to buy shares in the Company. Second, $177M profit on $218M revenue means that Splunk is in a high-margin market segment (Big Data) and is also executing well from a profitability stance. An 81% gross profit margin is absolutely insane and unheard of.
While the growth and profit numbers are impressive it’s important not to get too far ahead of ourselves because there are real financial risks. With all the good numbers I just described, you will notice that the Profit Margin listed above is actually -14.77% so you might be asking yourself ‘how can this be?’ Simply put, Splunk is aggressively investing in the future, and therefore, is less concerned about the short-term profitability of the Company and growth is the key objective. When a software company like this is in growth mode they are doing things such as hire talented workers, execute many sales and marketing campaigns as well as invest in building more and better technologies.
The other number that is somewhat of a concern is the $5.73 BILLION Market Capitalization Valuation. This is not to say that it’s undeserved, just that it should be justified. Market Cap Valuation is based on a simple calculation of Shares Outstanding (103.81M) times Current Stock Price ($55.24) which results in $5.73B for Splunk as of today. This basically means that at Splunk’s current Yearly Revenue of $218M it would take them roughly 26 years to achieve what the stock market has considered the ‘value’ of the Company. While there are surely many other factors to consider when it comes to determining a company’s valuation, I just wanted to point out this startling number because it’s an indication of clear interest by investors in bright potential future of either the Company and/or the market segment itself.
Market Cap Valuation comparison to other industry icons
Just for fun let’s compare two long time industry icon companies and where Splunk ranks among the others from strictly a Market Cap Valuation standpoint.
One, Eastman Kodak Company. You will know them as the famous photo film manufacturer founded in 1880. They have admittedly fallen into crisis mode having filed Chapter 11 bankruptcy in 2012, but again, this is just for fun to compare Market Cap to give your perspective how the stock market investment community feels about these respective companies.
Eastman Kodak Company (EKDKQ) key statistics:
- Yearly Revenue: $3.97 billion
- Shares Outstanding: 272.78 million
- Current Stock Price: $.05 per share
- Market Cap Valuation: $14.18 million
- 13,000 employees
SPLK $5.73 BILLION versus EKDKQ $14.18 million in Market Cap Valuation. Isn’t that amazing? Also, notice that even on Yearly Revenue of nearly $4 billion the Market Cap is incredibly low.
Next, Federal National Mortgage Association (a.k.a. Fannie Mae). Founded in 1938, Fannie Mae is a company at the heart of our financial system who provides securitized mortgage loans to other lending institutions. Most long-term mortgage loans such as the 30-year can be traced back to FNMA in one form or another.
Fannie Mae (FNMA) key statistics:
- Yearly Revenue: $32.26 billion
- Shares Outstanding: 5.74 billion
- Current Stock Price: $1.22 per share
- Market Cap Valuation: $7 billion
- 72,000 employees
Even though Fannie Mae has a Yearly Revenue of $32.26 billion the stock market has this Company valued at less than one-quarter this yearly revenue number at $7 billion.
Market Cap isn’t everything but investors typically are forward-looking
There are many sorts of investors whether they are long-term (buy and hold stocks for years), short-term (hold for weeks or months) or even day-traders. So this is not to say that Splunk is a good company simply because the overall investor community has priced-up their stock so high. Neither is this to say that neither Kodak nor Fannie Mae cannot reclaim their once iconic status in the eyes of investors simply because of their respective low Market Cap’s. However, high market caps and continually growing stock prices are fairly good indicators of a Company that is doing a lot of right things and Splunk seems to be hitting on all cylinders.
Is Big Data the next Big Thing?
In summary, when starting a business of any kind it’s critically important to choose the right market if you plan for impressive growth. In reflection, Kodak made absolutely the right choice to enter the film business in the late 1800’s and was certainly most successful for many decades. Also, Fannie Mae was one of the most profitable companies in the world when they were established in the late 1930’s to service the mortgage loan business and they, also, experienced quite prosperous times for many decades. And, while this might be a truly getting ahead of ourselves I ask you, is Splunk (and the Big Data market segment in general) the next Big Thing?
Use Case: In today’s business environment, more than ever, it’s simply not good enough to be average. Organizations of all sizes have to strive to create competitive advantages, understand trends and gain better insight into operational efficiency. One of the most useful techniques to accomplish these goals is to Exploit Big Data through analysis. However, this is challenging due to the volume, velocity and variety of content that must be analyzed. Image-only files are useless in data analysis. Therefore, in order to take the all-important first step in exploiting all of your content is to apply indexes so that computer systems can properly begin to understand the information.
- Reporting: Business executives are generally paid good money to make important decisions about the business and these decisions are often based on reports. These reports are often compiled from various data sources such as spreadsheets, interviews with customers or employees and possibly other documents. This method of gathering all this various data is not only time-consuming but it’s problematic due to the fact that the data is often presented in a inconsistent manner. For this reason you will want to use a Big Data system such as Splunk where business executives and have instant access to sets of data from various sources that is real-time information and presented through dashboards or graphics that can clearly show trends or other information that is pertinent to the decision making process.
- Predictive analytics: Historical reporting is fantastic to analyze information yet this information is typically in the past. Imagine if you can proactively determine a trend or predict, with solid data, future events? This is a major benefit of Big Data aggregation. For example, given the right set of data you can probably predict where mortgage interest rates will increase or decrease in a particular geography. You would use statistics such as current available housing inventory supply, real-time unemployment rates as well as possibly the latest transactions within a certain time period. Also, using the same Big Data aggregation concept but for a completely different application is predictive analytics is in the field of Healthcare. If you can feed enough Index information into a Big Data solution then healthcare providers can narrow down much quicker the proper diagnose on people with illnesses where this can enrich people’s lives.
- Business process improvement: There is always room for improvement and this is especially true in the business world and the most effective way to effect positive improvement is through the visibility to business processes themselves. Once you understand the process then you apply matrixes to these processes such as time needed to complete a task or steps needed to finish a project. A Big Data solution such as Splunk is an ideal complement to the efficiency improving technologies such as ABBYY Data Capture with tangible return on investment through reduced labor costs associated with manual data entry and Box with highly effective collaboration where enterprise workers can get work done quickly and be overall more effective in their business activities. Just by deploying a Big Data analysis system with Data Capture efficiency and Collaboration on mobile that is secure is absolutely one way to achieve better process improvement but just imagine all the possibilities that can be done with the data itself. And it all starts by Exploiting Big Data with Indexes.
Solution Description: This solution might sound gaudy and complicated but it’s actually straight-forward and logical. There are three basic concepts which are Index Creation (ABBYY technology), Index Analysis (Splunk) and secure Image Storage (Box). We will use several technologies to create indexes for various reasons and then we will feed our Big Data system all these indexes so that this software can do what it does best. The Big Data system allows administrators to easily aggregate all this data and then create dashboards, reports and other useful business intelligence tools. So the process is quite logical: Capture indexes for all sources including existing databases, paper documents and, of course, images and send all these indexes to Big Data. Then send the images to Box for safe storage, easy access and effective collaboration.
Note: This is a software developer and systems integrator solution. We are using Splunk as our Big Data aggregator in this solution because it is so easy to configure, yet extremely effective. Splunk can only perform well when you can provide lots of “Index” information. As seen in this graphic, “Index” is at the core for Big Data to even begin analyzing different data sets.
- Box account
- ABBYY FlexiCapture for Automatic Data Capture
- ABBYY Recognition Server for Full-Page recognition
- ABBYY TouchTo for touch indexing
- Splunk Big Data software (free download)
Configuration Steps (Complexity = Moderate to Involved):
- Start Splunk and review choose Add data
- Depending on the output type and format of indexes select the proper Splunk Add Data function
- Now connect Splunk to your data source(s)
- For example, maybe Recognition Service you might choose ‘From files or directories’ and as an option Preview data before indexing
- …and for FlexiCapture you might choose the ‘any other data…’ then ‘Consume data from databases’ because you output to a SQL database directly
- …and for TouchTo you might choose the ‘a file or directory of files’
- After connecting all the index data sources to Splunk it is advisable to review the Splunk Manager options to familiarize yourself with all the various settings and configurations available
- Now that you have configured Splunk to utilize Indexes from your various Data Capture and Conversion sources, you will want to gather information contained within Box. To do this a software developer would utilize the Box API (Application Programming Interface) to import data such as tags, get comments or get file info
- A complete list of all the Splunk Indexes can be viewed in Manager
- Once all the indexes have been aggregated within Splunk then organizations can truly realize the benefits of Big Data with detailed reporting, predictive analytics and/or improved business process via simple visual tools such asdashboards
Associated screen prints on this solution:
1. Splunk architecture with Index at the core
2. Start Splunk
3. Add data
4. Splunk add From files or directories
5. Data preview
6. Any other data…
7. Consume data from databases
8. Splunk add A file or directory of files
9. Splunk Manager
10. Splunk Indexes Manager
11. Splunk dashboard
What do you think? “Big Data” is still a relatively new idea and many use cases are just coming to light. How can you imagine using Big Data? The possibilities to innovate in this area are tremendous, do you have a story to tell?
Oh, my dear friend “Big Data”. Oh, “Big Data” you are spawning a whole new industry. “Big Data” you are re-engineering the required skills of the stereotypical Information Technology (IT) specialist like never before. “Big Data” and your close relatives “Cloud Computing”, “Social Media” and “Mobile”, you are the new frontier of innovation.
From a personal standpoint, I generally avoid using over-used, or faddish, terms such as “Social Media”, “Cloud Computing” or “Big Data” but these terms do serve a purpose. Therefore, for this blog post I will concede to my detest for these terms and I would like to share some thoughts on “Big Data” and how as an industry we can take a very complicated topic and break it down into some logical methods that can be applied to help solve “Big Data” problems.
Understanding Big Data
The best description I’ve ever heard of “Big Data” was quite simple, yet extremely powerful to illustrate the essence of the problems we are trying to solve. “Big Data” is defined as Volume, Variety and Velocity (Updated note: I would like to acknowledge Gartner for the original reference of the 3V’s — http://blogs.gartner.com/doug-laney/deja-vvvue-others-claiming-gartners-volume-velocity-variety-construct-for-big-data/). Let’s break each of these items into how this confluence is contributing to a great opportunity for savvy IT individuals to reinvent themselves into Big Data content management professionals.
Think about Volume of content creation for a second. Volume is unquestionably increasing at incredible rates and I don’t think most reasonable people would dispute this fact. With more people using high speed internet connections than ever, plus these people becoming more proficient at creating content and just more people in general contributing information are combined forces that are causing this tremendous increase in Volume. The sheer number of content items that are created and stored is increasing and logically it should be assumed that the individual file sizes themselves are likely increasing.
Next in breaking down Big Data into easily digestible bite-size chunks is the concept of Variety. Take your personal experience and think about how much information you create and contribute in your daily routine. Your voicemails, your e-mails, your file shares, your TV viewing habits, your Facebook updates, your LinkedIn activity, your credit card transactions, etc. The point is, whether you consciously think about it or not the Variety of information you personally create on a daily basis which is being collected and analyzed is simply overwhelming. In this basic example, a data analysis specialist would have to gather data from audio, video, receive data from third-party systems and, of course, have a computer understand the information, as well as context, of images.
The speed at which data enters organizations these days is absolutely amazing. With mega internet bandwidth nearly being common place anymore in conjunction with the proliferation of mobile devices, this simply gives people more opportunity than ever to contribute content to storage systems. Additionally, the ease of use to contribute information only encourages more creation and more storage of content.
Big Data. Making sense of electronic junkyards
Understanding some of the factors such as Volume, Variety and Velocity that are causing this perfect storm of Big Data growth can also help us solve problems. Now let’s ask ourselves the following questions and start to create solutions with one simple, yet highly effective concept (which I will explain below).
- Question: What is the root problem with solving Big Data issues?
- Answer: Too much information is introduced into systems without the proper “Index” so computers can not understand the information, nor context of the data.
As individuals, businesses and organizations we are creating ‘electronic junkyards’. We are creating electronic junkyards because the images we upload have no context; much less index. Things go in but they rarely come out with much value and, therefore you have created this junkyard of nearly useless electronic information. There are many reasons including the following:
- “Non-compliant” users that insist on using more useful tools than their corporate policy offers (i.e. consumerization). You might have personally, or at least known of someone, who used an IT-unauthorized cloud storage service to share a large PowerPoint file, for example. In this case that information is simply non-discoverable and not available to the in the set of data for analysis.
Geoffrey Moore – “The Big Disconnect”
- IT reluctance to enforce business rules upon submission. For fear of a poor experience and having poor adoption of technology due to user frustration. In other words, IT continues to allow users to upload content without proper tagging, or metadata, associated with the content.
- Non-existent or inadequate back-end systems to transform electronic files into computer readable indexes. For example, image-only PDF or TIFF files that are non-searchable offer only limited value for the purpose of data analytics. If IT departments choose not to enforce indexing by the users for whatever reason then a back-end process must be in place to help achieve indexing of this content.
Exploiting Big Data with Indexes (Simple and Obvious)
Now that we’ve defined some of the general technological factors causing this explosion of “Big Data”, and we’ve explained some of the human nature factors contributing to the challenges of managing “Big Data”, let’s start by offering a simple, yet extremely important- way to start gaining control of sets of data. It might be obvious to some and over simplified to others, but Exploiting Big Data starts by capturing Indexes.
- Exploit: To ‘take advantage of’ to its full potential
- Big Data: The volume, variety and velocity of data
- Indexes: Computer understanding of content
There are many effective ways to capture Indexes. A common automated method to index the content itself is to make a Searchable PDF file that most of us are familiar with. Another way to automatically index is via Data Capture technology where only selective fields such as an invoice number and vendor name is extracted from an invoice instead of the full-page text. Data Capture is particularly useful for providing ‘relevant index’ versus all index values. An example is if an organization processes contracts. In this case there is no need to index all the terms and conditions of the contract agreement. Only the ‘relevant index’ values such as the parties involved in the agreement, the date and maybe a few other pertinent pieces of information. Also, new methods to offer simplified automation of indexing can be to utilize touch-screen devices for indexing fields from images. Touch indexing makes the user experience much more enjoyable and therefore encourages indexing by persons most familiar with the content.
A well-designed solution can perform indexing at the time content is introduced to the system, or after the fact on the server or a combination of both.
The proper architecture of an effective solution to Exploit Big Data with Indexes will depend on individual organizations requirements and needs. One of the most important things to know is now, more than ever, there are many ways to achieve a highly efficient system while also delivering these capabilities at an affordable cost. The benefits of Exploiting Big Data is tremendous for organizations in many different industries.
However, to truly realize these benefits a solution must consist of techniques and methods for machines to make sense of electronic content. And making sense of electronic content starts with creating Indexes to Exploit Big Data.
It looks as if ‘Big Blue’ (a.k.a. IBM) continues to make ‘Big Moves’ in big markets such as cloud computing and big data. The Companies’ fourth-quarter results were better than expected and Wall Street appears to like the direction and evolution of the Company.
Full article here:
The Company attributes the good financial results to their software businesses. “IBM beat Wall Street’s estimates in its fourth-quarter results, released after market close, boosted by strength in its software business.”
This transition from a focus on hardware, mainframes and computers has not simply happened overnight. Neither was it was not happenstance. The slow evolution to focus on software and services is still in its infancy, in my humble opinion, however the smart people at IBM are much further along than many other companies that are struggling to evolve their businesses. Take some of the most successful hardware companies of the past such as Hewlett-Packard and Dell in comparison, for example, and notice how they have fallen out-of-favor with customers and the investment community in general. HP is still debating whether or not to divest their personal computer business or not. Whereas, Dell is clearly trying to make the transition to higher margin software and services but their direction is not clear.
“We achieved record profit, earnings per share and free cash flow in 2012,” said IBM CEO Ginni Rometty in a statement released after market close. “Our performance in the fourth quarter and for the full year was driven by our strategic growth initiatives — growth markets, analytics, cloud computing, Smarter Planet solutions — which support our continued shift to higher-value businesses.”
While this seems promising for IBM in respect to their short-term financial strength it seems as though they are not being passive about their growth plans. They are aggressively going after high-growth, high-margin emerging market segments (including Big Data) and have been quite local about this as an important objective.
“Looking ahead, we continue to invest to deliver innovations for the enterprise in key areas such as big data, mobile solutions, social business and security, while expanding into new markets and reaching new clients,” added Rometty.
I find it humorous to hear all this buzz about big data, mobile and social. In particular I love when the nay-sayers discount these emerging markets as ‘hype’, or ‘a fad’ or ‘cannot monetize’. In fact, this incredible doubt by many people that these are viable markets for successful business was the whole purpose of creating BigDataIsAFad.com, CloudIsAFad.com and SaaSIsAFad.com.
If the likes of IBM, Google, Apple, HP, Amazon, Salesforce and others are investing billions and billions of dollars on these types of solutions, I have to think there is some legitimacy to these markets. Afterall, these companies all have enjoyed tremendous success at one time or the other.