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What is “Big Data”? Well, every day we create close to 3 exabytes of data. What is an Exabyte, you ask? It is one quintillion bytes, or a billion billion bytes (which equals 1000 petabytes, 1 million terabytes or 1 billion gigabytes!). In other words “Big”! According to IBM, 90% of today’s existing data has been created in the last two years due to the proliferation of social media posts, digital pictures and videos sent online, transaction records of online purchases, cell phone GPS signals and sensors as the Internet of Things gains traction. It is unlikely this trend will be reversed anytime soon.

Big data is characterised by data sets so large and complex it can’t be managed by traditional databases. By some measures, it is expanding at 45% per annum. Technology companies large and small are clamouring to provide products for their clients to make sense of the data deluge. According to IDC over 90% of “machine” generated big data is unstructured and therefore almost impossible, if not prohibitively costly, to use traditional relational databases that historically have been supplied by tech stalwarts IBM, Oracle, and Microsoft.

Big data is also characterised by the “3Vs”. That is the extreme volume of data, the wide variety of data, and the velocity at which the data must be processed. Moving beyond traditional databases, the industry is experimenting with new approaches to storing and analysing big data. For example, raw data and metadata are aggregated and through machine learning and artificial intelligence programs, complex algorithms search for repeatable patterns.

In theory, companies that embrace big data analytics should be more competitive in the marketplace, have a better understanding of their underlying business processes, be able to better access and understand stored digital information, and be more efficiently able to tailor new products and services to customers. It’s no wonder that big data analytics (along with Cybersecurity) is garnering a larger piece of the non-discretionary IT spending pie.

Investing in Big Data Analytics

The growth potential of Big Data analytics has attracted many software vendors of all sizes, from small start-ups to well-known tech giants. Players in the Big Data analytics industry fall into four primary categories:

  • Tech giants like IBM, HP, and Microsoft that offer software services and Big Data capabilities.
  • Large enterprise software vendors like CA Inc., BMC Software, SAP, and Oracle.
  • Pure play software vendors specialized in Big Data analytics, such as Splunk.
  • Business Intelligence providers such as Tableau and QLIK.
  • Relative to my investment style, I tend to prefer the smaller high growth, pure plays like Splunk (SPLK-US).


U.S. based company, Splunk is a data analytics “pure play” that offers software products for searching, monitoring, and analysing machine generated big data. (The rather curious name is derived from “Spelunking” an obscure sport whereby participants explore caves.)

Splunk’s value proposition is to make machine data accessible, usable and valuable to everyone in the enterprise. Machine data is one of the fastest growing and most pervasive segments of "big data"—generated by websites, applications, servers, networks, mobile devices and all the sensors and RFID assets that produce data every second of every day. By monitoring and analysing everything from customer clickstreams and transactions to network activity and call records, its software sifts through these largely unstructured digital data sources and provides a management tool to get a sense of transaction activity, system performance, security threats and fraudulent activity.

Traditional databases can’t deal with the size or complexity of this data nor can they usually provide analysis in real time. One of Splunk’s particular strengths is that they monitor every data source in the enterprise-IT systems, sensors, mobile devices, security systems, even air conditioning systems and elevators. In other words, Splunk provides the infrastructure that allows customers to collect, index, store, analyse, and monitor data in real time and perform “operational intelligence”, as the company calls it.

The key to understanding Splunk is to realize that it is a platform upon which a number of IT and analytical applications can be built and not just a single product. A number of early customers are discovering that by analysing machine data, they are receiving valuable business intelligence as well as how their systems are performing.

The addressable market for Splunk is vast and the company’s penetration is still small, hence the opportunity. Analysts estimate Splunk's core “Total Adressable Market” (TAM) to be in excess of USD 10 billion with the ability to triple that if it continues to disrupt and penetrate the security, IT operations markets, and business analytics spaces. There is also an opportunity for Splunk to become a larger part of the Internet of Things (IoT).

Splunk’s business model is unique. Most application and infrastructure software companies charge by the person (seat) or per processor. Splunk’s pricing is based on the amount of data used, which provides a growth engine for the company going forward and also allows smaller companies to use the service while scaling up. Essentially, Splunk sells a perpetual license and associated maintenance fees (about 20% of the license fee). These licenses will remain the largest source of revenue for the company.

Analysts believe that Splunk is quickly gaining market acceptance across many software industries beyond pure analytics (security, ITOM, business analytics) with current customers expanding their licenses and the company on-boarding new customers at a rapid pace. It also appears that customers are beginning to use Splunk mainly as a security solution, with the platform able to analyse, correlate, and issue real-time reports across the IT environment, irrespective of source.

Historically, Splunk has focused on the US market (75% of revenues in 2015) but it is in the early stages of expanding internationally, as well as shifting toward more channel-driven sales.

Splunk has a market capitalization of USD 7.8 billion and over the past 5 years (it has been a public company since April 2012) it has grown revenues 59%. It has only recently become profitable (not unusual in companies in a hyper growth phase) earning 0.09c / share for FY2015. Consensus estimates show Splunk’s revenue growth is expected to be in excess of 30% p.a. to 2019 but (more importantly) with earnings growing 67% over the next three years.


  • Large IT vendors such as IBM, Microsoft, Computer Associates, and Oracle have flagged “Big Data” as a growth area. Whether they try and replicate Splunk’s functionality in the machine logging space (which is patent protected) or take another approach is open to question.
  • Splunk’s consumption based pricing model could come under pressure as data volumes grow in excess of 40-50% per annum and customers become more price sensitive possibly inviting some competitive threats of discounts.
  • Splunk’s products are based on a single technology which may or may not evolve into multiple platforms.
  • Inability to significantly penetrate the security (SIEM), ITOM, and business analytics markets will impact future growth and profitability.

Important: This content has been prepared without taking account of the objectives, financial situation or needs of any particular individual. It does not constitute formal advice. Consider the appropriateness of the information in regards to your circumstances.

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