Last Friday I attended SDForum’s The
Analytics Revolution Conference. The
presenters were startup public and private company executives and
investors. The presentations were mostly
around real-time analytics. For the past few years Gartner analysts have
been writing
and talking about the Real Time
Enterprise. Gartner sees the
analysis of the data that is generated by various enterprise applications such
as an ERP and residing behind the corporate firewall and an important
ingredient to achieving the Real Time Enterprise. However, Friday’s presentations brought to
focus that the impetus for real-time analytics is not the faster analysis of enterprise
data, but of the Big Data captured
on the Web (structured and unstructured social data, activity logs, data coming
from mobile applications, geolocation data, data mashups, the interactions
during the lifecycle of the captured data, etc). This data is orders of magnitude larger and
more complex than the data currently stored in the enterprise data
warehouses. I had written
that Internet SaaS applications will drive the SaaS innovation agenda. Friday’s presentations provided more examples
and additional justification for this conviction.
Companies like Facebook, eBay, LinkedIn and Zynga that presented at the conference are interested in making analytics-driven decisions in Internet speed and cheaper than it is currently possible with the existing data warehousing and analytics technologies. They want to use their data to continuously optimize their businesses, simulate the effect of decisions on business processes, and forecast the impact of actions. Making decisions based on reports that summarize and find trends in data that is a week or a month old simply won’t do.
To take advantage of emerging opportunity of real-time analytics of big data, we are considering investments in the following three areas:
- Infrastructures of next-generation data management systems, including open source systems like Hadoop and derivatives, and systems for creating OLAP cubes in real time.
- Horizontal and vertical analytic applications, examples of which are listed here. Additionally, these applications will need to find a way to monetize on the data they capture, not only on the data they process. See, for example, how internet data exchanges like BlueKai are able to monetize the cookie data they capture.
- Analytic services offered around the captured big data, e.g., the services offered around big mobile data by companies like Ground Truth, and Flurry.
My underlying hypothesis (driving one of my investment
theses, and shared by several of the investors present at the conference) is
that cloud computing will make big data
analysis faster and cheaper.
I am convinced that over the next few years, investments in each of these three areas will result in several strong exits. I don’t believe that the established on-premise data warehousing, BI and analytics vendors will be able to create adequate technological and business model innovations around real-time analytics. The results of a survey conducted by InformationWeek and published on March 27, 2010 make it clear that more enterprises are looking to cloud computing to address the perceived shortcomings of on-premise applications, with SaaS BI being one of the top areas of interest. Finally, as stated here, incumbent on-premise software vendors will continue to face the innovator’s dilemma that won’t allow them to move fast to offer the novel solutions for real-time analytics that the market needs and is starting to demand.
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