We recently released our benchmark research on big data analytics, and it sheds light on many of the most important discussions occurring in business technology today. The study’s structure was based on the big data analytics framework that I laid out last year as well as the framework that my colleague Mark Smith put forth on the four types of discovery technology available. These frameworks view big data and analytics as part of a major change that includes a movement from designed data to organic data, the bringing together of analytics and data in a single system, and a corresponding move away from the technology-oriented three Vs of big data to the business-oriented three Ws of data. Our big data analytics research confirms these trends but also reveals some important subtleties and new findings with respect to this important emerging market. I want to share three of the most interesting and even surprising results and their implications for the big data analytics market.
First, we note that communication and knowledge sharing is a primary benefit of big data analytics initiatives, but it is a latent one. Among organizations planning to deploy big data analytics, the benefits most often anticipated are faster response to opportunities and threats (57%), improving efficiency (57%), improving the customer experience (48%) and gaining competitive advantage (43%). However, once a big data analytics system has moved into production, the benefits most often mentioned as achieved are better communication and knowledge sharing (51%), gaining competitive advantage (51%), improved efficiency in business processes (49%) and improved customer experience and satisfaction (46%). (The chart shows rankings of first choices as most important.) Although the last three of these benefits are predictable, it’s noteworthy that the benefit of communication and knowledge sharing, while not a priority before deployment, becomes one of the two most often cited later.
As for the implications, in our view, one reason why communication and knowledge sharing are more often seen as a key benefit after deployment rather than before is that agreement on big data analytics terminology is often lacking within organizations. Participants from fewer than half (44%) of organizations said that the people making business technology decisions mostly agree or completely agree on the meaning of big data analytics, while the same number said there are many different opinions about its meaning. To address this particular challenge, companies should pay more attention to setting up internal communication structures prior to the launch of a big data analytics project, and we expect collaborative technologies to play a larger role in these initiatives going forward.
A second finding of our research is that integration of distributed data is the most important enabler of big data analytics. Asked the meaning of big data analytics in terms of capabilities, the largest percentage (76%) of participants said it involves analyzing data from all sources rather than just one, while for 55 percent it means analyzing all of the data rather than just a sample of it. (We allowed multiple responses.) More than half (56%) told us they view big data as finding patterns in large and diverse data sets in Hadoop, which indicates the continuing influence of this original big data technology. A second tier of percentages emphasizes timeliness as an aspect of big data: doing real-time processing on streams of data (44%), visualizing large structured data sets in seconds (40%) and doing real-time scoring against a database record (36%).
The implications here are that the primary characteristic of big data analytics technology is the ability to analyze data from many data sources. This shows that companies today are focused on bringing together multiple information sources and secondarily being able to process all data rather than just a sample, as well as being able to do machine learning on especially large data sets. Fast processing and the ability to analyze streams of data are relegated to third position in these priorities. That suggests that the so-called three Vs of big data are confusing the discussion by prioritizing volume, velocity and variety all at once. For companies engaged in big data analytics today, sourcing and integration of various data sources in an expedient manner is the top priority, followed by the ideas of size and then speed of arrival of data.
Third, we found that usage is not relegated to particular industries, certain types of companies or certain functional areas. From among 25 uses for big data analytics those that participants are personally involved with, three of the four most often mentioned involve customers and sales: enabling cross-selling and up-selling (38%), understanding the customer better (32%) and optimizing pricing (28%). Meanwhile, optimizing IT operations ranked fifth (24%) though it was most often chosen by those in IT roles (76%). What is particularly fascinating, however, is that 17 of the 25 use cases were named by more than 10 percent, which indicates many uses for big data analytics.
The primary implication of this finding is that big data analytics is not following the famous technology adoption curves outlined in books such as Geoffrey Moore’s seminal work, “Crossing the Chasm.” That is, companies are not following a narrowly defined path that solves only one particular problem. Instead, they are creatively deploying technological innovations en route to a diverse set of outcomes. And this is occurring across organizational functions and industries, including conservative ones, which conflicts with conventional wisdom. For this reason, companies are more often looking across industries and functional disciplines as part of their due diligence on big data analytics to come up with unique applications that may yield competitive advantage or organizational efficiencies.
In summary, it has been difficult for companies to define what big data analytics actually means and how to prioritize their investments accordingly. Research such as ours can help organizations address this issue. While the above discussion outlines a few of the interesting findings of this research, it also yields many more insights, related to aspects as diverse as big data in the cloud, sandbox environments, embedded predictive analytics, the most important data sources in use, and the challenges of choosing an architecture and deploying big data analytic products. For a copy of the executive summary download it directly from the Ventana Research community.