One Is Easy; a Trillion Is Rather More Difficult

Not all data is equal. Event data has particular qualities that differentiate it. Specifically, the concepts of chronology and causality make it especially interesting for analysis. The challenge is that in order to definitively answer the question, “Why did X happen?” you will need to look at all the circumstantial data leading up to the event. For simple systems or a small  number of actions, this is relatively straightforward, but for complex environments with billions or trillions of data points, quickly getting to a detailed understanding of occurrences becomes quite daunting. A few basic principles ecome important, including:

• Examining all available data—not a sample or subset—to avoid skewed results or missed nuances.
• Developing behavioral models on the fly to answer ad hoc, iterative questions.
• Bringing the capabilities to less-­-technical decision makers, fast enough and easy enough to satisfy them.

ESG research found that many businesses now report leveraging a very wide variety of types of event data in their business intelligence (BI) and analytics efforts. ¹This multi-­-sourced information is combined to provide the full picture of a given situation.

Figure 1. Data Sources Currently Used or Planned for Use in BI and Analytics

Source:  Enterprise  Strategy  Group,  2015

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Interana has focused development on building an analytics solution specifically for event data, which has three properties, an entity, attributes, and a time-­-stamp. This approach has a few advantages over   more generic system, including:

• Vertical integration – By combining the data management and the analytics software with the user interface, Interana simplifies deployment and administration, but more importantly, eliminates many of the bottlenecks and ETL inefficiencies that come from stitching together different layers from multiple vendors.
• Horizontal integration – Gathering data from the many different sources available (see illustration) is a good first step. Interana takes the next step, too, by analyzing all data—not a subset or a sample—to ensure a true view of the environment and actions, not just a statistically valid representation.
• Speed and scale – Some databases and analytics run in-­-memory for speed, but they can’t scale. Others leverage low cost storage, but are slow. Interana offers the best of both options, plus tuned in-­-processor” level analytics to get even more performance on massive volumes of “raw” data.
• User friendliness – Often, analytics exercises are delayed significantly because only highly skilled IT staff, data scientists, or business analysts can identify, collect, model, and interrogate the data. Interana has created an intuitive workspace so anyone can ask questions of the data, get an immediate answer, and ask follow-­-on questions. Bringing the intelligence to the domain expert is critical to reacting to events.

Not least, all of these capabilities can be deployed in a data center or in a public cloud. This flexibility allows businesses to conduct their analytics where it makes the most sense: at the data’s point of origin. This advantage further reduces the inefficiencies of data migration and ETL.

As organizations explore primitives for grouping their customers, defining paths of engagement, tracking flow, and measuring outcomes, Interana offers ready-­-made models to simplify the data science and accelerate the time to insight.

The Bigger Truth

Interana’s solution is optimized and ready for common types of analytics around event data, answering crucial business questions such as: What happened in the past? What is happening now? What will happen next? Why?

Behavioral analytics are especially useful in finding patterns of activity and consequences, which is critical for understanding both customers and products. While other analytics solutions can be adapted to address the complexity of these types of analytics, it would require significantly more effort and advanced skills to build a complete solution from . End-­-users of business intelligence and analytics just want to get to the answer, make a decision, and move on to the next strategic activity.

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Understanding “Event” in Event Data 

How to get Insights on Customer Behavior 

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¹ Source: ESG Research Report, Enterprise Big Data, Business Intelligence, and Analytics Trends, January 2015.
This ESG Solution Showcase was commissioned by Interana and is distributed under license from ESG.
© 2015 by The Enterprise Strategy Group, Inc. All Rights Reserved.