As more and more companies find software at the core of their businesses, the need to provide always-on, scalable infrastructure becomes critical.  With the “consumerization of IT,” customer expectations have soared as has pressure from executives determined to digitally transform their businesses.

While technology has advanced and demands have soared, the tools and techniques used to manage IT operations have quickly become outdated, resulting in increased business risk and exposure to brand damage.  The good news is that there is now an opportunity to elevate IT operations to new performance levels by leveraging powerful analytics capabilities — essentially Analytics 3.0 — that bridge the worlds of analysis and action.

Analytics Evolution

Two years ago Thomas H. Davenport prognosticated Analytics 3.0 in his article in Harvard Business Review, and in it he carefully dissects the lifespan of analytics to this point.

In the beginning there was Analytics 1.0, which reported what happened using data from internal transactional systems (CRM, ERP, etc.).  This initial era was defined by new “business intelligence” companies like Cognos, Business Objects and Microstrategy that enabled customers to build data warehouses and run analyses of past performance.

Then, as online and social media companies began processing massive volumes of unstructured data, and grew quickly, analytics got big.  Analytics 2.0 combined traditional transactional data with “big data” from online clickstreams, social media, and other sources to provide insights on past performance – also known as descriptive analytics – via more sophisticated reporting and visualizations.  Data scientists were enlisted to apply their analytical and computational skills to deal with the scale and fast-moving nature of “big data”.

Now, with Analytics 3.0, analytics finally gets smart.  As the world continues to instrument itself with the growing internet of things, data volumes are increasing at an unprecedented rate.  It is widely reported the amount of data globally is now doubling every two years.  But more than the extraordinary volume, the new 3.0 era is defined by the application of algorithms to these data streams to recommend, or prescribe, well-informed actions.

Intelligent algorithms fuel recommendations that help run always-on businesses in the Application Economy.  With greater volumes of data coming from multiple sources, we can effectively eliminate the outliers and triangulate to gain a better understanding of reality, both current and the immediate future.  Data Scientists play a more prominent role – first to decipher and understand the data, and then to model actions based on confidence levels.

Harnessing Analytics 3.0 for IT Operations

By combining the unstructured data, time series data, and relational stores from “big data” analytics, with high-value processing capabilities like pattern recognition and anomaly detection and advanced visualizations, IT can partner with line-of-business executives to drive real business value.

There is no better place to begin the Analytics 3.0 journey than with IT operations for the reasons stated above.  With Analytics 3.0, IT operations can:

• Detect usage, behavior patterns and problems before they occur
• Predict capacity or resource needs of planned or unplanned changes
• Prescribe recommendations to support operational and business decisions

By identifying patterns like Black Friday shopping habits, resources are better aligned with business needs – effectively creating an automated, just-in-time application infrastructure. With the right analytics tools, IT departments can now glean business insights that ensure high-quality and rewarding customer experiences.

New Analytics 3.0 Solutions    

To meet this potential, Analytics 3.0 requires an advanced technology stack that enables pattern recognition and anomaly detection to be applied to real- and near real-time data.  Algorithms and machine learning now provide not only a real-time view of the IT infrastructure, but also recommend actions based on current data and predictive models.  This could include unplanned events, like hacking counter-measures, or planned ones like the re-allocation of resources based on a traffic spike from a Super Bowl advertising campaign.

Attempting to apply incremental improvements to existing analytics solutions to achieve these benefits will result in a muddied set of objectives and disappointment – success depends on a solution designed with “always on” business outcomes as the foundation.  However, by capitalizing on the network effect of data and linking analysis to action, Analytics 3.0 exposes the art of the possible in an increasingly hyperconnected world.  And as adoption of Analytics 3.0 increases and companies learn how to integrate the intelligence from this data into their overall application architectures, and into their products and services, we will all benefit in the form of better customer experiences.

IT Operations Gets Smart by Putting Data to Work

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