#infrastructure analytics

Posted by Manu Lavanya, Managing Director at Brillio

Over the past year or two, we have read story after story about how big data and business analytics spending is increasing. We have also seen how a significant amount of an enterprise’s spending in information technology is migrating to the CMO side of the house. The emerging world of Infrastructure Analytics is a completely contrary story to this, with the spend decision and business value sitting squarely in the world of the CIO.

Let’s talk about why.

The Cloud and IoT — Bringing Both Flexibility and Complexity to Infrastructure

The promise of the Cloud has been centered around the idea of liberating IT infrastructure from old legacy premise-based models so that there can be an evolution to a more fluid and on-demand model for allocating and consuming IT services.

The Cloud is becoming the way for businesses to consume infrastructure. Yet, along with the Cloud’s promise of freedom and flexibility has come significant additional complexity. Companies struggle to understand not only what should be public, private or hybrid, but which parts of their infrastructure to shift on the fly. With a simultaneous dependence on services from so many major players (including Amazon to Microsoft to Cisco), operating in a smart and efficient manner in this new hyper-hybrid IT environment is no easy task. Fluid, real-time interoperability within infrastructure remains a vision or dream to most CIOs.

Today, companies are facing technology complexities — beyond the Cloud. More of the devices in the enterprise are becoming IP addressable. The Industrial Internet of Things (IIoT) is showing us that it’s no longer just servers, but devices with sensors, that are creating data that needs to be captured, stored and analyzed. This proliferation of IoT has also impacted the infrastructure world, where more and more assets are creating data that lends itself to be analyzed and acted upon.

This complex and expensive environment of infrastructure has, to date, lacked effective tools to assist CIOs and their teams in the prediction of or intervention in common problems. These problems include unplanned consumption, predictive capacity planning, or dealing with emergency situations.

That is all about to change as we enter the new world of Infrastructure Analytics.

What if you, the CIO, could look at your complex network in a truly holistic manner in real-time?

What if you could discover the causes of various infrastructure events and then leverage that intelligence in real-time to optimize and increase the quality and reliability (and yes, also decrease the cost) of your infrastructure?

What if you could actually intervene in outcomes before they happen by creating and controlling a highly resilient infrastructure network that acted like a living, learning organism?

What a CIO Needs to Know to Proactively Impact Network Infrastructure

Soon enough, it will be the case that it is no longer good enough to know why something happened in a company’s infrastructure. The requirement will become the ability to predict when something might happen again, under what conditions, and identify the levers of control and intervention. For businesses that are cyclical, there will be a need to forecast robustness of infrastructure needs BEFORE the business event happens.

What does a CIO need in order to move into this new reality?

It’s about Infrastructure Analytics implemented to give the CIO’s organization the ability to:

• Monitor effectiveness and efficiencies, while making resource changes in real-time, • Develop insights into the triggers of cause and effect events, • Build predictability and simulation into IT decisions, before they are made, • Create resilience in infrastructure.

1. Monitor effectiveness and efficiencies, while making resource changes in real-time. Infrastructure Analytics will add intelligence to the network, enabling the “agile sourcing” of Cloud resources. For apps that are built as natively interoperable, workloads will be shifted — on the fly and as needed — across multiple vendors while maintaining quality of service. The IT department will be able to understand the best use of every “compute dollar” that they spend.

2. Develop insights into the triggers of cause and effect events. Infrastructure Analytics will enable specific causes to be assigned to variability in infrastructure behavior. Analytics will enable action. For example, when a specific network threshold is crossed, what is the result and what happens elsewhere in your infrastructure?

3. Build predictability and simulation into IT decisions. Infrastructure Analytics will enable prediction and simulation to be used to evaluate IT related purchases or financial decisions even before they are made. Infrastructure performance and problems can be simulated and viewed under different conditions.

4. Create resilience in infrastructure. One of the essential proactive questions that a CIO can ask is: “What drives resilience in my network?” The critical answers that Infrastructure Analytics can give will be centered around:

• Predictability of failure — When will it fail? • Improvement in performance — How can I make it fail less? • Responsiveness — How can I act in the midst of failure? • Intervention — How can I change elements to avoid failure altogether in the future?

Three Components Combine to Make Infrastructure Analytics Possible and Actionable

If CIOs and their organizations are going to be able to impact network infrastructure performance in the ways just described, three components need to exist to create actionable Infrastructure Analytics.

1. An understanding of the discrete infrastructure elements themselves and the data they generate. This is about the ability to deconstruct devices, data and their interaction in the Cloud in a multi provision environment. Extracting the right data is what will determine the sort of intelligence you can get from and use with your infrastructure.

2. Analytic models using Predictive and Causal Sciences. It takes more than raw manpower and linear models to get to the intelligence and insights that Infrastructure Analytics can provide. The emerging field of predictive analytics married with causal modeling will build the complex machine learning platforms that drive Infrastructure Analytics.

3. Data visualization and decision-report dashboards. Information and intelligence may exist, but in the world of Infrastructure Analytics it is usually so complex that if it is not presented in a visual dashboard format, it will rarely be real-time actionable.

The Resilient Network

There are some important decisions to be made by CIOs if the management of infrastructure is to be moved from a reactive to a proactive position. The evolution of the Cloud as the dominant way in which businesses consume infrastructure, and the proliferation of IP addressable devices via IoT in the enterprise — means even more data and greater complexity.

The new field of Infrastructure Analytics can help you cut through this complexity and grow from an organization focused on network repair to infrastructure intervention. As a CIO, you can lead the discovery of the causes of various infrastructure events and leverage that intelligence in real-time to optimize quality and reliability of your network.

Won’t it be amazing when you can actually intervene in outcomes — before they happen — by using Infrastructure Analytics to transform your network into a living, learning organism?