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Interpret the Future with Analytics and Machine Learning


What’s the average click-through rate of our email campaigns? How did our company website perform this month?

These questions show how familiar we have become with analytics in our lives. Algorithms, sensors and smarter data analysis enable us all to monitor our health, assess personal productivity and measure professional performance.

Companies such as Amazon have long been analyzing our online and offline behavior, improving our shopping experiences and better understanding their sales.

In the workplace, business intelligence and analytics applications are transforming every department.

Marketing can prove its impact on revenue. The CFO is forecasting further into the future and with greater accuracy. IT is becoming increasingly strategic with project delivery and management.

The language of analytics is also influencing how we speak. Dashboards, metrics and trend analysis have entered corporate boardrooms. We talk in KPIs and tangible outcomes.

Shifting Sectors

The workhorses behind analytics applications are data centers. Some consist of vast data halls, others are no larger than an air-conditioned room controlled by IT.

It is this range in scale that makes the topic so fascinating. Hyper-scale facilities consume the same amount of energy as a small town. Extensive pressure is being put on the world’s water supplies by data centers. In the US alone, data center power usage in 2014 was the equivalent of 6.4 million average homes. This level of consumption puts data centers under the microscope to ensure compliance with an organization’s CSR promises.

Facilities are also incredibly complex to manage. The largest can cost billions of dollars to build. A simple mistake can damage a company’s bottom line far into the future. Despite these risks, many data centers are still managed with rudimentary data techniques and, in most cases, spreadsheets. This over-reliance on unreliable metrics and manual monitoring and managing means organizations struggle to accurately measure how facilities perform financially and operationally. In this era of rapid business results, that’s hardly evidence of strong corporate governance.

Analytics Delivers the Benefits

When such large sums of capital investment are at risk, why aren’t businesses changing faster?

Most of the time it is because the data center is too low on the corporate agenda. This mindset needs to change. The data center is a corporate asset that should be assessed on equal terms with other business projects.

Analytics can deliver with this objective and not every business is standing still. Some executive teams have recognized how analytics improves the forecasting, construction and management of facilities. These analytics are also beneficial to companies investing in the data center industry that previously didn’t have any involvement – for example, land purchasing organizations and investment funds.

Predictive modeling software is validating prospective designs. Those responsible for site selection, project management and IT sourcing can identify, before construction even starts, where the most economically viable location would be, what equipment to purchase and even the best type of facility for the business – hybrid, cloud or colocation.

Once built, real-time analytics tools enable finance professionals to accurately track the total cost of ownership of a facility. With a smart analytics solution, this figure is no longer an approximate or static number, but a quantifiable cost that adapts to facility changes, to fluctuations in the cost of water and energy, and to other internal and external factors. Predictions can be made for the entire lifecycle of the facility.

If the business provides outsourced data center or cloud services, the CFO can analyze customer margins to an impressive degree – e.g. which customers are generating the most revenue, are certain IT halls using too much power against contracted amounts? Is the facility performing to our CSR targets? This data filters through to operational teams who can allocate capacity to maximize available resources.

Learning from the Machines

The progress doesn’t stop there. Recent advancements in Machine Learning have made the business outcomes even more attainable.

Applications are fed pre-cleansed and calibrated data to recognize and learn patterns. The result is a new wave of analytics solutions that can suggest causes and recommended actions from previously learned results, enabling automated decision making.

Look at some use-cases. Google reduced its data center energy bills by 40% using DeepMind, its Artificial Intelligence (AI) platform. Companies are using similar solutions to reduce how much water their facilities consume - a massive boost to CSR and transparency. Engineers are even assessing how different physical layouts will impact the efficiency of a data center before spending a single dollar. Businesses face an important choice. Be part of the problem or become known for leadership in analytics, business management and environmental sustainability.

Everyone can help - find out who within your organization is responsible for the data center that underpins analytics applications. Ask them – are they using analytics themselves? After all, this could be your chance to recommend a solution that can generate impressive financial savings, and the career enhancement that accompanies those results.

(About the author: Zahl Limbuwala is chief executive offficer at Romonet)

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