The power of machine learning and artificial intelligence in the data centre

The power of machine learning and artificial intelligence in the data centre
Richard’s career in sales and marketing includes roles in enterprise systems management to Internet of Things (IoT) enabled smart footwear. On leaving the Royal Navy in 1990, Richard worked for UK-based IT resellers developing channels across EMEA, building and selling a Y2K IT contractor firm, and providing IT consulting services to London-based banks. Richard has held management roles with Tivoli/IBM and Crystal Decisions (later Business Objects) and senior executive positions with Corporate Radar, Kyoto Planet, Plantiga and RF Code. In 2004, Richard launched his own company, Performedia International, which published strategy books for the IT industry, provided video marketing solutions and established global sales and marketing divisions for growing technology companies.

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Data is everywhere – masses of it. And it’s helping businesses to make better decisions across departments. Marketing can utilise data to discover the effectiveness of email campaigns, finance can analyse past trends to make predictions and projections for the future, and sales can target their follow-up with detailed information on prospective customers.

But data is only useful when business tools transform it into valuable information. Data intelligence through algorithms and analytics make business data relatable. The most advanced solutions require enormous amounts of data to be able to offer accurate insight to users. As a result, many solutions are cloud based, as most businesses do not have the IT capacity or budget to store this amount of information.

So where does all this data reside? The data centre.

Powering the analytics movement

The data centres that power business applications vary from tiny rooms in office buildings to enormous, purpose-built data halls with their own systems, controlled climates and land-use regulations.

Unsurprisingly, hyper-scale facilities require hyper-scale amounts of energy and water to power them, which is adding to the pressure already facing the world’s natural resources. For example – data centres account for 3% of the global electricity supply and this will likely grow with the increase in data.

The cost of building the very largest facilities can reach billions of dollars, so any mistake, no matter how small, is damaging to a company’s bottom line. Accuracy is crucial in the planning stage to ensure the best return on investment yet many data centres are managed with basic data techniques. And it’s surprisingly common for spreadsheets to be used!

It seems slightly ironic that the data centres providing such advanced data analytics tools are built and run on such basic, unintelligent software.

Using analytics for good

Something has to change. The data centre is business-critical after all, and the more it is viewed as an important strategic asset the better. Interestingly the answer lies in analytics itself.

Business intelligence applications are proving crucial for improving data centre accountability and as a way of delivering tangible benefits across the entire organisation.

The visibility gained from real-time analytics enables executive teams – and the shareholders who hold them to account – to improve the forecasting, construction and management of facilities and to ensure funds are being invested wisely.

This insight is most beneficial during the planning stages. Predictive modelling software assists with site selection, project management and IT purchasing by validating designs and identifying the most economical options. All of this can be decided before a facility is even built, ensuring the best possible chance for success.

These analytics can also be used to calculate and measure natural resource use, including water and fossil fuel consumption. The data centre has a major impact on company CSR policies. Investors are likely to look elsewhere if a company is committed to unsustainable business practices or unable to provide detailed evidence of efforts to improve efficiency and environmental impact.

From a practical management perspective, analytics grants businesses the ability to compare the effects of using different energy and water technologies. This can be analysed over the entire lifespan of a data centre to determine the best choice for the facility and then once built, data can be provided on water and energy use to adhere to company CSR policies.

Turn to the machines

For businesses further along in their analytics journey, there is Artificial Intelligence (AI), Machine Learning and Predictive Analytics. Many cloud-based software applications can and do benefit from these advancements to provide richer data to users. This technology can and should be used for the data centre as well.

Recently Google’s DeepMind AI software showed the financial outcomes from using its own proprietary AI to reduce data centre energy usage. It is important, however, to not solely depend on these emerging technologies as they cannot predict every outcome, especially in the case of human intervention.

Many are still evolving, but even at this early stage utilising these technologies to ensure availability of the facilities that power applications is becoming crucial for data-dependent businesses. There is so much intelligence to be gained across the business, for customers and the business itself, and the data centre is the key to storing and utilising this data. If you’ve come to expect insight from every other department, why should the data centre be any different? 

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