Is data accurate enough for high impact decisions?

Analytics tools are being increasingly used to help organisations make crucial strategic decisions. The major challenge for organisations using analytic tools stems from the incorrect assumption that the source data is of an acceptable quality.

But, unfortunately, this is very rarely the case, especially when data is sourced from built-in metering and sensors. If data is of a poor quality and cannot be validated and labeled accurately, no amount of analysis will be able to provide reliable, high confidence, actionable results.

>See also: Why data suppression is key to GDPR compliance

Data is being used across the business to improve processes and performance, and analytics projects are being deployed to gain a deeper understanding and shape business decisions, but the outcomes will only be of use if companies can be confident that the source data is accurate.

Proving the facility’s worth

Ironically, given that the data centre is considered critical to most organisations and holds their most important information, it is quite often the last on the list to benefit from scalable analytics tools.

So while the data centre requires large investments and is managing vast amounts of information, enabling businesses to provide cutting-edge services, its own physical environment is more often than not being managed only with raw data of questionable quality derived from uncalibrated meters and sensors.

Regardless of whether the facility is owner-operated, cloud or colocation, it is still required to prove its financial predictability, operational stability and performance, and increasingly, its environmental sustainability.

>See also: Businesses putting customer data at risk

For CEOs and CIOs, access to accurate analytics allows them to closely manage the performance and demonstrate the ROI of these strategic assets at board level. But without the capability to validate data against a dynamic baseline, there will always be a question mark hanging over the accuracy of the output of any analytics tool.

Machine learning

In a data centre, best practice and regulations regarding the design, build and management of cleaner lower impact facilities mean that energy and water are constantly monitored, and any inefficiencies need to be spotted and addressed immediately. At the same time, pressure is applied from a financial perspective to ensure that service quality is maximised while expenditure is kept to a minimum.

Sensors and data collection networks collecting equipment and environmental performance information are never 100% accurate.In fact, on average, only 60-65% of data is accurate. Therefore, dynamic validation of raw data from these devices is crucial to a successful deployment of any data centre analytics platform.

And this situation is set to get more complex as businesses increasingly add more and more sensors and metering points making it impossible for humans to verify and clean the vast pools of data that constantly stream from these devices.

>See also: How to improve data quality in your CRM System

Here is where solutions based on artificial intelligence and machine learning algorithms come into play and help automatically validate the accumulated data ensuring that decisions are based on accurate input data giving high-confidence analytic decision making information.

Predictive analytics for planning ahead

Organisations looking for a new data centre, or for an ideal site on which to build, will find that accurate, validated data can help to compare suitable designs and outline how a location will impact on a facility’s ability to perform.

The complex and often expensive considerations of moving to a hybrid data centre model – a confluence of virtual or private clouds alongside traditional hosted facilities, colocation, SaaS (Software-as-a-Service) and IaaS (Infrastructure-as-a-Service) applications – requires accurate data that can be used to assess the likely performance of the different models and ROI predictions.

CEOs and CIOs will need a finger on the pulse of their facilities, and accurate data will allow them to prove that the decisions they have taken were correct. Data centre managers, at the same time, will look to verified data to predict performance against changing workloads and climate conditions.

>See also: Predictive analytics: the next frontier in business intelligence

The bottom line is that while business leaders need to analyse the ROI of cloud, colocation and edge computing investments, data centre managers must be on top of availability, capacity planning, energy usage and operational costs. But for both, maintaining peak performance is paramount.

Deep understanding enables informed decisions

There are many benefits to advanced data technologies that can power businesses, but in order to use these benefits with confidence, organisations need to ensure they have the right ‘checks and balances’ in place and their data is accurate and reliable.


Sourced by Zahl Limbuwala, co-founder and executive director, Romonet


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Nick Ismail

Nick Ismail is a former editor for Information Age (from 2018 to 2022) before moving on to become Global Head of Brand Journalism at HCLTech. He has a particular interest in smart technologies, AI and...