Industries of all sectors and sizes are looking at how they can collect more information from the proliferation of endpoint devices and computing power. This data is a crucial component of the modern economy. Effective analysis can result in powerful, data-driven business decisions that help organisations succeed.
For many businesses processing this data in-house and on-site will quickly become unmanageable as the volume increases. Other businesses already hold a huge quantity of data, but don’t realise – or are unable to access – its full potential. In both cases, a further risk arises that the company may accidentally contravene data protection legislation as a result of its inappropriate data processes. All of this data needs to be managed, stored and protected in a better, smarter way than traditional data solutions can provide.
Industrial manufacturing is a perfect example of an industry where data generation has exploded in recent years. Almost every new product that comes out of modern manufacturing plants has more endpoints, monitoring systems and connected capabilities than the last. Many manufacturers are still trying to collate and manage this influx of data on-site and in-house and we’re seeing the same thing across the financial, retail and healthcare industries. However, the traditional method of data handling will soon be overwhelmed. The processing power required to make useful sense of the volume of big data is unprecedented and is increasingly something that must be outsourced.
Data centre providers are working smarter to avoid being overwhelmed by big data. Artificial intelligence (AI) and automated systems hold the key to managing the data overload. Removing the human element from data analysis and management allows for much greater speeds and power-saving efficiencies.
An effectively programmed AI system can reduce the amount of information that has to be actually processed and stored. It does so by monitoring all data streams, but only flagging significant variations from the norm for further inspection and analysis. This filtering expands the capabilities of existing systems to cope with the demands of handling big data.
A key contributor to big data is the Internet of Things (IoT). These small, connected devices running lightweight software can be easily installed in extremely remote destinations. As a result, business data networks are fast becoming decentralised. In such situations the traditional factory-based data centre model is even more inappropriate. Smaller “edge” data centres are the answer for coping with the disparate networks that big data and IoT produce. These readily deployable, modular data centres provide mobile jumping on/off points for the larger network allowing data to be efficiently collected and processed globally.
Businesses must react to pressure from governments and regulators to know exactly where data from specific geographies is being transferred to, analysed or stored. Legislative changes have forced businesses to take responsibility for how their data is managed, or face the financial consequences.
They include the General Data Protection Regulation (GDPR), which the European Union (EU) adopted in April and is scheduled to come into force from May 2018 and the Privacy Shield, which aims to provide stronger protection for transatlantic data flows between the EU and the US. Google and Dropbox are the most recent groups to have signed up to the Privacy Shield since it was approved in July. This regulatory pressure emphasises the importance of implementing a localised network of edge data centres, securely housing, processing and transferring big data within clearly defined geographies.
In order to take full advantage of data as a business, owners and managers should look to reduce their dependency on on-premise IT, embrace virtualisation (cloud) and move towards a future where data management can be automated through software-defined networking. An understanding of how to properly collect, process and derive value from data is not something that can be put off.
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