Data management: the essential ingredient to a successful IoT strategy

Pic: an Howells jan@sarumpr.com

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The Internet of Things (IoT) has not been with us for that long, but it has already made a significant impact on the world. Furthermore, there is little doubt that it will dramatically change the way we work and live, bringing with it a truly connected, data-centric landscape.

Gartner forecasts that by as early as 2021 there will be 25 billion connected devices, churning out huge volumes of data that drive IoT. In addition, the analyst firm sees artificial intelligence (AI) being applied to IoT data, including sensor data, network traffic activity, still images, video and speech. All of which will provide enterprises with even more business insight. At the same time IoT Analytics predicts that the global IoT market will be worth a gargantuan $1.5 trillion by 2025.  

The surge of data coming from these devices will prompt enterprises to evaluate their data management strategies if they are to make any sense of all this information coming down the pipe - and work out what is actually relevant to their businesses. By applying advanced analytics to these diverse streams of data, enterprises will gain access to previously unobtainable insights for smart decision making. But to achieve this, their data must be properly prepped and managed. 

Yesterday’s data management processes are inadequate when it comes to managing IoT’s data volume and diversity and puts huge pressure on traditional data management infrastructures. But with so many enterprises still struggling with the storage of content alone, this has the potential to be a seriously overwhelming challenge. 

Data is continually being referred to as the new oil in terms of its value to business. With this growing importance and the sheer volume of IoT data that is coming, how can enterprises prepare themselves for the information onslaught and what is the best overall approach to take?

A connected world

As IoT goes mainstream, consumers will benefit from faster product time to market, increasingly personalised products and a better user experience amongst others. IoT has the power to enhance energy efficiency, health, transport and education. 

For enterprises however, it means a tsunami of data. To put this in perspective IDC's ‘Data Age 2025’ whitepaper predicted that the collective sum of the world’s data will grow from 33 zettabytes in 2018 to a colossal 175ZB by 2025. That is an enormous increase in data to contend with. 

Before embarking on an IoT data management strategy, enterprises must understand the importance of the data created by their IoT devices, and the importance of it being reliable, easily accessible and secure. An effective data management strategy enables business leaders to ensure that the challenges posed by an IoT infrastructure do not outweigh any benefits and bolster the bottom-line. 

Many enterprises however, are glaringly unaware of the data challenge they are facing. The speed at which this data will be generated by IoT devices will require a complete re-think of data management and the data lifecycle. Many enterprises simply will not have the architectures, policies and technologies in place to handle these massive amounts of data. Without a centralized management infrastructure and a scalable approach, enterprises will find the whole issue insurmountable. 

In addition, enterprises must not forget the immediacy that also comes with IoT solutions. Data is generated so fast and has such a short shelf-life, storage becomes a real problem. IoT works on rapid data flow and immediate insight. Connecting a diverse range of devices can make real-time processing and analysis much more complex. 

The IDC whitepaper forecast that by 2025, 30 percent of the data generated will be consumed in real-time. This brings up the question of recording data for compliance and legal purposes. Enterprises for example, must understand what data needs to be recorded before and after the event as proof that the right decision was made. 

It is now more than 12 months since GDPR came in with businesses needing to show that they are applying due care and attention with any data coming into the organisation. IoT brings yet more data and complexity to this regulatory equation. A common mistake is to think that GDPR doesn’t impact an enterprise’s business directly in its use of IoT. This is foolhardy. An enterprise may not know where all its devices are located, for example, and where the data is flowing from. GDPR comes with teeth and this year we have already seen a number of large fines for non-compliance. It is essential that GDPR is factored into any robust IoT strategy to avoid such fines and any legal issues. 

Metadata is invaluable

Metadata is crucial when it comes to IoT data. Metadata provides information about other data, making finding and working with particular data instances much easier. Metadata also makes the organisation of data far more efficient and avoids an IoT data sprawl. 

Take for example a sensor that is continuously uploading data from a remote device. This data means nothing unless an enterprise knows exactly what that sensor is recording, what the default values are, what the extremities are and how it relates to other sensors. Data needs to have this depth of context to provide real business value.

Any enterprise that wants to get maximum value from its IoT data must label, categorise and describe all its data clearly and effectively. This is where the power of a modern enterprise content management (ECM) solution comes in. Next-generation ECM systems now use the latest artificial intelligence (AI) and machine learning technologies to identify data and content and automatically apply the right set of metadata values to it so that it can be instantly recognised. 

The power of AI-enabled ECM

But in this era of big data – and with the rise of IoT data, ‘big data’ is going to get much bigger – an enterprise needs more than a generic AI solution to unlock the insight and value within this data. Enterprises need the capability to train their own custom AI models using business-specific data sets. This enhances results as the metadata management continuously improves as the machine learning itself develops. 

But AI allows an organisation to do even more, to access and manage the large volumes of unstructured data it holds. Any IoT data is then viewed within the context of this data, meaning it delivers significantly more insight.

IoT data offers a massive opportunity when it comes to the extraction of data insight, but there is the possibility that many enterprises will feel completely besieged. The smart use of metadata and deploying the latest AI technology can help ensure that this does not happen. 

Moving forward 

IoT is revolutionising the way companies do business. It brings with it great opportunities, but it doesn’t come without risk. It demands a new way of thinking and that includes a transformation of models relating to the storage and management of data. Enterprises that can change and pull value-added insight from their data will be the ones that will succeed in this brave, new connected world. Efficient data management has a major role to play in this.