The business case for using cloud-native big data platforms falls in-line with organisation’s wider digital transformation strategies, for which the increase in IoT technologies also forms a large part of. By capitalising on this, business intelligence (BI) and IT teams will benefit from increased business agility, improving customer experiences and the discovery of new business opportunities.
The cloud is also helping data gathering become more cost-effective, both in terms of storage and compute. From a business perspective this is of course ideal, but a key challenge that remains is the resource and time required to analyse these vast troves of data insights. Businesses are still adopting and transitioning to an IoT world, and therefore will require an adjustment period for the BI and IT teams to define IoT data, and place processes to streamline data capture and analysis.
Data lake or data warehouse?
When taking a broader consideration of cloud-powered data platforms, and ensuring organisations are equipped to make better use of IoT data, both data lakes and data warehouses can be used. While these terms are often grouped together, each has different qualities that can better suit individual business demands.
Data lakes are better for organisations who prefer to freely accumulate raw data from a variety of sources, without specifically defining which data they’d prefer being ingested. Data lakes can be a useful storage solution for these models, as patterns can be triggered as new models are injected.
For example, if a piece of equipment is about to fail, the data lake can potentially identify and pinpoint the exact location from raw data, as opposed to saying there is a general model fault. For those who have more of a focus on what they want to see from data and more defined business goals, data warehouses are valuable in this respect.
But a trend becoming more popular is using a cloud data warehouse as the data lake or even data ‘ocean’. Depending on the data warehouse, the benefits could extend to ingesting all of the raw data in a single location, bypassing intermediate technologies, whilst achieving low-latency relational analytics, and obtaining virtually unlimited, multi-workgroup concurrency scaling.
With IoT data being ingested so rapidly, these cloud powered platforms can ingest and analyse datasets in near real time, drawing immediate insights and action. This is helping alleviate the pressure of BI and IT teams of gathering this data themselves.
With your data platform chosen and IoT data actively being collected in real-time, BI and IT teams will benefit from adding a layer of automation through machine learning models.
As more and more data continues to enter data lakes or data warehouses, machine learning models are able to sift through data for complex patterns and derive valuable insights for the organisation based on their business needs. These models can be monitored, tested and modified to better meet business objectives as patterns in the data change over time through positive feedback and enhancements to upstream processes and applications.
On top of this, many analytics are batch driven and create reports on a wider scale, giving you updates on the entire device network. In the future, automation will help reports become more specific, telling the analyst automatically what within the report requires their attention.
The beauty of these automated processes is that they help reduce manual labour on time-intensive processes, such as gathering data, freeing up teams to focus on actually analysing pertinent data and respond faster than previously possible. With the fast pace innovation of IoT, automation is creating a balance where organisations are now able to stay ahead of the curve, and keep pace with the plethora of data entering the system.
The popularity of IoT will only see increased data volumes, requiring businesses to invest the time to accurately monitor and analyse trends to get the most out of their IoT infrastructure. Failure to do so will leave organisations with a large vacuum of untapped data and a blind view of IoT operations. For organisations, the long-term held mantra of never wanting to throw away data will be replaced by the idea that all data will be analysed and actioned. By combining automation to your cloud data lakes or data warehouses, IoT data will become the beating heart for organisations.