- The Internet of Things is not only changing how consumers interact with the world around them; it is also driving a tectonic shift in how companies process and analyze device data.
- Traditional best practices for gathering and analyzing data, where information is stored and processed centrally, are no longer relevant. Forget big data warehouses. IoT customers are looking to analyze data as close to the source as possible, at the edge of the network.
Companies looking to jump on the burgeoning IoT bandwagon may have to reevaluate how they architect their solutions. Traditional best practices for gathering and analyzing data, where information is stored and processed centrally, are no longer relevant, according to a recent GlobalData survey.
Successful IoT practitioners do not wait for data to coalesce within a central data warehouse before analyzing and taking action on that data. The vast majority (74%) of analytical operations instead are taking place close by – or, better yet, right on – the instrumented devices themselves.
Within vertical markets such as retail, where a sale can be won and lost in a matter of moments, there is no other way to make the necessary rapid-fire decisions, such as which offer to display for a specific customer as he or she enters a store. These decisions cannot wait for such transient events to be uploaded to the company’s cloud.
In response, cloud providers such as Microsoft are beginning to revamp their own platforms to push critical IoT analytics functions such as predictive artificial intelligence algorithms downstream to devices. Even server manufacturers HPE and Dell EMC are targeting edge processing with devices built specifically to run analytics close to instrumented devices.
This preference for IoT immediacy is also reflected in the dominant usage of in-memory databases like SAP HANA and distributed file systems such as Apache Hadoop. These two modern data stores together more than doubled the use of traditional relational databases (only 15%) among those surveyed.
On the other end of the spectrum, only 6% of successful IoT practitioners relied upon a data warehouse for storage and analysis. This isn’t to say that data warehouses have no role to play in IoT. They are an important component of any successful analytics endeavor. But, in the highly distributed world of IoT, immediate decisions cannot wait for traditional decision-making processes based on centralized data. IoT data thrives at the edge.