Consider a world of over 26.6 billion interconnected devices each producing, sharing and processing data that quantifies to over 2.5 quintillion bytes every day. That world is oursand comenext year these figures are projected to rise even higher. This is good news for organizations who possess mature analytics teams capable of exploiting the latentbenefits of IoT. It’s not as straightforward however, for other businesses with less seasoned analytics teams and or legacy infrastructure.

Amidst all the hype and exponential rise in data volumes associated with IoT, it becomes easy to lose a bit of objectivity. And as a consequence,IoT shows its face as a complex solution to the otherwise rudimentary problem of deriving value from data.

Analytics however wasn’t meant to be overly complex. While IoT insight can be a transformational, to the point that states like Houston and New York use it to drive energy conservation, it can also be a means to furthering less glamorous, but equally critical,organizational goals such as asset tracking to maintain inventory levels or theft detection. To do this,organizations must, however, break away from the norm and commit to the fundamentals – take more baby steps and less strides.

Optimizing Database Architecture

IoT thrives on prodigiousvolumes of data.  Any organization looking to unlock its potential must first scale its storage and update its RDBMS technology to one that can support data of considerablemagnitude. In that light, a traditional database management suit while relevant is no longer sufficient for handling streaming operations to collect and store petabyte-scale semi-structured data sets.

And so, slowly but surely, the Analytics ecosphere is transitioning to MPP technology or elastic cloud data warehouses better suited to handle the volume, variety and velocity challenges of working with IoT data.  Aside from being cheaper and easier to set up, operate and maintain, MPP technology also possesses the innate ability to support Lambda Architecture, the prevailing best practice for stream ingestion.  It’s no wonder why see companies like Snowflake (an MPP data-warehouse-as-a-service technology) experiencing tremendous growth, with over 200% increase in customers.  With the stratospheric rise in data volumes projected for 2020, organizations must modernizetheir existinginfrastructure to get a piece of the IoT analytics cake.

Parsing Distinct Segments of the Analytics Circle with IoT

On its own IoT lacks the transformational capacity to prosper organizational objectives. Forit to be effective, it must be combinedwith other key analytics competencies like AI/machine learning and streaming technologies. The good news is that the entire analytics architecture exists as a modular stack that allows for stepwise aggregation of its distinct fragments. For example, Data Meaning has an offering where real-time IoT data pipelines can be bolted on to your AWS or Azure cloud environments to allow for seamless integration into your analytics stack.

With every successful phase of implementation, organizations can expect to derive enough value to account for their investments plus sufficient trust from business partners to secure funding for additional analytics fragments. In other words, the Big Bang approach to building out internal competencies and infrastructure is out and the phased approach is in.  So, while the whole concept of analytics of IoT data might seem tumultuous when viewed from an empirical perspective, when broken into fragments,it becomes attainable. As your organization heads into the year, this is the approach you should be considering.

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