Patrick Callaghan

<p dir="ltr">The IoT market is on fire, and it’s only going to get hotter as we move further into the future.</p>

<p dir="ltr">According to one recent study, the <a href="https://www.globenewswire.com/news-release/2019/07/27/1892574/0/en/Global-Share-of-IoT-Devices-Market-Size-Will-Reach-USD-158-140-Million-by-2024.html">global IoT market</a> brought in $31.5 billion in 2016. By 2024, the market is predicted to increase more than five-fold, reaching $158.1 billion. Businesses in all industries are <a href="https://www.forbes.com/sites/louiscolumbus/2018/06/06/10-charts-that-will-challenge-your-perspective-of-iots-growth/#755137133ecc">expected</a> to contribute to this massive growth, with manufacturing, transportation and logistics, and utility companies leading the charge.</p>

<p dir="ltr">As smart cities, smart homes, and smart cars start having an even more profound impact on our lives, organizations that are behind these innovations will have more data under their control than ever before. In fact, <a href="https://www.networkworld.com/article/3325397/idc-expect-175-zettabytes-of-data-worldwide-by-2025.html">IDC predicts</a> that worldwide data will grow a whopping 61% each year through 2025, increasing from 33 zettabytes to 175 zettabytes along the way.</p>

<p dir="ltr">Managing all of this data effectively starts with having the right underlying technologies in place. Quite simply, technologies built for a pre-internet world or even a pre-cloud world aren’t powerful enough to keep pace with the coming proliferation of data.</p>

<p dir="ltr">With that in mind, let’s take a look at five technologies we believe are critical for successful data management in an IoT-driven world.</p>

<h2 dir="ltr">1. Time series data modeling</h2>

<p dir="ltr">For years, most organizations took a relational approach to data modeling. While relational databases still have their benefits, the truth is that they were built for a different world—not one where everyone carries significant computing power in their pockets.</p>

<p dir="ltr">The IoT world requires a new kind of data modeling—like the time-series data model, which lets you store data in columns instead of rows, accelerating performance.</p>

<p dir="ltr">Under the time-series data model, you can capture time-stamped data and track, monitor, and aggregate it over time. As the IoT devices become even more common, organizations that wish to view data in the context of time need time-series modeling capabilities.</p>

<h2 dir="ltr">2. Real-time streaming</h2>

<p dir="ltr">IoT devices are constantly sending and receiving information. For example, a water treatment facility might have pH monitors that provide real-time readings of pH levels in various containers so that a technician can ensure they stay within proper ranges.</p>

<p dir="ltr">As such, IoT-centric enterprises need a streaming architecture that supports real-time data analysis, with data coming from multiple sources.&nbsp;</p>

<h2 dir="ltr">3. Data tiering</h2>

<p dir="ltr">In certain IoT systems, data has a short shelf life. If you’re tracking a fleet of construction vehicles to make sure assets are in the right place during a job, for example, that data doesn’t have much relevance (if any) once the job is done.</p>

<p dir="ltr">In the IoT world, enterprises need databases that support data tiering functionality. As time goes on and certain data sets lose their relevance, they can be stored in different tiers to lower storage costs without having to delete data.</p>

<h2 dir="ltr">4. Hybrid cloud</h2>

<p dir="ltr">Organizations are already increasingly moving to <a href="/resources/definitions/what-is-hybrid-cloud">hybrid cloud environments</a> to take advantage of both the cost-effectiveness of the public cloud and the security of the private cloud. As the IoT world develops, more and more of them will follow in those footsteps.</p>

<p dir="ltr">By moving to the hybrid cloud, organizations get the best of both worlds. Data processing can happen closer to the source and in a centralized repository, too.</p>

<h2 dir="ltr">5. Advanced replication</h2>

<p dir="ltr">For organizations to unlock the most value from their IoT investments, they need to be able to easily discover usage patterns and identify weaknesses in devices. Data replication, which allows for the instantaneous transfer of data between two master nodes, helps companies meet these needs.</p>

<p dir="ltr">Advanced replication takes this a step further by enabling data to be replicated from remote devices to a central repository. With the rise of <a href="/blog/2019/07/four-cloud-technology-trends-watch-out">edge computing</a> and the IoT, more and more organizations are leveraging advanced replication to ensure they have reliable data that’s available at all times.</p>

<h2 dir="ltr">Is your organization ready for an IoT-powered world?</h2>

<p dir="ltr">The era of big data has transformed the way organizations think about data management. As we move further into the IoT frontier, data management will become an even more critical component of an organization’s success.</p>

<p dir="ltr">Capitalizing on this next step in the evolution of data starts with having the right underlying technology layer.</p>

<p dir="ltr">To learn more about these five technologies and how they can help your enterprise develop and scale IoT applications while handling an ever-increasing amount of data and maintaining performance, download our ebook, <a href="/resources/ebook/5-things-your-enterprise-needs-manage-iot-data-scale">5 Things Your Enterprise Needs to Manage IoT Data at Scale</a>.</p>


5 Things Your Enterprise Needs to Manage IoT Data at Scale

Patrick Callaghan

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