Before the Internet of Things (IoT) came along—billions of networked sensors and devices capable of generating enormous amounts of new, unstructured real-time data—big data was already really, really big. To tackle this, businesses small and large have taken to the cloud and reworked their IT architectures to create more flexible, scalable ways to manage their data.
However, for those businesses and data scientists looking to capitalize on the high-value, target-rich data the IoT will be churning out over the next decade, there will be even more to consider when it comes to data architecture. And the data scientists equipped to turn this data into meaningful insights with advanced analytics will be in even greater demand.
So how much bigger is data going to get with the IoT, and how will this change the way businesses gather, store, compute, and consume data?
Five ways to overcome common data-related barriers to IoT adoption
IoT data presents a number of challenges for companies. Here are a few ways companies can meet these challenges head-on.
1. More data means companies will have to rethink their IT and data center infrastructures.
For all of its potential, effective IoT data analytics will hinge on better IT infrastructures—data centers, server clusters, cloud-based computing, and more. Businesses that want to leverage IoT data will need to invest in long-term IT architecture planning. Why? Because this new influx of data from sensors and devices will put more pressure on existing networks and data centers and require more power to process it. Before data experts can even begin applying analytics, data needs to be aggregated and organized—and this will be no small feat.
Whether it’s a consumer company gathering data from wearables and mobile devices, or enterprise organizations processing data from industrial sensors and manufacturing equipment, upgrades will be inevitable. Services like Hadoop, with its distributed server clusters and parallel processing, will be important, as will the people who know how to set it up and work with its more tricky aspects.
Data centers themselves will most likely lean toward a more distributed approach, with tiered mini centers that pull data, then send it on to be processed further in second- and third-tier clusters. Obviously, this approach will have an impact on data storage, bandwidth, and backup.
2. With the IoT, quality data will be actionable data.
The key to all this new data? Finding the information that’s actionable and capable of creating real, meaningful change. More isn’t always more, and many companies collecting automated data from sensors will likely have more data than they know what to do with.
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Complex estimations aside, the 20+ billion devices predicted to be around by 2020 are going to have an inevitable effect on the three V’s of big data: volume, velocity, and variety. More, faster, and less structured data will be pouring in from sensored devices. But is all of this data going to be valuable?
IoT data is unique in that it’s only really valuable to us if it’s actionable, and that percentage of the massive–and totally new streams of data coming in–will be a bit easier to manage. Sifting through this data will be the job of business analysts who know what questions they want their data to answer, and of data scientists who know how to get those answers.
A car equipped with various sensors constantly transmitting data points about its performance, for example, can create a lot of noise. Being able to hone in on the data and patterns that can yield valuable information that’s helpful to consumers and manufacturers will be the key.
3. NoSQL databases will most likely outpace traditional RDBMSs.
Much of this IoT data will be unstructured, meaning it can’t be easily sorted into tables like a relational database management system (RDBMS). NoSQL databases like Couchbase, Cassandra, and MongoDB will be able to offer IoT data scientists the flexibility they need to organize data in a way that makes the data usable.
More data means we’ll need more places to aggregate the data, and more power to process it—often in real-time scenarios. Microsoft Azure, Cloudera, Amazon, and Apache’s cloud-based computing platform Hadoop, with its Hive and Pig components and Spark processing engines, are all poised to take on this surge of new IoT data.
4. Beyond collecting data, businesses need to choose a software stack for preprocessing and analyzing IoT data.
Once this massive amount of data is collected and organized, businesses need to have the right plan and software stack in place to analyze it. Carefully choosing a stack of software and databases will ensure the system can handle the types and the scale of the data anticipated.
First, because much of this data will be raw and unstandardized, it needs to be transformed and preprocessed with tools like Hadoop’s Pig component, then stored in a database. Analytics tools like Apache Storm, which is especially suited for the continuous streams of real-time data the IoT will generate, should be put in place for analytics. The overall analytics solution should be strategic specifically for IoT data, its speed, and its volume.
5. We’ll need more—and more skilled—data analysts to make IoT data valuable.
Companies will need to have the right people in place to analyze and make all of this structured, unstructured, or semi-structured data into valuable business insights.
To make the most of your data, you’ll need skilled business analysts who know what they’re looking for from the data, what questions to ask of it, and how that data will translate into value for the company. Then, it’s up to the data scientist to do the looking, answer those questions, and deliver that value, through a combination of:
- Data infrastructure and processing: Hadoop’s file system computing (and Spark) can be challenging even for seasoned data scientists and architects. Having a large-scale Hadoop cluster requires a lot of assembly, so anyone who knows their way around Hadoop will be in demand.
- The R data programming language and modeling package: This powerful big data analytics tool will be an in-demand skill for data scientists who will need to provide deep learning. R is a popular software package and open-source statistical modeling language that allows statisticians to undertake specialized tasks, including text analysis, speech analysis, and tools for genomic sciences, with add-on packages for handling big datasets and parallel processing techniques.
- Other skills to look for:
- Deep learning
- Data mining
- Algorithms
- Machine learning
- Complex event processing
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