used with permission from IBM Big Data & Analytics Hub
by Kayla Matthews
Data management is a discipline that’s remained relatively unchanged and, to put it bluntly, somewhat stagnant over the past 10 or 20 years.
Since the dawn of the Internet of Things (IoT), these trends have already reversed.
With estimates predicting 50 billion interconnected devices within IoT by 2020, and the fact that there were only four billion smartphone users in mid-2017, it’s easy to see that we’re staring down a deluge of new data in the next few years.
This newfound growth is affecting data management in profound and significant ways.
As industry professionals attempt to rein in big data, they’re using a mixture of deployment strategies, hardware configurations and software packages.
Collating and centralizing big data
Processing and organizing big data isn’t the easy and straightforward task that it seems to be.
Instead of working with uniform data sets that are easily interpreted and entered into spreadsheets or charts, big data draws information from a countless number of sources.
Complicating matters even further is the fact that many of these sources use different methods of data collection, including various sensors, automated reports, historical trend analysis and much more.
With such a wide variety of information streaming in from so many different avenues, big data analysts have their work cut out for them.
In some cases, multiple sources will produce conflicting or contradictory information. Recognizing duplicate or incorrect data at the beginning of the process is critical to maintaining data quality and integrity throughout the entire operation.
This is where a keen human eye and astute attention to detail come in handy.
Choosing the right architecture
Like all digital information, the larger data sets of today are manageable through many different file architectures.
Your personal computer likely uses NTFS (New Technology File System) but some systems, particularly older machines, use some variation of the File Allocation Table (FAT) architecture.
However, these modern frameworks don’t provide enough computational power for the large datasets seen in the IoT and other similar applications. Instead, professionals turn to platforms like Xively, ThingSpeak, Plotly, Carriots, Exosite, AMEE, Axeda and Connecterra to use the cloud for big data storage, processing and management.
Enterprises that maintain their own IoT-connected servers and hardware also have options. Some of the most popular operating systems include:
- Windows 10 for IoT. Microsoft’s product is a little rushed in that it was hurried to the market to compete with several existing brands. But unlike most of its competitors, Windows 10 for IoT comes with the reputation, usability and comprehensive technical support that is already familiar to most mainstream users.
- Google Brillo. Focused on the development of Android apps and Android-based embedded systems, Brillo is an excellent alternative to Microsoft or any of the other products on the market today.
- RIOT OS. Highly popular due to its open source design, RIOT OS is a free-to-use system that supports various embedded devices, sensors and PCs.
Although Apple doesn’t have a specific IoT-oriented platform or operating system available for public consumption, its HomeKit framework provides rudimentary access and is likely a key building block for a future system from the popular developer.
With the ability to capture and process data in real-time, generate intuitive data models and automate decision-making tasks, these next-gen file architectures and operating systems represent a monumental shift in the way professionals approach the field of data management.
Experiencing the change and adapting
The field of data management is currently in the midst of explosive growth and development. It’s not something that will happen a week or a month from now. It’s already underway.
As IT experts and business professionals continue to come together for the sake of big data analytics and IoT, we’ll see even more innovations, including new architectures and operating systems, to accommodate the needs of consumers and big business alike.