A NEW APPROACH TO DATA
MANAGEMENT and ANALYTICS
IoT challenges require a new approach
to data management and analytics
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Data management and analytics tools will be the core enabler of new values created by the intersection of the Internet of Things (IoT), people and the physical world. However, these values will not be reached through today’s fragmented incomplete tools that are not capable of addressing the diverse data types flowing from new IoT applications. An emerging class of data management and analytics tools, which are increasingly tailored to address unique IoT data requirements, will be critical to realizing the full potential of smart systems. HPE Vertica’s analytical database offering addresses these challenges, and goes far beyond fragmented and legacy systems that are simply not equipped to handle the challenges of IoT data.
CONFRONTING THE REALITIES OF IOT DATA
The Internet of Things (IoT) and the new world of Smart Systems are ushering in an era where people, machines, devices, sensors, and businesses are all connected and able to interact with one another. The convergence of networked computing and large-scale data management with real time machine intelligence is driving the integration of the physical and virtual worlds and creating unimagined new values. Data management, modeling and analytic tools are the core enablers of these new values.
While analytics tools and techniques are already finding their way around the Smart Systems and the Internet of Things arena, the integration of systems and tools is lagging and there are numerous hurdles that have constrained growth in machine data management and IoT analytics. Both commercial and industrial applications will benefit from better organized end-to-end solutions.
The challenges and complexities of developing and deploying IoT applications have led to a great deal of confusion over the requirements for and benefits of newly emerging data management and analytics tools. Underlying this lack of understanding are major pain points that span a wide range of applications.
A major driver of the need for new data management tools is the diversity of data types users want to analyze. Because machine and sensor data tends to be noisy, analog, and high-velocity, there are major challenges that traditional data management and analytics tools and techniques do not handle well. This is especially true if you want to integrate streaming sensor data and historical structured data in real time.
The requirements for data management and analytics solutions are rapidly evolving. Legacy data management and analytics offerings, which were built for a pre-IoT world, are not equipped to handle this deluge of structured and semi-structured data from machine logs, sensors and other devices. Fortunately, emerging data management and analytics solutions can address these challenges much more quickly and in a significantly more cost-effective manner than previously possible, while also enabling new business efficiencies and increasingly complex IoT applications. These solutions are comprised of multiple elements that make it easier to normalize, cleanse and aggregate data and analyze data.
ADVANCED ANALYTICS NECESSITATES NEW DATA MANAGEMENT TOOLS
Today, equipment manufacturers are under pressure to provide new services that drive additional customer value and lead to new growth. To address this challenge, we envision select equipment manufacturers, machine builders and tech suppliers acting as a catalyst for data management and analytics adoption and dissemination of tools – driving new tools and capabilities into end customer accounts and processes. However, an equipment manufacturer’s role as a catalyst is only as good as the tools they are enabled with and the collaborative ecosystems they are able to form. Customers are looking to equipment manufacturers not just for high-quality equipment, but also for help in optimizing their ability to supply consistent and high-quality products and services to their customers.
Equipment manufacturers would be remiss if they attempted to develop their own in-house, cobbled together data management and analytics solutions. Instead, these equipment manufacturers now have the option to implement a unified solution that provides all functional requirements needed to implement a true IoT data management solution. In a Smart Systems-centric world, the winners will be those who embrace collaborative business models that have the potential to offer entirely new levels of service and create new value through multi-party collaboration across a complex, multi-brand, multi-business system—and all of this starts with effective data management and analytics tools that directly address the challenges of IoT data.
Unfortunately, this potential is not yet realized because most of today’s IoT platforms merely function as inefficient data traps. It is critical that IoT platforms incorporate data management solutions that allow for not only more efficient organization, storage and query of data, but also the integration of multiple, disparate data sources into a unified view in a more efficient manner. This is much easier said than done, especially for IoT platforms that are attempting to use legacy and open-source solutions that are ill-equipped to handle the unique challenges of IoT data. Therefore, if IoT platform players want to live up to their promise of providing new levels of value to end-users, they must revisit their current data management solutions.
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