The Evolution of Data Management for IoT
By JAdP on October 17th, 2016
In Data Management and Analysis, Internet of Things, Data Integration, Advanced Metadata
In the upcoming webinar for SnapLogic, we will be looking at the Internet of Things from the perspective of data.
- What data can be expected
- How IoT data builds upon the evolution of data management and analytics for big data
- Why IoT data differs from data from other sources
- Who can make the most use of IoT data or Who can be impacted most by IoT data
- Where IoT data needs to be processed
- When IoT data has an impact
Specifically, how the recent evolution of data management in response to big data, is ideally suited in some ways for IoT data, and is still evolving for some unique characteristics of IoT data and metadata.
The business drivers range from new sources of data that can help organizations better understand, service and retain customers, to consolidation in many industries bringing about the need to bring together data from disparate and duplicate information and operation systems after merger and acquisition. One of the more pervasive developments has been the movement of data acquisition, storage, processing, management and analytics, to the Cloud.
Beyond these corporate motives, governments and non-government organizations (NGOs) are using data for good to bring about better quality of life for millions or billions of individuals. Clean water, prosecuting genocide, fighting human trafficking, reducing hunger, and opening up new means of commerce are only a few examples. Some look at the future and see a utopian paradise, others a dystopian wasteland. The IoT with evolving data management and analytics are unlikely to bring about either extreme, but I do think that the future will be better for billions as a result.
The basic question that we’ll ask in this webinar is “What is the Internet of Things?”. From simple connectivity, to the resulting cognitive patterns that will be exhibited by these connected things, we will explore what it means to be a thing on the Internet of Things, how the IoT is currently evolving, and how to bring value from the IoT. It is also important to recognize that the IoT is already here, many organizations are reaping the benefits from IoT data management and sensor analytics. The webinar will show ways in which your organization can join the IoT or mature your IoT capabilities.
Big data was often described by three parameters overwhelming the old ways of integrating and storing data: volume, velocity and variety. Really, we are looking at deftly interweaving the volumetric flow of data in timely ways that flexibly provide for privacy, security, convenience, transparency, governance and compliance. Nowhere is this evolution better expressed than in data management for the Internet of Things (IoT).
We will cover some of the more interesting and useful aspects of preparing for IoT data and sensor analytics. Though coined by Kevin Ashton in 1999, the IoT is still considered in the early stages of adoption and relevance. While the latest trends in data management and analytics apply to IoT data and sensor analytics, there are specific needs for properly addressing IoT data, which legacy ETL (extract, transform and load) and DBMS (database management systems) simply don’t handle well, such as time-series data and location data, as well as metadata specific to IoT. In addition to these characteristics of IoT data, we will explore other aspects that make IoT data so interesting.
The IoT isn’t meeting its hype as yet, which requires many solution spaces coming together as ecosystems. Instead, the IoT is growing within each vertical separately, creating new data silos. This is exemplified by the 30-plus standards bodies addressing IoT data communication, transport and packaging. Metadata and API management can help. Metadata also addresses the nuances of IoT data, such as the factors arising from replacing a sensor that allow continuity of the data set and understanding of the difference before and after the change.
Information Technology (IT) and Operational Technology (OT) are coming together in IoT. This means interfacing legacy systems on both side of the house, such as enterprise resource planning (ERP) and customer relationship management (CRM) systems with supervisory control and data acquisition (SCADA) systems, and relational database management systems (RDBMS) with Historians DBMS. This also means deriving context from the EDGE of the IoT for use in central IT and OT systems, and bringing context from those central systems for use in streaming analytics at the Edge. Further this means that machine learning (ML) is not just for deep analysis at the end of the DMA process; ML is now necessary for properly managing data at each step from the sensor or actuator generating the data stream, to intermediate gateways, to central, massively scalable analytic platforms, on-premises and in the Cloud.
As we discuss all of this, our participants in today’s webinar will come away with five specific recommendations on gaining advantage through the latest IoT data management technologies and business processes. For more on what we will be discussing, visit my post on the SnapLogic Blog. I hope that you’ll register and join the conversation on 2016 October 27 at 10:00 am PDT.
