Welcome!

@ThingsExpo Authors: Zakia Bouachraoui, Yeshim Deniz, Liz McMillan, Elizabeth White, Pat Romanski

Related Topics: @ThingsExpo, @CloudExpo, @DXWorldExpo

@ThingsExpo: Blog Post

Accelerating IoT Deployments to Achieve Business Goals Faster | @ThingsExpo #IoT #M2M #API

IoT creates an opportunity to measure, collect and analyze an ever-increasing variety of behavioral statistics

Accelerating IoT Deployments to Achieve Business Goals Faster

The "Internet of Things" is an exciting area of tech, one in which industry experts estimate there will be more than 30 billion connected IoT devices by 2020. IoT is the inter-networking and instrumentation of physical devices - everything from streets, cars, factories, power grids, ice caps, satellites, and clothing to phones, microwaves, milk containers, planets, human bodies, etc.

IoT creates an opportunity to measure, collect and analyze an ever-increasing variety of behavioral statistics. That being said, data, and more importantly insight into the data, is key for enhanced business value and achieving goals quickly. The proliferation of IoT devices and the sheer volume of large data streams have changed the data processing characteristics that we were accustomed to, forcing new architectures and infrastructure that are designed for streaming data, real-time insights, and real-time analytics.

In order to gain real-time insights to facilitate automation, data about these devices needs to be collected and analyzed with the help of various technologies making up IoT Architecture. For example, by providing IoT monitoring and control companies can optimize plant safety and security, as well as extend into asset management allowing forpredictive maintenance thatdrives efficiencies and maximizes reliability.

Another example of IoT is home automation (also known as smart home devices) to control and automate lighting, heating, air conditioning and household appliances. IoT in infrastructure management monitors and controls urban and rural solar panels, railway tracks, wind-farms, and manufacturing.

Today's modern applications require action. For developers, in order to take appropriate action given the type of data they are working with, it's becoming essential to have an IoT data platform that can provide a set of tools and services to get metrics and events data from sensors, devices, systems, machines, containers, and applications. With an IoT data platform, developers can closely monitor and analyze data for collecting, normalizing, detecting events, as well as storing, managing and automating the entire system.

Given the potential for mixed workloads and data requirements, and to fully evaluate and manipulate IoT data, companies need to consider an IoT platform that specializes in handling a high volume of writes and queries over large and changing data sets. In addition, consideration of storing time stamped data - and looking at changes over a period of time - has to be taken into account. This leads to using a Time Series Database (TSDB), which makes sense because IoT data is time series data and modern TSDBs are built specifically for handling metrics and events or measurements that are time-stamped. A TSDB is optimized for measuring change over time, which is important because time series data, including IoT data, is very different than other data workloads because it must collect data lifecycle management, summarization and large range scans of many records.

Regarding analytics, there are specific kinds of analytics that are looking at real-time change over time - and for deriving insights from these changes -  a TSDB is critical. Time series data such as IoT data comes in two forms: traditional regular (metrics) and irregular (events). Both data forms are present in IoT data. Today's more advanced TSDB platforms are optimized for both regular (example a sensor sending temperature readings every millisecond) and irregular time series data (for example a sensor sending pressure data only if it is above 1000psi). In addition, this class of TSDBs has evolved their data model over earlier time series solutions and have no limits on the number of tags and fields that can be used. This allows timestamp precision in nanoseconds, which is important as sub-millisecond operations become more common with IoT architectures.

Developers also need the ability to perform correlation, aggregation, and pattern detection of the streaming data before it gets to the database - in short the delivery of streaming analytics. They also need the ability to visualize real-time data and set up notification, control and action services to automate the entire IoT system. This can all be delivered by today's advanced TSDB platforms.

Using TSDB platform to help solve the business goals of developing an IoT application - there are three steps that are critical for success:

Accumulate
Developers need to accumulate a comprehensive set of tools and services to get metrics and events data from sensors, devices, systems, machines, containers, and applications. With an open source solution, users can access a number of integrations to popular databases, containers, services, applications, and other monitoring and alerting products.

Analyze
With the right platform, developers can analyze and access real-time stream processing of the data and storage of the time-series data. They are able to graph and visualize data and perform ad hoc exploration of data as needed.

Act
Today's modern applications require actions. Using plugin custom logic or user-defined functions, developers can process alerts with dynamic thresholds, match metrics for patterns or compute statistical anomalies, automatically scale containers, and basically do anything that can programmed.

The world of IoT seems limitless. Developers need to have visibility into all aspects of their data in real time to help meet the demands of even the largest monitoring and IoT deployments.

More Stories By Mark Herring

Mark Herring is a zealous marketer who believes that the road to marketing success always leads with the developer. Before InfluxData, he was VP of corporate marketing and developer marketing at Hortonworks, SVP of Products at Software AG, VP of Middleware, Java and MySQL Marketing at Sun Microsystems, and VP of Marketing at Forte Software. Earlier in his career, he was a developer and technical support engineer for Oracle. He holds a BS from the University of Witwatersrand, South Africa.

Comments (0)

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.


IoT & Smart Cities Stories
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-c...
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by ...
The explosion of new web/cloud/IoT-based applications and the data they generate are transforming our world right before our eyes. In this rush to adopt these new technologies, organizations are often ignoring fundamental questions concerning who owns the data and failing to ask for permission to conduct invasive surveillance of their customers. Organizations that are not transparent about how their systems gather data telemetry without offering shared data ownership risk product rejection, regu...
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...
Poor data quality and analytics drive down business value. In fact, Gartner estimated that the average financial impact of poor data quality on organizations is $9.7 million per year. But bad data is much more than a cost center. By eroding trust in information, analytics and the business decisions based on these, it is a serious impediment to digital transformation.
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
Predicting the future has never been more challenging - not because of the lack of data but because of the flood of ungoverned and risk laden information. Microsoft states that 2.5 exabytes of data are created every day. Expectations and reliance on data are being pushed to the limits, as demands around hybrid options continue to grow.
Digital Transformation and Disruption, Amazon Style - What You Can Learn. Chris Kocher is a co-founder of Grey Heron, a management and strategic marketing consulting firm. He has 25+ years in both strategic and hands-on operating experience helping executives and investors build revenues and shareholder value. He has consulted with over 130 companies on innovating with new business models, product strategies and monetization. Chris has held management positions at HP and Symantec in addition to ...
Enterprises have taken advantage of IoT to achieve important revenue and cost advantages. What is less apparent is how incumbent enterprises operating at scale have, following success with IoT, built analytic, operations management and software development capabilities - ranging from autonomous vehicles to manageable robotics installations. They have embraced these capabilities as if they were Silicon Valley startups.
As IoT continues to increase momentum, so does the associated risk. Secure Device Lifecycle Management (DLM) is ranked as one of the most important technology areas of IoT. Driving this trend is the realization that secure support for IoT devices provides companies the ability to deliver high-quality, reliable, secure offerings faster, create new revenue streams, and reduce support costs, all while building a competitive advantage in their markets. In this session, we will use customer use cases...