Automating the Indoors: How Objectivity is driving innovation in the Industrial IoT

Automating the Indoors: How Objectivity is driving innovation in the Industrial IoT

Thinking of my previous blog about our long-term customer in the oil and gas industry got me walking down memory lane regarding other customers, this time one in building automation, which I was involved with in the early days of Objectivity.

This customer has a long history with Objectivity during which it has grown through various corporate and technology acquisitions in several different countries. Although their automation system has evolved over time, Objectivity/DB has remained as the persistent object store at the heart of it.

The system monitors networks of sensors for heating, air conditioning and ventilation, fire detection, intrusion, and other equipment. The main goals of the system are the following: provide investment protection well into the future while protecting past investments; support expanding needs as new technology trends in information, communication and building automation emerge; and ensure compatibility across a wide range of equipment from many different suppliers. Deployments of the system range from single buildings to large distributed campuses and airports.

Objectivity and Intel: Building the Big Data Backbone for the Industrial IoT

Objectivity and Intel: Building the Big Data Backbone for the Industrial IoT

We are at the cusp of the golden age of Fast Data. As more and more organizations deploy massive sensor networks, there is a growing imperative to better leverage increasing volumes of data from sensor networks to provide timely insight and improve business operations. At the same time, a recent McKinsey & Co report on the Internet of Things (“Internet of Things: Mapping the Value Beyond the Hype”) points out that in some industries, less than 1% of data from sensor networks is being utilized in discovering operational insights. This chasm between potential and real value can be bridged through improved streaming analytics tools, data fusion tools, and Hadoop-based enterprise data management systems to allow organizations to pursue new data strategies for creating greater value from IoT Fast Data streams.

When planning for Big Data events is its own Big Data problem

When planning for Big Data events is its own Big Data problem

As a marketing coordinator at Objectivity, I need to have certain qualities: strategic thinking, creativity, and attention to detail—especially when it comes to event planning.

One of the upcoming events that Objectivity will be attending is Strata + Hadoop World, which takes place Sept. 29 – Oct. 1 in New York City. There are many action items to complete before the event starts, one of which is handling logistics. This process can be time-consuming, deciding which giveaways to order, which marketing materials to ship, and which exhibit setup is best suited for this event.

Hard Data vs. Soft Data

Do you ever wonder to yourself: which way home is going to be the fastest today? I know this conundrum personally, for although I have a few options on how to get home (thanks to innovative apps like Waze), I know from experience that the shortest path from work is not always the best choice. There could be unexpected delays from accidents, events, general congestion, construction, or just the particular time of day. Not only do I need to know my actual route options, I also need to understand what is happening along the routes to figure out the best way to get home. This is a perfect example of the entwined relationships of Hard and Soft Data. Hard Data is defined as data in the form of numbers or graphs, as opposed to qualitative information¹. In the world of Big Data and the Internet of Things (IoT), Hard Data describes the types of data that are generated from devices and applications, such as phones, computers, sensors, smart meters, traffic monitoring systems, call detail records, bank transaction records, among others. This information can be measured, traced, and validated. Most organizations today already use Hard Data for analysis, geo-location, predictions, and optimization and are now trying to to differentiate themselves and add more value to their Hard Data applications for their customers. Just like in the earlier example of finding the best traffic route home, organizations are realizing that the best way to exact real value from Big Data is to look at All Data. This, of course, means taking Soft Data into account. Objectivity refers to Soft Data as...