Incorporate streaming analytics in the Internet of Things

Product Marketing Manager

The power of the Internet of Things (IoT) has already begun disrupting business. According to the Evans Data Corporation’s Global Developer Population and Demographics Study, some 6.2 million developers are already working on IoT applications. Not surprisingly, then, an IDC study predicts that IoT spending will reach $1.7 trillion by 2020. These figures speak clearly of the growing importance of the Internet of Things in the modern world of business.

As clients move from data management to action, they rely on continuous insight. Speed isn’t merely about how quickly data is produced or changed but rather involves the speed at which data must be received, understood and processed. Here are four ways of harnessing fast-moving data both inside and outside the organization, driving the shift from data management to action.

Accelerate the delivery of streaming applications

Python support, available in IBM Streams 4.2, can accelerate the delivery of streaming applications. Because many of the data scientists and data engineers who develop analytics applications know Python already, and because Python is broadly preferred as a language for writing algorithms and machine learning, Python support flattens the learning curve for developers, speeding application delivery and accelerating overall time to value for the business. Because developers can use their existing Python code to build Streams applications, they can avoid starting from scratch. What’s more, Python allows the creation of applications using APIs. Because data scientists primarily code in Python, they can quickly begin working with Streams to create their own applications. insights from within human voice data

Speech-to-text algorithms such as those used by IBM Watson allow developers to convert spoken English into text in real time for algorithmic analysis using streaming algorithms, thus unlocking additional levels of insight. A capable speech-to-text toolkit allows developers to create applications that ingest voice, convert it to text and then, using text analytics, perform natural language processing applications. What’s more, speech-to-text capabilities are useful in other scenarios in which voice interactivity is required. For example, they can do much to enhance mobile experiences and can aid transcription of media and call center data, voice control of embedded systems and conversion of sound to searchable text. In particular, speech analytics is the key to unlocking hidden insights that can boost customer satisfaction by analyzing the wealth of information available in customer calls.

Build federated IoT applications leveraging analytics

Apache Edgent allows streaming analytics developers to create federated applications that are designed to provide the computing capabilities so important for IoT-based uses. Developers who are doing streaming analytics on the edge need the ability to manage and control IoT applications via a central console. Moreover, such applications should be able to integrate with a cognitive IoT platform, such as IBM Watson Internet of Things, to supply developers with device events and status updates and to give them the ability to send device commands without requiring them to understand MQTT-related topics and connectivity details.

Enjoy a simple view of applications

Developers also need to access to a simple view of streaming analytics application that allows them to create rules using tools such as IBM Operational Decision Management (ODM). With this the developers create rules using the same language as Industry leading ODM capabilities.

Streams already has support for ODM business rules with rules toolkit, introduced in a previous release. This capability called and ran rules in ODM. The new capability compiles the rules into Streams Processing Language for native execution, so licensing of ODM is no longer required.

All these capabilities are available in IBM Streams 4.2, which integrates them closely with IBM Watson, Apache Edgent and IBM ODM. To put them to work for you, explore IBM Streams customer success stories, then try IBM Streams 4.2 for yourself. To learn more, investigate the changing state of data and find out how IBM Streams is redefining real-time analytics. If you’re a new user, download the IBM Streams Quick Start Edition to begin your streaming analytics journey.