IoT Data Analytics

It is essential for companies to set up their business objectives and identify and prioritize specific IoT use cases

As IoT technologies attempt to live up to their promises to solve real-world problems and deliver consistent value for companies, there is still confusion among businesses on how to collect, store, and analyze a massive amount of IoT data generated from Internet-connected devices, both from industry and consumers, and unlock its value. Many businesses that are looking to collect and analyze IoT data are still unacquainted with the benefits and capabilities the IoT analytics technology offers, or struggle with how to analyze the data to continuously benefit their business in different ways such as cost reduction, improving product and services, safety and efficiency, and enhancing customer experience. Consequently, businesses still have the prospect of creating competitive advantage by mastering complex IoT technology and fully understanding the potential of IoT data analytics capabilities.

The Product Key Features and Factors to Consider in the Selection Process
To help businesses understand the real potential and value of IoT data and IoT analytics across various IoT analytics applications and guide them in the selection process, Camrosh and Ideya Ltd., published a joint report titled IoT Data Analytics Report 2016. The report examines the IoT data analytics landscape and discusses key product features and factors to consider when selecting an IoT analytics tool. Those include:

1. Data sources (data types and formats analysed by IoT data analytics)
2. Data preparation process (data quality, data profiling, Master Data Management (MDM), data virtualization and protocols for data collection)
3. Data processing and storage (key technologies, data warehousing/vertical scale, horizontal data storage and scale, data streaming processing, data latency, cloud computing and query platforms)
4. Data Analysis (technology and methods, intelligence deployment, types of analytics including descriptive, diagnostic, predictive, prescriptive, geospatial analytics and others)
5. Data presentation (dashboard, data virtualization, reporting, and data alerts)
6. Administration Management, Engagement/Action feature, Security and Reliability
7. Integration and Development tools and customizations.

In addition, the report explains and discusses other key factors impacting the selection process such as scalability and flexibility of data analytics tools, vendor’s years in business, vendor’s industry focus, product use cases, pricing and key clients and provide a directory and comparison of 47 leading IoT data analytics products.

The Product Key Features and Factors Impacting the Selection Process

IoT vendors and products featured and profiled in the report range from large players, such as Accenture, AGT International, Cisco, IBM Watson, Intel, Microsoft, Oracle, HP Enterprise, PTC, SAP SE, Software AG, Splunk, and Teradata; midsize players, such as Actian, Aeris, Angoss, Bit Stew Systems, Blue Yonder, Datameer, DataStax, Datawatch, mnubo, Mongo DB, Predixion Software, RapidMiner, and Space Time Insight; as well emerging players, such asBright Wolf, Falkonry, Glassbeam, Keen IO, Measurence, Plat.One, Senswaves, Sight Machine, SpliceMachine, SQLStream, Stemys.io, Tellient, TempoIQ, Vitria Technology, waylay, and X15 Software.

Business Focus of Great Importance
In order to create real business value from the Internet of Things by leveraging IoT data analytics, it is essential for companies to set up their business objectives across the organization and identify and prioritize specific IoT use cases that support each of the organizational functions. Companies need to ask specific questions that need to be addressed (such as “How can we reduce cost?”, “How can we predict potential problems in operations before they happen?”, “Where and when are those problems most likely to occur?”, “How can we make a product smarter and improve customer experience?”, etc.) and identify which data and what type of analysis are needed to address these key questions.

For that reason, the report examines use cases of IoT data analytics across a range of business functions such as Marketing, Sales, Customer Services, Operations/Production, Services and Product Development, as well as illustrates use cases across industry verticals including Agriculture, Energy, Utilities, Environment & Public Safety, Healthcare/Medical & Lifestyle, Wearables, Insurance, Manufacturing, Military/Defence & Cyber Security, Oil & Gas, Retail, Public Sector (e.g., Smart Cities), Smart Homes/Smart Buildings, Supply Chain, Telecommunication and Transportation. To help companies get the most from their IoT deployments and select IoT data analytics based on industry specialization, the report addresses use cases for each of the mentioned industry sectors, its benefits, and indicates use cases covered by each of the featured IoT data analytics tools.

Selecting the right IoT analytics tool that fits the specific requirements and use cases of a business is a crucial strategic decision, because once adopted, IoT analytics impacts not only business processes and operations, but also the whole supply chain and people involved by changing the way information is used, and the overall impact it has on the organization. Furthermore, it is evident that companies that invest in IoT with a long-term view and business focus are well positioned to succeed in this fast evolving area.

Building the Right Partnerships – The Key to IoT Success
IoT data analytics vendors have created a broad range of partnerships and built an ecosystem to help businesses design and implement end-to-end IoT solutions. Through the detailed analysis and mapping of the partnerships formed by IoT analytics vendors, the IoT data analytics report shows that nearly all featured IoT analytics vendors reviewed are interconnected to one or more of the sample set, as well as a list of partners from different industries.

The report reveals that the partnerships play a key role in the ecosystem and enable vendors to address specific technology requirements, access market channels, and other aspects of providing services through partnering with enablers in the ecosystem. With the emergence of new use cases and their increasing sophistication, industry domain knowledge will increase in importance.

Partner Ecosystem Map of Featured IoT Analytics Vendors produced in NodeXL

Other factors, such as compatibility with legacy systems, capacity for responsive storage and computation power, as well as multiple analytics techniques and advanced analytics functions are increasingly becoming the norm. Having a good map to find one’s way through the dynamic and fast-moving IoT analytics vendors’ ecosystem is a good starting point to make better decisions when it comes to joining the IoT revolution and reaping its benefits.

Source: http://cloudcomputing.sys-con.com/node/3892716