Tuesday, 17 October 2017 14:58

Combining OLTP , OLAP and Real-Time Data Streaming to Realize the Promise of IoT

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dramatically improve their decision-making capabilities by investing in IoT analytics.

To reap these benefits, companies must be able to ingest real-time streaming data from multiple sources, in both structured and unstructured formats, and easily combine it with business process data. In tandem, they must have an in-memory analytics data platform so they can quickly process and gain insights from IoT data. A combination of online transaction processing (OLTP) and online analytical processing (OLAP) is a must for managing the streaming data in real time.

An IDC Perspective, SAP HANA Accelerates Real-World IoT Analytics, looked at how two companies are using SAP HANA to perform stream data processing and analytics to continuously increase the agility and robustness of their IoT investments.

“One of the most important aspects in delivering value from IoT analytics is the ingestion and processing of streaming data in real time or near real time,” says Chwee Kan Chua, vice president of IDC’s Big Data and Analytics and Cognitive/AI Computing Practice. “As such, the SAP HANA in-memory platform is one of the select few solutions in the market that can help deliver on the promises of business value from IoT initiatives.”

The report’s central theme is real-world use cases that show the compelling business value that comes from combining OLTP and OLAP for analytics. These two SAP customers are already reaping the rewards adding SAP HANA to underpin their IoT environments.

Teknoleum Improves Plant Management, Output, and Cost Savings with SAP HANA

Teknoleum specializes in modernizing and streamlining the supply chain IT landscape for oil and gas, utilities, and mining companies. The company, also involved in solar photo voltaic (PV) power generation, had been looking for a way to meet related challenges, including minimizing soiling losses to its rooftop installations, lack of central monitoring for each installation and inaccurate energy generation forecasting.

Teknoleum chose SAP HANA to ingest and process PV, historical plant performance and weather data. The company relies on SAP HANA for real-time calculations and user-friendly visual analytics accessed via role-based dashboards. Users can track key performance indicators (KPIs) in real time from a centralized operational view across multiple PV plants from any device. The company was also impressed with the out-of-the-box predictive libraries for predictive maintenance and energy generation forecast algorithms and its ability to integrate and replicate data from different data sources.

SAP HANA’s analytics delivered several key benefits. First, the analytics empowered users, providing plant technicians and managers with real-time visibility into plants to activate proactive resolution and processes. Second, the insights led to increased plant output and cost savings. Finally, SAP HANA’s analytics capabilities led to revenue increases, better grid management, and increased trading margins.

Greenojo Realizes 35% Costs Savings

A technical consulting firm that focuses on IT solutions for energy and other industries, Greenojo offers Upstream Data Analyzer (UDA) to oil companies needing insights from an integrated analytics platform. The solution meets many challenges oil companies face, including nonproductive time when drilling operations are halted, significant delays for data to be relayed between drilling sites to rig analytics centers, long well and drilling log processing time, and an inability to extract insights from multiple data sources.

Greenojo built its UDA solution based on SAP HANA. Oil and gas customers rely on UDA to ingest and process real-time structured and unstructured data from multiple sources like seismic, well, and drilling logs, reservoir, and production logs. The UDA solution predicts subsurface characteristics, feasibility of hydrocarbon zones and drilling and completion operations.

The company’s UDA solution succeeded in reducing nonproductive time by up to 65%, accelerated time-to-production by minimizing data bottlenecks and afforded up to a 35% savings on data processing costs. UDA now provides instant and on-demand predictive model generation and a scalable framework to manage analysis for large data sets from multiple sources and increased efficiencies.

Recommendations from Analysts

IoT investments can yield tremendous benefits and drive value for organizations and their customers. IDC recommends that companies work with a partner that can bring business and technical expertise—and identify a solution that minimizes latency and provides high-performance data integration. The rewards are enormous for those that get it right.

Download the full report by IDC here.

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