[Partner Blog] 9 Data Science Solutions to Drive Better Business Outcomes

16 Aug [Partner Blog] 9 Data Science Solutions to Drive Better Business Outcomes

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The applications for machine intelligence are growing in popularity within industrial organizations. The timing is right as the Industrial IoT requires a new way of thinking to extract value from industrial data. The IIoT needs a new methodology to make sense of large complex data sets that require advanced analytic capabilities not regularly exposed through mainstream BI & analytics tools. Leveraging data science to understand complex industrial data is a critical component of Bit Stew’s unique approach to machine intelligence. In this blog, find out how particular algorithms can be applied to solve real business problems.

Industrial enterprises are not your typical data analytics customers. The business problems they try to solve require an intricate understanding of legacy systems, market forces, and even weather in some cases which cannot be achieved using traditional methods of analytics. A powerful analytics framework should allow real-time analytics at the edge (device level), real-time analytics before and during the data ingestion (to remove noises, normalize the data, map to proper canonical models, etc.), and also allow to analyze and detect patterns in the ingested data and recommend appropriate actions.

In order for industrial IoT projects to successfully drive better business outcomes, industrial knowledge must be embedded in the platform. The machine learns complex patterns and connections between variety of data sources in any volume through its powerful library of algorithms and mature knowledge repository. This knowledge repository allows the platform to become more intelligent over time. The table below demonstrates particular algorithms and methods to solve business problems using data science.

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In some cases, industrial customers have a particular use case or question they want answered with their industrial data. One in particular is common to several industrial environments: the challenge of simply ingesting data coming from assets. Take the example of major commercial airlines. Ingesting data from control systems such as Full Authority Digital Engine Control (FADEC) and sensors on jet engines has long been a manual process that requires intensive human intervention. The ability to correlate events to sensor data typically takes 30 days due to manual cleansing of messy aviation data. There are some problems that are better handled by machine intelligence. Data keeps aircraft in the skies, so the latency in getting data from FADEC systems results in more downtime and a reactive approach to asset performance & maintenance.

By applying machine intelligence to this problem, Bit Stew’s MIx Core platform was able to reduce time to actionable intelligence from 30 days to real time. Using schema recognition algorithms the platform was able to mine the messy data for patterns and generate proper decoders for different FADEC packets coming from different aircrafts. Other algorithms in the Mix Core Analytics Framework (MCAF) looked at correlation between events and sensor data where flight information, asset data and external data like weather information together create a real time view of the aircraft’s performance.

Industrial data is complex, but predictable in a lot of ways that lends itself to automation. Applying data science to the exploration of this data allows industrial enterprises to accelerate the diagnosis of asset problems, discover ways to optimize operations, reduce downtime, and more.

NEW White Paper: Leveraging Machine Learning for Industrial IoT Data
To learn more, download the white paper on The Science of Industrial Data to dive deeper into what differentiates machine intelligence from machine learning and why data science is the key to solving challenges associated with industrial data.

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About Massoud Seifi, Lead Data Scientist at Bit Stew

Picture3 Massoud received his PhD in Computer Science from the University of Pierre and Marie Curie in Paris. His PhD was focused on graph theory and structural analysis of complex networks. Having software engineering and mathematics background, he has been working on different projects from Air Traffic Control to Fraud Detection. He is an expert in design and innovation of machine learning algorithms which can be served to solve challenging problems in big data analytics and visualization.