Achieve business outcomes that matter
Measure, manage, and operationalize your sustainability goals – including decarbonization, energy resources management, and reduced WAGES.
Achieve operations visibility and AI-based optimization, linking plant-floor actions to your enterprise sustainability initiatives.
A system of record to automate accurate GHG data collection, provide valuable insights, and identify ways to reduce carbon emissions
One modular solution to connect, see, control, and optimize DERs from a technical and an economic standpoint
Reduced operational costs and risks using Digital Twins, machine learning and predictive models
Increased network reliability
Advanced analytics to predict future asset and process performance for reduced variability and improved operations
Optimized asset performance to reduce risk and improve safety, reliability, compliance, and efficiency
Optimize assets and processes – from plant-level operations to the enterprise – with self-service process analytics software.
Minimized potential impact of anomalies
Comprehensive visibility of asset health for rapid situational adjustments with quality information
Streamlined mechanical integrity solution to reduce risk, maintain compliance and optimize resources
Develop, implement, maintain, and optimize asset strategies to effectively balance cost and risk
Operational visibility and analysis to reduce asset failures, control costs and increase availability
Performance Intelligence with APM Reliability is your partner in meeting your plant and fleet performance goals.
Predictive analytics software, helps prevent equipment downtime by detecting, diagnosing, forecasting & preventing emerging failures.
The AI-powered product automatically explores the space of operation of gas turbines, builds a machine learning model, and continuously finds the optimal flame temperatures and fuel splits to minimize emissions
BoilerOpt works within existing plant technology to improve boiler productivity and air-fuel ratios in a closed-loop system
Pre-built templates for equipment health monitoring, asset strategies, and process workflows
Operator rounds efficiency and operational impact
Secure and scalable data connectivity, analytics, and application services
Visualized data intelligence to make informed maintenance decisions
End-to-end digital solutions reducing costs, empowering crews, and improving the passenger experience
Addressing fuel usage, carbon emissions, airspace efficiency, predictive maintenance, and more
Increased fuel efficiency and reduced waste
Early detection of aircraft and component degradation
Reduced costs related to disruptions
Visualizations and analytics to help airline decision makers identify waste in an airspace
Fuel efficiency reports, helping airlines operate at peak safety and efficiency while reducing their carbon footprint
Operational excellence and improved safety
Outcomes that move your business forward in fuel, safety, and predictive maintenance
Analysis of multiple flights, routes, and assets across years
Reduced environmental footprint
Services and solutions to reduce vulnerability and identify, detect, prevent and protect
Turnkey solutions to reduce vulnerability and identify, detect, prevent and protect assets and systems
A globally recognized benchmark for procurement of OT secure products.
Strengthened device security across the development lifecycle
Informed decision making with data and insights from across the enterprise
Native cloud service for a data historian.
Safe and secure management and orchestration of the distribution grid
Network-level optimization with high-performing distribution power applications
Overcome foreseeable load variations
Minimized disruption of service even in extreme weather conditions
Effective management and orchestration to unlock the power of renewables and DERs
AI/ML energy market recommendations to improve profit for renewables and thermal generation assets
Increased output and energy production at times of highest demand
A common network view to ensure electrical integrity, network validity and infrastructure management
Operational efficiency and reduction in build costs while meeting regulatory regulations
Accurately model your asset network, support traceability, help assure data completeness, & support integrity management
End-to-end network connectivity modeling and data workflow management
Software designed to help grid operators orchestrate the grid
Increased efficiency and reduced costs
Secure-by-design connectivity and certification management, and faster operator response
Faster operator response and increased efficiency
Centralized visualization and configuration, digitized processes and intelligence
Full visualization and control seamlessly across devices, including phones, tablets and desktops
Best practices and proven deployment learnings
In-depth understanding of how GE Digital software can help your operations
Holistic performance management for today’s connected enterprise
Management of fast-moving processes as well as slower moving, labor-intensive jobs
Cost savings with improved manufacturing overall equipment effectiveness
Batch automation, regardless of the underlying equipment
Data analysis for quick identification of defects and better optimization of processes
Unified manufacturing data from disparate systems to better meet changing consumer demands
Procedures managed in an electronic format for consistency and predictability
Optimized production with better planning
Improved throughput with greater efficiency and lower costs
Materials to help you better understand GE Digital software and its robust functionality
Integrated solutions for improved efficiency and sustainability while supporting business growth
Mobile operator access to essential on-site HMI monitoring and control functions
Enable remote staffing, flexible resourcing and centralized monitoring across facilities
Boost worker productivity, automate tasks, keep safety first
Energy management for the zero carbon grid
Reliable mobilization of network assets to ensure maximum transmission of energy from multiple sources
Integrated solutions suite for energy market management
Decentralized data collection, data volume handling, and remote management
Getting the most benefit out of digitization and industrial IoT
Services that deliver best-in-class results
Rapid digital transformation wins based on industry-proven value cases and ROI
Best practices for your industrial processes to help build and maintain operational resilience
GE Digital’s expert service and support teams create value and deliver on business objectives
Expert service and support teams to maximize the benefits from your IIoT software
Improved efficiencies, optimized production and quality and reduced unplanned downtime
Increased reliability and availability, minimized costs, and reduced operational risks
Increased value from your equipment, process data, and business models
Facilitate documentation between airlines and lessors
Reduced costs related to disruptions with real-time visibility
The cornerstone of your journey to operational excellence
Operational excellence including improved reliability, reduced costs and managed risk
GridOS, the first grid software portfolio designed for grid orchestration
Reduced operational costs and risks using predictive models
Enhanced overall situational awareness
Field-connected operations and management
One modular solution that enables grid operators to connect, see, control, and optimize DERs from a technical and an economic standpoint
Reduced operational and new build costs and improved field inspection productivity
A holistic picture of the grid, reducing cost and complexity from traditional inspection approaches
Optimized operations to best meet changing consumer needs
Reduced variability and improved operations.
In-depth understanding of our software and its functionality
A clear a path to operational transformation
Maintain consistent quality and reduce cost per ton
Optimized costs and improved reliability while reducing risk to keep your teams and communities safe
Streamlined end-to-end operations driving high-volume, high-quality production
GE Digital software is the backbone of modern plant operations
Improved reliability, increased availability, and reduced O&M costs
AI/ML to make your gas turbine's fuel and air controls smarter
Increase energy production at times of highest demand without costly maintenance adders or adversely impacting the maintenance interval
Operate from anywhere with secure remote/mobile operator controls
Inclusive outsourcing services that deliver best-in-class results
Achieve digital transformation
Expert service teams to maximize the benefits from your IIoT software
Reduced costs, lower risk, and faster response times
Analytics to predict future asset and process performance for reduced variability & improved operations
A common network view to ensure integrity, network validity and infrastructure management
Mission critical software to better operate, optimize and analyze your work to deliver results
Locate the best partners to meet your needs
Digital transformation acceleration
Technical and domain expertise that complements GE Digital’s industry leading applications
Assistance to accelerate your digital transformation and put your industrial data to work
Deep domain knowledge and technical expertise
Product training, industry education, and rigorous certification programs
More efficient and secure electric grid, greater sustainability and waste reduction
Solutions for today, scale for tomorrow
Increased reliability and reduced reactive maintenance leading to higher efficiency and reduced costs
Using Digital Twin blueprints, GE's Industrial Managed Services team monitors 7,000+ global assets
Understanding of the latest thought leadership that can be applied to your operations
Understand how our software and services help our customers solve today's toughest challenges
Experienced team dedicated to customer success
Success stories and product updates from the world of Electrification Software
Analyst and third-party expert opinions of Electrification Software and our software and services
White papers, product overviews, and other content to help you put your industrial data to work
Experience in leading edge software development and business working with best-in-class leaders
Understand how Electrification software and services helps our customers solve today's toughest challenges
Blog
In my last post, I discussed the role of the data science practice in an enterprise and introduced the idea of building data products. Now, I want to define an industrial data product, provide reasons for building industrial data products, and share key insights for developing industrial data products.
Data scientists use data to generate insights. So, in its simplest definition, a data product incorporates the process of generating insights into a product. In other words, data products put data science processes in production to transform data and continuously create value. The key is to understand how typical enterprise software products are made.
Enterprise software products are typically developed based on business process alone. Data products, on the other hand, are driven at its core by data generated by a process, users, or machines. And industrial data products are data products developed for the industrial domain. This distinction is important because consumer companies are drastically different than industrial companies due to many factors—such as data quality, data access rights, adoption audience, cost of false positives, type of domain knowledge, and the product team composition. I’ll cover some of these differences further in the post. In general, industrial data product development requires change in organizational thinking, change in design thinking, and the ability to engineer products focusing on features that are data and user-centric. Therefore, building industrial data products are not just hard, but can become frustrating if your industrial organization does not have the right people and culture in place to understand how to build these products.
Key lessons to keep in mind when you aim to build a data product include:
Since data is at the core, you have to know how to collect the right data and process it to make it useful. In order to get this right, you will need to perform some (often many) steps to understand, clean, and transform data, which is typically the case with any data science process. The difference now is that you are incorporating this process in production to feed the most useful data to the product features by using the right “plumbing.” Before you start this, you have to acknowledge that data is messy. The quality of data from both the industrial assets and processes, such as manufacturing processes or services operations, are going to be messy to begin with. This is the first and most important problem that you need to tackle. It will take significant time (more than 70%) to make the data in a usable form and then build the pipelines in order to feed the data to any dashboard or analytics you want to develop.
In any data science activity, a typical outcome is a collection of insights that is useful to the business or to a set of stakeholders. When you build data products, you are essentially putting the process of generating those insights into production. For any offline data science activity, a team of data scientists typically explain how they generated those insights or predictions through a combination of various statistical and/or machine learning techniques. But in a data product, when the insight generation and visualization process is automated, a lot of onus is on the consumer of that insight to interpret and understand the reasoning behind it. This is a big challenge we face when building industrial data products. For most industrial customers, the ability to use data and analytics to make decisions hinges significantly on their comfort and ability to believe the insight or prediction the system is generating. For example, if we developed machine-learning models to predict machine failure or forecast performance degradation, we’d often be asked to explain how the algorithms generated those predictions. Many of the machine learning and deep learning techniques data scientists often use are black box techniques, which can become challenging to explain how and why the models are producing the outputs. So, while developing industrial data products, the adoption and usage efficiency greatly relies on how easily explainable and user-centric the insights are. This, in turn, creates a requirement to balance between accuracy (eg., advanced black-box models) and interpretability (eg., straight-forward linear models).
Even though, conventionally, it is expected for industrial products to be complex, simplification should be your best friend if you want to build successful industrial data products. There are two main aspects of simplification. First, is about starting with simple industrial data products. Don’t aim to start building a super complex predictive maintenance solution with the most advanced machine learning algorithms. For example, while building an IIoT solution for a customer, a requirement was to develop predictive algorithms to detect equipment failures from sensor data. Before we could do that, we developed simple analytics to clean sensor data, align it, and then count unique events. Building a simple counter of events from a handful of sensor streams took a good deal of iterative process and collaboration across teams. The customer may easily accept the iterative process, but to truly adopt it, try as many things as possible early, and still make progress is still hard. The key is to realize that it takes time to make a data product mature. You go from using “version A”, getting feedback from “version A”, generating more data from “version A” to producing a better “version B.” As long as you are doing this in production, you will find the gaps in your pipeline, you will plug those gaps, and make a better “version C.” It’s a cycle—rinse and repeat.
It is not a surprise that the success of any product development process relies heavily on the right product team. The right mix of people, skillset, and above all, a right mindset makes all the difference. Data scientists are often considered to work disjoint from product or engineering teams to perform all tasks from EDA to model development to validation. It is often expected of data scientists to simply ‘hand over’ the models and relevant codebase to the engineering teams. There has been growing recognition that data scientists should be a more integral part of product and engineering teams. Typically, data scientists are interacting with the customers while also rapidly iterating on experiments with data—making them immensely useful to guide product development. If you are providing data science services to your customers, it is exponentially beneficial to keep iterating the integrated end solution that will be deployed and drive analytics alongside solution development with a cross-functional team with data scientists and engineers working together. There is another key difference from the perspective of domain knowledge while developing industrial data products. The requirement and necessity of having domain knowledge with either the data scientist and/or the product manager is much higher. It is absolutely critical to have the right people with required domain knowledge for the success of an industrial data product.
I’ll leave the readers with the two most important qualities required in a team responsible for building data products. First is the ability to deal with ambiguity. If you go in trying to perfect your understanding of the product, what the customer wants before you start, you will fail. This is not to be confused with setting the initial vision of the product. Secondly, the team should be comfortable experimenting and iterating as much as required. With every experiment, measure critically and adapt not just your data product but also how your team should be organized.
Putting industrial data to work.
GE Digital is a leading industrial software company - transforming how our customers solve the toughest challenges by putting industrial data to work. We bring simplicity, speed, and scale to our customers' digital transformation with industrial software that delivers breakthrough business outcomes. By partnering with our customers to transform industry, our software is enabling power-generating assets to be more efficient and reliable, the electrical grid to be more secure and resilient, flights to be more efficient, and manufacturing to be smarter and reduce waste.
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