Re-evaluating data management in the generative AI age

Generative AI has altered the tech industry by introducing new data risks, such as sensitive data leakage through large language models (LLMs), and driving an increase in requirements from regulatory bodies and governments. To navigate this environment successfully, it is important for organizations to look at the core principles of data management. And ensure that they are using a sound approach to augment large language models with enterprise/non-public data.

A good place to start is refreshing the way organizations govern data, particularly as it pertains to its usage in generative AI solutions. For example:

◉ Validating and creating data protection capabilities: Data platforms must be prepped for higher levels of protection and monitoring. This requires traditional capabilities like encryption, anonymization and tokenization, but also creating capabilities to automatically classify data (sensitivity, taxonomy alignment) by using machine learning. Data discovery and cataloging tools can assist but should be augmented to make the classification specific to the organization’s understanding of its own data. This allows organizations to effectively apply new policies and bridge the gap between conceptual understandings of data and the reality of how data solutions have been implemented.

◉ Improving controls, auditability and oversight: Data access, usage and third-party engagement with enterprise data requires new designs with existing solutions. For example,  capture a portion of the requirements that are needed to ensure authorized usage of the data. But firms need complete audit trails and monitoring systems. This is to track how data is used, when data is modified, and if data is shared through third-party interactions for both gen AI and non-gen AI solutions. It is no longer sufficient to control data by restricting access to it, and we should also track the use cases for which data is accessed and applied within analytical and operational solutions. Automated alerts and reporting of improper access and usage (measured by query analysis, data exfiltration and network movement) should be developed by infrastructure and data governance teams and reviewed regularly to proactively ensure compliance.

◉ Preparing data for gen AI: There is a departure from traditional data management patterns and skills which requires new discipline to ensure the quality, accuracy and relevance of data for training and augmenting language models for AI use. With vector databases becoming commonplace in the gen AI domain, data governance must be enhanced to account for non-traditional data management platforms. This is to ensure that the same governance practices are applied to these new architectural components. Data lineage becomes even more important as the need to provide “Explainability” in models is required by regulatory bodies.

Enterprise data is often complex, diverse and scattered across various repositories, making it difficult to integrate into gen AI solutions. This complexity is compounded by the need to ensure regulatory compliance, mitigate risk, and address skill gaps in data integration and retrieval-augmented generation (RAG) patterns. Moreover, data is often an afterthought in the design and deployment of gen AI solutions, leading to inefficiencies and inconsistencies.

Unlocking the full potential of enterprise data for generative AI

At IBM, we have developed an approach to solving these data challenges. The IBM gen AI data ingestion factory, a managed service designed to address AI’s “data problem” and unlock the full potential of enterprise data for gen AI. Our predefined architecture and code blueprints that can be deployed as a managed service simplify and accelerate the process of integrating enterprise data into gen AI solutions. We approach this problem with data management in mind, preparing data for governance, risk and compliance from the outset. 

Our core capabilities include:

◉ Scalable data ingestion: Re-usable services to scale data ingestion and RAG across gen AI use cases and solutions, with optimized chunking and embedding patterns.

◉ Regulatory and compliance: Data is prepared for gen AI usage that meets current and future regulations, helping companies meet compliance requirements with market regulations focused on generative AI.

◉ Data privacy management: Long-form text can be anonymized as it is discovered, reducing risk and ensuring data privacy.

The service is AI and data platform agnostic, allowing for deployment anywhere, and it offers customization to client environments and use cases. By using the IBM gen AI data ingestion factory, enterprises can achieve several key outcomes, including:

◉ Reducing time spent on data integration: A managed service that reduces the time and effort required to solve for AI’s “data problem”. For example, using a repeatable process for “chunking” and “embedding” data so that it does not require development efforts for each new gen AI use case.

◉ Compliant data usage: Helping to comply with data usage regulations focused on gen AI applications deployed by the enterprise. For example, ensuring data that is sourced in RAG patterns is approved for enterprise usage in gen AI solutions.

◉ Mitigating risk: Reducing risk associated with data used in gen AI solutions. For example, providing transparent results into what data was sourced to produce an output from a model reduces model risk and time spent proving to regulators how information was sourced.

◉ Consistent and reproducible results: Delivering consistent and reproducible results from LLMs and gen AI solutions. For example, capturing lineage and comparing outputs (that is, data generated) over time to report on consistency through standard metrics such as ROUGE and BLEU.

Navigating the complexities of data risk requires a cross-functional expertise. Our team of former regulators, industry leaders and technology experts at IBM Consulting are uniquely positioned to address this with our consulting services and solutions.

Source: ibm.com

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Trustworthy AI helps provide equitable preventative care for diabetics

There are over 30 million people in America who have diabetes, and people with diabetes need to remain vigilant about their health. They need the extra attention and resources provided by their healthcare systems because, unfortunately, around 38% to 40% of people with diabetes end up visiting the ER due to complications. Healthcare organizations – both providers and payers – across the nation are seeking transformative new ways to render quick aid to vulnerable members. For many, part of the solution is trustworthy AI.

A large North American healthcare organization uses an AI powered solution to help them identify vulnerable members who can benefit from timely intervention. The organization had established a community health program, with units ready to reach out to member communities to promote better health and improve health outcomes. They needed a system to identify the people who most needed the help. If they could deliver preventative care, they could also reduce member trips to the ER and help members enjoy a better quality of life, while reducing costs for the company and optimizing hospital staff and equipment.

Healthcare organization uses technology to identify members for proactive care

The goal was to predict member groups that were at risk 30–60 days before hospitalization would be necessary, to give community health units time to intervene. In addition, they needed demographic data to ensure appropriate care for the community in need. For example, if a non-Spanish-speaking unit member reaches out to a primarily Spanish-speaking community, the odds of successful intervention will be lower. The healthcare company understood that it’s not enough to build an accurate machine learning model; they needed to connect it to the human experience.

To accomplish this, the organization brought together various data sets, analyzed, and combined them, then built predictive Machine Learning (ML) models to identify their most at-risk members. At this step in the journey to AI many businesses run into trouble with their AI initiatives. And with good reason: not all AI systems are created with the proper ethical guardrails in place. Organizations need to be able to trust their data science outcomes. An AI system, especially one with an impact on health, must be fair, explainable, robust and transparent. The AI must be trustworthy.

A lot can go wrong when an organization decides to operationalize AI, and avoiding undue risk is a significant part the process. To mitigate that potential risk, many business leaders are finding success using proven data fabric architecture patterns and adapting those patterns to their specific organizational processes.

What healthcare is doing with data fabric and AI to mitigate risks

A data fabric architecture provides visibility and insights into data, enhanced access, control over your data and advanced protection and security. Here’s how that North American healthcare company achieved its goals using data fabric.

The first step was ensuring they were using relevant data sets. They started with claims data. Demographic data as well as diagnostic information from the patient’s past medical visits were then combined. To complete that story, the organization brought in socio-economic data via Social Determinants of Health (SDOH) datasets.

After connecting all the different data sources — claims data, diagnostics data, socio-economic data, and demographics data — with appropriate rules and policies in place within a data fabric using IBM Cloud Pak for Data, a team of data scientists built Machine Learning models, following best practices for the AI/ML lifecycle.

Just as being accurate in your predictions is important, it is equally important that the predictions be equitable. The organization must have confidence that the predictions will cover a diverse member population, to ensure quality care reaches everyone. Guardrails were put in place to check for and catch bias at various stages of the AI/ML lifecycle. This starts with checking for bias in the data set, checks during the model build and validation stages, and ongoing bias monitoring after the model is deployed. Similar guardrails were built to monitor quality and data drift as well as to generate explanations for the model predictions.

How trustworthy AI provides better help

Using architecture patterns for Data Fabric and Trustworthy AI, the North American healthcare organization can ensure care that is equitable across diverse member races and social classes. The solution can identify at-risk members who need intervention, the automation saves time, increases efficiency, and provides a pathway to get members help when they most need it. Additionally, community health workers have access to better information about the communities they serve, making it easy for them to explain why members are receiving a visit, which builds trust in the process and maintains a good relationship between the organization and members.

IBM Expert Labs offers a variety of architecture patterns mapped to successful use cases and common entry points like Data Governance and AI Governance/Trustworthy AI. The healthcare organization used such an architecture pattern to help them better address their members’ health and well-being. What could your business use it for?

Source: ibm.com

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How to stay ahead of ever-evolving data privacy regulations

Enterprises are dealing with a barrage of upcoming regulations concerning data privacy and data protection, not only at the state and federal level in the US, but also in a dizzying number of jurisdictions around the world.

Kicked off several years ago by the groundbreaking introduction of the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), the regulation and compliance trend is only going to intensify. In August the Federal Trade Commission (FTC) released an Advance Notice of Proposed Rulemaking (ANPRM) titled Commercial Surveillance and Data Security that encompasses a wide range of data protection and privacy issues, including data monetization models, discrimination and algorithmic biases and data security, to name a few.

As these types ANPRMs continue to be released and regulation swiftly catches up to innovation, a recent Gartner survey predicts that 75% of the world’s population will have its personal data covered under modern privacy regulations by the end of 2024.

At IBM’s recent Chief Data and Technology Officer Summit on data privacy, I spoke with some of the world’s top data leaders about the two-pronged challenge they’re now facing: ensuring that data policies and practices meet regulatory demands, while also continuing to innovate with new technologies.

We agreed there is a way to navigate this complicated landscape and maintain a competitive advantage that delivers business value. The journey starts with having a multimodal data governance framework that is underpinned by a robust data architecture like data fabric. This framework can create a standard approach for meeting regulatory compliance while allowing for customization to address local regulations and being proactive when handling new regulations.

Adopting a privacy-centric approach built around a data fabric

A data fabric is an architectural approach that simplifies data consumption across a diverse and distributed landscape, while adhering to data privacy requirements. Think of a data fabric as a single pane of glass that creates visibility across an enterprise. By doing so, it greatly reduces the complexity of managing disparate regulations worldwide. What’s more, a data fabric can automate data governance and security by creating a governance layer across the lifecycle.

To understand how a data fabric helps maintain compliance to privacy regulations, it’s helpful to look at some essential elements of that single pane of glass.

Build a foundation using a common catalog and metadata

Building a data fabric starts with creating visibility using a data catalog, which is an inventory of an organization’s information assets. It lets appropriate parties, such as the company’s chief data analyst, know what the data is and where it resides. Without a data catalog, data can remain hidden or unused and become impossible to manage.

A proper data catalog has a common taxonomy that helps everyone communicate more effectively and solves a common challenge of data integration—different data sets describing the same terms differently. This is important for data privacy: If the wrong term is used, data that should be limited in access might accidentally be made available to the whole business.

Similarly, active metadata — data about data — is at the heart of how a data fabric delivers on privacy for the same reason as a common data catalog. If you don’t know the details about your data, how can you truly say who is meant to see it or how you can use it? In the context of a data fabric, think of metadata as an augmented knowledge graph displaying the network of data across an entire enterprise, along with the conditions that apply to these sets of data.

Operationalize data privacy through automation

Once metadata has been created, it can be tagged, signifying which data is sensitive, limiting who has access to it and so forth. Then intelligent automation begins.

Automated metadata generation is particularly important for access and privacy. Consider, for example, an enterprise that wants to bring in a new data set containing transaction information such as item descriptions, quantity purchased, name, address and credit card number. When this data set is ingested, automated tagging labels the item descriptions and quantity as general transaction data, the name and address as personal data, and the credit card number as financial data. This tagging allows policy enforcement at the point of access. If business users access the data set, they can see the general transaction data, but the personal and financial data is automatically made anonymous.

Govern data and allow self-service consumption

While many of the regulations coming down the pike will be similar or even identical, how they are enacted will look very different across countries and regions. The challenge lies with demonstrating compliance to regulators while providing business users with a way to easily access the information. Otherwise, compliance creates a speed bump for innovation. That’s where the self-service element plays a critical role.

While self-service suggests a lot of freedom, the data fabric must include multimodal governance, allowing only certain people to access that data. Again, that single pane of glass will bring together the privacy and the security aspects at a single access point, while offering users an easier way to serve the data they want accessible to others. The ability to conduct real-time monitoring and audits helps secure the systems and comply with regulations, but it also helps the business mitigate data loss through breaches and keep models accurate.

Find your holistic data privacy and security solution by getting started with a data fabric strategy.

To hear more from data leaders around privacy, watch the replay of our CDO/CTO Summit series and attend our upcoming in-person CDO Summit.

Source: ibm.com

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IBM continues advancing disease progression modeling and biomarkers research using the latest in AI

New research by IBM and JDRF published in Nature Communications advances AI’s ability to better predict onset of Type I diabetes.

Type 1 diabetes (T1D) is an autoimmune disease that can strike both children and adults. It can leave those afflicted with difficult, life-long disease management issues and potentially devastating long-term complications such as kidney failure, heart attack, stroke, blindness, and amputation.

There is no prevention or cure for this disease, and rates of T1D have risen steadily over recent decades, making research on prevention and early detection increasingly critical.

Last year, IBM Research highlighted previous related research conducted in collaboration with JDRF and five academic research sites which form the T1DI Study Group: in the US (DAISY, DEW-IT), Sweden (DiPiS), Finland (DIPP), and Germany (BABYDIAB/BABYDIET). That work advanced insights into development of biomarkers associated with the risk of T1D onset in children and ultimately found that the number of islet autoantibodies present at seroconversion, the earliest point in autoimmunity development, can reliably predict risk of T1D onset in young children for up to 10 to 15 years into the future.

Now, IBM Research has another important milestone in this field of research. This week, Nature Communications published new research by the T1DI Study Group which shows that the progression of Type 1 diabetes from the appearance of islet autoantibodies to symptomatic disease is predicted by distinct autoimmune trajectories. In this work, we added a unique data visualization tool – DPVis – to the AI and machine-learning tools IBM Research has been developing for disease progression modeling (DPM Tools). This has allowed us to unlock entirely new insights from the study data that may ultimately help refine how we understand the impact of islet autoantibodies on the development of T1D, thereby improving our ability to predict onset of the disease. 

These new tools allowed us to unlock entirely new insights from the study data that may ultimately help refine how we understand the impact of islet autoantibodies on the development of T1D.

As we previously showed, the presence of multiple islet autoantibodies at seroconversion increase risk of T1D, but they may not occur consistently over time — and a patient may have different combinations of antibodies at different points in time. Our previous research showed that the implications of these changes were unclear, so we set out to address this by analyzing the complex patterns of antibodies that occur over time instead of at a single point in time. In doing so, we identified three distinct trajectories, or “pathways,” associated with varying degrees of risk, each of which was comprised of multiple distinct states.

The combination of AI and data visualizations made it possible to put researchers in the loop for this large-scale, long-term collaboration by providing a common framework for our evolving understanding of how biomarkers influence a patient’s journey toward disease onset.

This research could eventually make it easier to identify at-risk children whose families can learn about the symptoms of T1D, allowing early diagnosis. Unfortunately, many today are not diagnosed until they have progressed to diabetic ketoacidosis, a life-threatening condition with potential long-term negative health effects. In addition, at-risk children can participate in clinical trials focused on delaying, and possibly preventing, the onset of T1D.

Broader efforts on disease progression modelling and biomarker discovery

Our work on T1D is just one part of a broader mission at IBM Research to develop AI technologies such as the DPM Tools to advance scientific discoveries in healthcare and life sciences. In addition to JDRF, we collaborated with other foundations that brought in deep commitment and scientific expertise, notably CHDI Foundation for Huntington’s disease.

Symptoms of Huntington’s typically begin to manifest between ages 30 and 50 and worsen over time, ultimately resulting in severe disability. While there is currently no treatment available to slow the progression of the disease, there are medications that are used to treat specific symptoms. Unfortunately, most have side effects that can have a negative impact on quality of life for patients with Huntington’s.

CHDI and IBM have been engaged in collaborative research along with other academic institutions for several years, addressing multiple research questions that have spanned disease progression modeling, brain imaging, brain modeling, and molecular modeling.

Most recently, this collaboration created another important paper, describing nine disease states of clinical relevance discovered using our suit of DPM Tools. The ability to derive characteristics of disease states and probabilities of progression enabled by models like these could accelerate drug development through the discovery of novel biomarkers and improved clinical trial design and participant selection.

This work was published in Movement Disorders the official journal of the International Parkinson’s and Movement Disorder Society, and the significance of our work was highlighted in a Nature Reviews Neurology “In Brief” section. We also carried out similar work on Parkinson’s disease in collaboration with the team at the Michael J. Fox Foundation which was published in The Lancet Digital Health.

While the specifics vary, these three conditions all share certain characteristics, specifically a profound, life-long impact on the lives of patients and their families and the fact that they are all highly complex conditions, with progression pathways that are difficult to assess, characterize and predict.

Looking to what’s ahead

Modern science is a team effort, but nowhere is this truer than in healthcare where breakthroughs in understanding disease and developing new treatments, require collaboration among research teams across numerous disciplines.

IBM has been actively convening, coordinating, or participating in such inter-disciplinary teams, resulting in clinically important findings and high-impact journal publications. The three projects highlighted here stand out as exemplars of our approach to scientific discovery through collaboration with communities of discovery. Through this work, we have established an ecosystem of reusable methodologies, models and datasets that is already being applied to study other pathologies and that will be used to broaden the scope of our work in the future.

Source: ibm.com

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How to prioritize data strategy investments as a CDO

My first task as a Chief Data Officer (CDO) is to implement a data strategy. Over the past 15 years, I’ve learned that an effective data strategy enables the enterprise’s business strategy and is critical to elevate the role of a CDO from the backroom to the boardroom.

Understand your strategic drivers

A company’s business strategy is its strategic vision to achieve its business goals. Data that can be managed, protected, and monetized effectively will provide insights into how to achieve those goals. A CDO works in collaboration with senior executives to steer a business to its strategic vision through a data strategy.

Strategy environments contain complex moving parts, points of view, and competing needs, all working toward three goals:

◉ Growing the top line by improving revenue growth

◉ Expanding the bottom line by making operations more efficient

◉ Mitigating risk

A CDO’s priority is not just to learn the strategic needs of the business and senior leadership, but also to implement a data strategy that helps leaders achieve their goals faster and embrace data as a competitive advantage. When prioritization of these goals is decided and agreed upon by all, the enterprise can more easily achieve true alignment, resulting in a collaborative, data-driven environment.

Strategic alignment also ensures that competing day-to day responsibilities will not challenge the CDO role. Quick wins and fighting fires are a part of the job, but it is only when they are in service of an enterprise-wide supported strategy that a CDO will have a comprehensive impact on a business.

The evolution of IBM’s Global Chief Data Office (GCDO) strategy

When I joined IBM in 2016, our business strategy centered on hybrid cloud and AI. As a result, I could align and evolve our data strategy with that focus going forward. For example, how do we grow revenue in AI if many leaders don’t fully understand what an AI enterprise looks like?

At IBM, we embarked on a data strategy to transform the enterprise into a data-first business, with AI infused into every key business process. Our data insights sharpened our definition of what AI meant to an enterprise, which also fed directly back into our business strategy. Thus IBM, itself, served as client zero and became a showcase of our solutions in the data and AI space.

In terms of implementing that strategy, we set several key pieces in place, such as:

◉ A hybrid distributed cloud and AI platform

◉ A robust understanding of our DataOps pipeline

◉ Governance with a focus on transparency to instill trust

◉ A data-literate culture

All these pieces worked together to set us up for a successful strategy pivot in 2021.

IBM’s data strategy aligns to revenue growth in 2021

Focus

From 2019 to 2021, our GCDO successfully aligned our data strategy to top-line growth with a strong focus on revenue. Sharpening our focus resulted in more than doubling our contributions to enable revenue growth, for our product sales and consulting teams, over the last three years. This year – 2022 – we are on track to increase our contribution by 150%. These additional revenues accrue to all our major brands and channels: hardware, software, business services and ecosystem.

Align

Aligning our data strategy to support revenue and profit was a smooth transition because our central GCDO acts as an extension of all lines of IBM’s businesses and functions.  We have assigned data officers throughout all major business units, and we meet with them regularly to ensure strategic alignment.

Discover

Another part of our pivot was an education and mindset shift to design thinking. We worked directly with the people involved in the end-to-end process, an often underused step to transformation. The power of design thinking surfaced pain points directed to data, and it provided new opportunity benefits that rippled out to teams across the enterprise.

2022 and the future of IBM data strategy investments 

 Our future data strategy will maintain a foundation of top-line revenue focus. Looking forward we are excited to additionally focus on using the power of a data fabric, improving user experience, and tapping deeper into our ecosystem of partnerships.

Data fabric and user experience

A data fabric architecture is the next step in the evolution of normalizing data across an enterprise. Its untapped potential provides an exciting opportunity to expand within our own data efficiencies and strategy.

A data fabric is an architectural approach that automates data discovery and data integration, streamlines data access and ensures compliance with data policies regardless of where the data resides. A data fabric leverages AI to continuously learn patterns in how data is transformed and used, and uses that understanding to automate data pipelines, make finding data easier, and automatically enforce governance and compliance. By doing so, the data fabric significantly improves the productivity of data engineering tasks, accelerates time-to-value for the business, and simplifies compliance reporting.

It is an exciting time for the future of data. We can now mine the capabilities of a data fabric architecture to provide a more positive user experience that gets data into the hands of those who need it most with trust, transparency, and agility. The significance of a data fabric architecture is magnified with the emergence of the virtual enterprise and ecosystem partnerships.

Ecosystem partnerships and IBM as a living lab

In 2022, the IBM GCDO strategy also includes an increasing attention to our business partnerships, a growing space in the data field. Leveraging an ecosystem of partners with complimentary technologies can bring solutions to clients faster.

Additionally, the massive, heterogeneous, and innovative environment at IBM allows our GCDO to focus on solutions as part of a living lab. Acting as our own power user, we can test our solutions at scale to consistently provide a roadmap of insights back into our own products and partnerships. Our current partnership with Palantir showcases how we do this at scale.

Data and leadership as an ongoing conversation

When data strategy is prioritized, data can govern processes as well as augment the leadership experience. As a CDO whose role is that of change agent in the enterprise, I will continue to shape strategic conversations with leadership. And as we move further into 2022, our data strategy investments will continue to evolve alongside our offerings.

Source: ibm.com

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Building a platform of innovation to transform golf data into predictive insights

The Masters Tournament is steeped in tradition. From Amen Corner to Butler Cabin, it seems everywhere you look at Augusta National Golf Club, history is staring back at you. But the Masters has another, more forward-looking, tradition: innovation.

Since 1934, the Masters has pushed the boundaries of innovation in golf, from low-tech inventions like the under-over scoring system, to award-winning technology like serving up every shot, from every player, on every hole through the Masters app. And to help the Masters continue to define the future of digital experience in sports, IBM Consulting developed a next generation “platform of innovation.”

The Masters has always had a clear and consistent vision for the digital experience on Masters.com and the Masters app. Among other things, they want to bring digital “patrons” closer to the Tournament with meaningful insights harvested from data. To do this, IBM and the Masters Digital team worked closely using the IBM Garage™ methodology, a way of co-creating solutions to a variety of business problems. The teams constructed a powerful platform that uses hybrid cloud and AI technologies to transform vast quantities of data into insights.

The result is a digital experience that generates player insights that bring patrons closer to the players they love. Using IBM Cloud Pak® for Data® and Red Hat® OpenShift® to manage the flow of structured and unstructured data, the platform applies the natural language processing capabilities of IBM Watson to analyze millions of statistics and articles, identifying and delivering insight right to the player pages in the app.

In addition, the teams built AI models that could analyze six years of historical Masters data on hundreds of players and more than 120,000 shots. These models generate predictions of every player’s score in every round. Users of the app can then use those projections to help select their Masters Fantasy foursome.

These new features do more than enhance the fan experience. They also automate critical elements of the editorial workflow for the Masters Digital team, which allows them to scale their capabilities and focus their attention on the most urgent, creative work.

These new features join other recent fan favorites, like My Group, Track, and the AI-generated Round in Under Three Minutes, all of which make the Masters digital experience one of the best in all of sports. And they extend the tradition of partnership between IBM and the Masters.

Source: ibm.com

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Fighting for fairer sentences with data and AI

I’ve always been excited about artificial intelligence and the potential for it to enhance everything – whether that is in the workplace or in society. So when the Call for Code for Racial Justice initiative emerged in 2020 I just felt like I had to get involved in the AI-based projects that were proposed by the Black community in IBM and their allies. Of all the projects the Black community in IBM incubated, I was attracted to Open Sentencing as it had a close connect to the voluntary work I do around community policing. At its core, this solution is about using data to highlight where there are disparities in the judicial system: where individuals may face harsher sentencing purely based on the color of their skin. The initial data we obtained showed that Black people are more likely to be charged with a higher sentence for petty crime than those from other communities. We realized that we would be able to present this data through a dashboard – but where could this information have the biggest impact?

Finding the right end user

We originally thought that judges would be the best recipients for this data when making decisions on the sentences they pass down. However, we realized there are many stages in the criminal justice system before a trial even begins, and it would make sense for us to work earlier in the process, potentially even being able to stop many of these cases from getting to trial.

Public defenders are involved from the outset and can petition to stop cases going to trial, especially if they can make the case that a Black defendant may be prejudiced by the system. So we focused our efforts on building a dashboard specifically for public defenders. We conducted design thinking workshops to get feedback from this group and one thing that came through was the need for a dashboard that was incredibly easy to use and understand, as time is often at a premium.

The Open Sentencing solution

The Open Sentencing model uses AI to detect bias in sentencing. This API-based application uses two trained models: one focused on US Federal sentencing and the other trained on data obtained at the State level. The IBM AI Fairness 360 toolkit is used to identify bias by comparing benchmark data with an individual case that a public defender enters into the system. The Open Sentencing solution then highlights if there is a disparity – say a particularly harsh sentence being proposed for a first-time defendant from the Black community. A public defender can use this data to make the case for a more reasonable sentence or settlement.

Getting hold of the data

To build a dashboard, we needed data on sentencing rates by demographic. One big surprise for us was that although many court houses will publish this data, there is no standardization in the legal system so the format of the data can vary widely at a court level, county level or state level. We had to do a lot of manual work to get the data into a common format so that it could be compared and benchmarked against. This is not a small issue, especially when we think about scaling the solution.

One observation from working on this project is that open standards for data will help bring more technology into the judicial system in a way that will benefit us all. The easier it is for data scientists to get hold of standardized data, the greater the role they can play in fighting for social justice. Learn more about how to make sure you’re standardizing and able to access the right data by reading about a new approach, Data Fabric.

The power of open source teaming

As I said, this project started internally in the summer of 2020. Later that year, it was released as an open source model and published to GitHub. There have been many different contributors along the way and in fact I’m one of the few people that was involved with this project since its inception. There have however been core skills that are useful for us across the board. We’ve needed front-end development, back-end development and data science skills to bring this project to life. We also have the need for someone who can help collect data and organize it into a common format, and a strong project manager and system to keep the team focused.

It has been encouraging for me to know that this project has been the focus of a class at Rensselaer Polytechnic Institute (RPI) who are using the project to learn about open source and its potential for taking on the greatest challenges we face. On that note, I look forward to seeing how this solution could make a difference not just here in the US, but potentially throughout the world.

Source: ibm.com

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Transforming data into artist insights and enhancing the fan experience at the GRAMMYs®

We want to help music fans discover something new and interesting about their favorite artists…” — Corey Shelton

When the world’s top recording stars cross the red carpet at the 64th Annual GRAMMY Awards®, IBM will be there.

IBM Consulting builds long-term relationships with clients and 2022 marks the 5th year the Recording Academy® and IBM have collaborated to enrich the digital experience of Music’s Biggest Night®. This year, the Recording Academy asked IBM to help create a solution that could provide unique insights about the lives and accomplishments of GRAMMY® artists.

Together, we built GRAMMY Insights with IBM Watson, an artificial-intelligence (AI)-powered intelligent search and text-analytics solution, which uses the AI and natural language processing (NLP) capabilities of IBM Watson Discovery to dig deep and reveal distinctive information that fuels passionate fan engagement. Putting AI to work in a real-world application like the GRAMMYs® is a powerful demonstration of how AI is changing the way we learn, work and consume content.

When IBM Consulting partners with the GRAMMYs and other premier events—including Wimbledon, the Masters Tournament and the US Open Tennis Championships—our clients rely on us to orchestrate and implement automation, AI, analytics and skills to fundamentally change how work gets done. In collaboration with digital, marketing and technical teams from the Recording Academy, IBM Consulting helps them jumpstart initiatives using IBM Garage Methodology, bringing together an open, seamless set of practices with a human-centric, outcome-first approach, used to create, execute and operate workflows alongside the client. The process delivers an end-to-end model for accelerating digital transformation, which facilitates innovation and develops the practices, technologies and expertise to rapidly turn ideas into business value.

IBM Watson Discovery scans more than 20 million articles from the past two months using natural language processing to recommend interesting facts—including some hidden gems—about each recording artist. Using these gems, insights are provided to music lovers.

Ultimately, this harnesses intelligent workflows allowing those like the GRAMMY digital team to shift their focus to more critical, run of show processes while AI works in an automated fashion to handle the voluminous tasks otherwise beyond reach, advances scalability thereby improving the fan experience.

2018 marked the first time the Recording Academy and IBM used AI to generate award-show content. Then in 2021—in an innovative effort to help fans feel more connected in the COVID-adjusted telecast—GRAMMY Debates with Watson summarized fan opinions through structured and fun debates during the live show. Thousands of fans weighed in on a variety of statements that fueled strong opinions such as the most groundbreaking artist of all time, the top style icon and how virtual concerts compare to live shows. Natural language processing was used to analyze a half-million keywords and summarize music fans’ points of view around lively conversations.

This year, the Recording Academy worked with IBM Consulting to use AI technology delivered by IBM Watson Discovery. IBM Watson Discovery scans more than 20 million articles from the past two months using natural language processing to recommend interesting facts—including some hidden gems—about each recording artist. Using these gems, insights are provided to music lovers during the GRAMMY Live Pre-show and on the artist pages on GRAMMY.com. IBM Watson Natural Language Understanding, which works behind a firewall or on any cloud, uses deep learning to extract meaning and metadata from unstructured text data. The analytics from the text data reveals categories, classification, entities, keywords, sentiment, emotion, relations and syntax. This uncovers real-time actionable insights that can be used to pull meta-data and patterns from massive troves of data.

The AI insights unearthed by IBM Watson Discovery contribute to the show’s digital workflow and help streamline the operations and production behind GRAMMY.com. The power of AI and NLP greatly speed data mining and queries that would otherwise take weeks or months to parse can now be accomplished dynamically. Once insights have been harvested the production and curation teams decide which content cascades across TV, social media and digital platforms. As stars arrive, meet with the press and showcase their designer fashions, data will be embedded in red carpet live streams and posted to the artists’ pages on the GRAMMY website.

IBM Consulting teams go far beyond creating a solution and handing it off to the client for implementation. Throughout the planning process for the GRAMMYs, IBM Consulting collaborated with multiple stakeholders across the Recording Academy and frequently met with information technology, marketing and digital teams. And on the big night, IBM will be on-site during the event as an extension of the editorial and production teams.

The powerful combination of IBM Consulting, IBM Garage, IBM Watson Discovery and the Recording Academy digital team delivers an engrossing digital experience to more than seven million music fans worldwide. Learn how IBM Consulting can help you use market-leading natural language processing to uncover meaningful business insights from documents, webpages and big data.

Source: ibm.com

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Accelerating sustainability and transparent reporting with digital supply chains

Supply chains are the key to reducing Scope 3 greenhouse gas emissions which can account for more than 90 percent of an enterprise’s carbon footprint.

The criticality of environmental strategy choices, as a subset of a broader sustainability agenda, increasingly defines a company’s prospects in today’s competitive marketplace. According to the World Economic Forum’s Global Risk Report, environmental concerns dominate the top long-term risks among members of the World Economic Forum’s multi-stakeholder community; one of the top five risks by impact are categorized as environmental. Environmental sustainability is no longer just a corporate social responsibility issue or a nice to have—it’s a business imperative.

Customers and employees have become much more environmentally conscious and a recent study by IBM found that nearly 80 percent of consumers indicate sustainability is important to them and 60 percent are willing to change purchasing habits to reduce environmental impact. Moreover, opportunities and risks related to decarbonization strategies and operating models impact all sectors, business functions and communities in the world we share.

With expanded regulatory compliance and pressures from key stakeholders, companies are realizing they must be transparent about sustainability efforts. As a result, leading companies around the world are now committed to validating measured Environmental, Social and Governance (ESG) reporting and non-financial performance disclosures. ESG performance and reporting summarize efforts and provide stakeholders and investors with documented performance and insights to make more holistic and sustainable decisions.

The European Union (EU) has the most sophisticated set of ESG regulations. The regulations were developed to help increase sustainable investing and to further the EU Green Deal. The vision of the EU Green Deal promises to combat climate change and environmental degradation by eliminating net emissions of greenhouse gases by 2050, decoupling economic growth from resource us, and leaving no person or community behind in sustainable transition.

Sustainability as a transformation catalyst an IBM Institute for Business Value (IBV) January 2022 report describes how converting aspirations into reality are met with challenges—and that to operationalize sustainable change, you need more than a coalition of the willing. The gap between intent and action is glaring, where only four out of 10 companies have identified the initiatives to close their sustainability gaps. Additionally, only one-third of companies have integrated sustainability objectives and metrics into business processes. Yet, in the sea of unrealized ambitions, there is hope. The emergence of the “Transformation Trailblazers,” who make up 13 percent of executives surveyed, are considered the most successful in their sustainability journey.

Other key takeaways from the report:

Trailblazers have embedded sustainability across company functions and within their broader ecosystem.

Sustainability efforts show up across functions within the organization. Some 79 percent work effectively with their partners on sustainability engagements, 59 percent engage customers for sustainability input, and more than 50 percent have embedded sustainability within the core of their product innovation, manufacturing, and supply chain processes.

Trailblazers are winning with sustainability.

Between 2018 and the first half of 2021 trailblazers achieved an estimated cumulative revenue growth of 51 percent, a difference of nine percentage points over their next best performing peers.

Trailblazers rely deeply on digitalization and variety of technologies to drive ESG-outcomes.

Trailblazers engage with, and deploy more technology than other companies, and 70 percent are using hybrid cloud to advance their sustainability objectives. The variety and depth of their technology stack enables them to do more with their data, driving better decisions and innovation. Scope 3 emissions continue to be difficult to track and report without digitalization and assurance.

Supply chains are the conduit for change in Scope 3 emissions

Scope 3 emissions are the resulting emissions of activities not owned or controlled by the reporting organization. But they are significant because the value chain of the reporting organization is indirectly impacted. Given that most of Scope 3 emissions occur within the supply chain, addressing supply chain emissions leads to significant impact and improvements when enterprises are committed to comprehensive reduction goals. Transformation Trailblazers stand out from their peers by tapping the potential of emerging technologies and data, broadening C-level and CEO involvement and responsibility and collaborating with ecosystems and supply chain partners.

A focus on supply chain heightens efforts to address Scope 3 emissions. Leaders have a duty to understand their organization’s impact and opportunity to tackle these emissions, while designing a future supply chain landscape that involves product development, sourcing, manufacturing, transportation and logistics. To drive responsible and equitable outcomes enterprises must align people, the planet, profit and purpose.

According to the Environmental Protection Organization, supply chains often account for more than 90 percent of an enterprise’s greenhouse gas emissions, when considering their overall climate impacts.

Supply chains can be a source of innovation and impact when it comes to sustainable outcomes. The ability to identify, measure and track the source of scope 3 emissions, and use technology and process re-engineering to tackle emission reduction is a game-changer. The redesign to reduce and/or eliminate the contribution of scope 3 emissions along with efforts to future-proof further impact of increasing emissions is critical at every step of the value chain including sourcing manufacturing, operations, transportation and logistics.

Talk is one thing, finding a path to successful action is another

Digital transformation will help make the leap from idea to reality. Technology innovations unavailable to previous generations—artificial intelligence (AI), 5G, Internet of Things (IoT), cloud and blockchain—will accelerate this progress in three ways:

◉ The innovations capitalize on data to reveal new insights and underpin new solutions to existing problems. They can change business practices and drive the emergence of the sustainable enterprise, and they support greater public, private and not-for-profit collaboration.

◉ More data and insight equate to more progress. Data and information allow economic actors to drive change in business priorities and practices. Greater transparency and insights allow consumers, companies, investors and governments to change the way they buy, produce, sell, transport, consume and govern. These shifts have the potential to transform the way economies operate as data is infused into business processes and decision making.

◉ The technologies that bring data and insight to bear on the environmental imperative are reshaping the very nature of a company’s operations and business model. Businesses are not just becoming digital. They are applying AI and other exponential technology to create new business platforms to compete and collaborate, and intelligent workflows to drastically improve operations and customer experiences. They’re also using these technologies to augment the capabilities of their people and improve the employee and customer experiences of their organizations.

Digital technologies make possible many market-based mechanisms that drive change and innovation. They can support incentive mechanisms for action at a scale and speed that would be impossible through traditional means of regulation and government intervention. Not only are digital technologies critical for monitoring, verification and reporting, technologies such as blockchain make it easier to share data and manage transactions that support more efficient climate markets.

The combination of business model transformation, digital transformation and a new environmental governance structure has the potential to bring about the societal transformation needed for environmental sustainability. Digital technologies can reshape what is possible, stimulate new innovations and enable effective ways of working together.

Digital supply chains need to prioritize sustainability goals to drive responsible and equitable outcomes

Key areas in supply chain and finance transformation can increase shareholder value, transparency and deliver the brand promise to create differentiation.

Re-thinking sourcing, networks and business models including:

– Product and network design using life-cycle assessment, AI and machine learning

– Circular innovation

– Responsible sourcing and supplier diversity

– Supply and network risk management

Optimizing for net-zero, green operations and asset management, including:

– Decarbonizing operations, networks and logistics

– Circular networks

– Green factories and facilities

– Asset management

Accounting for sustainability and the quadruple bottom line (economic, environmental, social and cultural sustainability) including:

– Sustainable finance

– Environmental, social and governance reporting

– Circular value flows

– Measuring social impact

Enterprises that embrace sustainable practices are creating a vibrant corporate culture, more engaged top and bottom line growth for future generations. Organizations that lead on sustainability measures and initiatives do not approach them as secondary objectives, philanthropy, or a stand-alone project. They integrate related ESG-objectives into core motivation and radically alter the corporate equation for success. Sustainability and impact provide a guiding and multi-faceted prism through which priorities and activities are viewed and leverage their supply chains for ecosystem collaboration. Moreover, digital technologies can create new paths for tapping into the power of data and information—providing visibility into the environmental and social implications of business activities across supply chains.

It’s an exciting and gratifying time to pursue supply chain sustainability. Wherever you are on your journey, we’re here to help you explore and capture new opportunities.

Source: ibm.com

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Intelligent asset management and the race to Zero D

In an earlier post, IBM industry expert Scott Campbell talked about how manufacturers are pursuing resiliency and Zero D to stop defects and improve products and service quality. In part two of our discussion, he discusses how mitigating rework can save millions and offers some insights on the value of creating citizen data scientists.

Can you explain the concept of “detect and correct” and the kind of technologies and processes you need to implement to reach that kind of efficiency and reliability?

The idea is if you can detect an issue or defect at the point of installation — using AI computer vision models — then you can correct that defect immediately without it becoming cemented into rework. The example I always use is a dashboard of a vehicle: the average vehicle has over 300 electrical connectors, and many of them reside within the dashboard. These have to be manually connected, because they’re wires and not easily managed by machinery. If a connector is not seated correctly, it’s going to short or it’s going to fail. This means that function won’t work. But if you catch the error at the point of installation — and this is where computer vision models are so important — you can determine if it is it fully connected, partially connected, or if the line technician forgot to connect it altogether.

This detection capability can also be integrated into an overall quality system and/or enterprise asset management system. In the case of Maximo Visual Inspection, it is tightly integrated into Maximo Application Suite for enterprise asset management and performance, while easily integrating into customer quality alert systems. So, when a defect is detected, it can immediately signal an alert on the manufacturing floor to ensure the worker verifies and fixes the issue before it moves to the next assembly process. This immediate alerting is what avoids expensive rework. In the case of connectors within the dashboard, if defects go undetected, the rework fix for a simple connection gets exponentially more expensive, as it often requires dashboard removal and re-installation.

Using computer vision and AI to see the errors before they turn into rework and fix them right then — and in some cases a company is willing to stop the manufacturing line to fix a problem before it gets cemented — is a pretty significant capability. Especially because scrap and defects can cost a company more than 10% of annual revenue.

When people think of AI models, they think of data scientists and the difficulty in hiring expensive resources who understand AI technologies, deep learning neural networks and specialized AI computer vision models. But what IBM has done is made it extremely easy for the subject matter experts (SMEs) — the people that know what they’re looking for defect-wise — to actually create and manage the AI models. We do it through a user interface that requires no code.

It’s literally labeling a few images within a picture as good or bad or any other decision criteria they wish to define. Then the system can provide auto-labeling based on what has been labeled thus far, greatly reducing the workload. Finally, the existing data set can be augmented to create very large data sets out of the original sample size. This provides the data to build models that give predictable outcomes — in most cases, the accuracy is high as 95% to 98%. The result: subject matter experts take control of the actual models without the need for data scientists. This makes adoption a lot faster because companies use the people who are familiar with what the manufacturer is doing on the assembly line. That expertise is also a major contributor to the high-level accuracy of the AI models.

What about the concept of predict and correct? Does that play a role in driving continuous operations?

At IBM, we asked, what if you could increase efficiency, extend asset lifecycles, reduce downtime and costs — all while building resiliency and sustainability into your business?

Predict and correct is fundamental to being able to answer that question.

We’ve made it easier to digitize operating environments by taking the sensor data coming off of assets, and understand at a point in time the condition and operational status of those assets. And it’s a lot of data! A single production line can produce more than 70 terabytes of data each day.

By understanding the asset’s total health in terms of lifecycle and leveraging historical time series data, Maximo can predict when a failure is likely to occur in the future. If you can accurately predict failure well before it happens, you can remediate it. This predict and correct capability plays a major role in delivering and facilitating continuous operations.

You start with Maximo Monitor — capturing data and gaining visibility into what your assets are actually doing. Then you add Maximo Health, which tells you from a lifecycle perspective what maintenance structure you should be looking at and allows a single view of assets across the enterprise. Finally, with Maximo Predict, you can see into the future to be much more prescriptive with your asset performance management. It’s an evolution, but Predict is where the AI models come together to allow a customer to see where there is probability for failure for all of their assets and take corrective action.

We’ve been talking about the auto industry but I’m assuming that any industry can benefit from this.

Absolutely. And it bridges beyond manufacturing. We’re talking about the pursuit of Zero D and resiliency for manufacturing because it aligns so well to Industry 4.0, but the same technology can be used, for example, in travel and transportation. Consider railways and the ability to understand the assets — which are both the railway tracks and the train itself — and looking for potential failure. Sensor data is part of it, but then AI visual inspection can also be used to visually inspect railcars, wear on couplings, wheels, and wiring as just a few examples.

Traditionally, with cargo trains, there are maintenance yards, and the train will pull in and then maintenance people manually inspect the train. They visually ensure everything is okay before they let it go back on the track. But that industry is quickly evolving to provide inspection while the train is in transit. Cameras over the tracks take pictures of the train and provide the results immediately through AI computer vision models. If there are urgent safety concerns for example, the railway operator could stop a train. If not, it could continue on, but the technicians might say, Okay, the next maintenance window we’re going to need to make these repairs. Not only is the inspection much more complete — and can happen with higher frequency — but it is also much more accurate in prediction, because it’s using sensor data as well as visual data to manage the assets. We’re also seeing this in civil infrastructure and with bridges and roadways. There’s just a lot of places where visual data and sensor data come together.

What are some of the issues and misconceptions that an organization might have when it comes to using AI to predict asset health and build a more resilient organization?

From a challenges perspective, the first one is a company that doesn’t use IBM Maximo EAM (Enterprise Asset Management) as its work order system. Often companies believe they can’t take advantage of the rest of our application suite if they don’t use Maximo EAM across their entire organizations. But IBM’s monitor, health and predict solutions can connect to other EAM systems so that companies can take advantage of their operational data. We can also connect to other systems that are gathering the sensor data and we can feed this data into Maximo Monitor. This is important because two-thirds of operational data goes unused. It also eliminates a hurdle a company might have to jump with another provider, simply because their work order system is in another vendor’s application. We can manage within that and still drive value with predictive capability by introducing monitor, health, and predict.

Another typical issue is that each asset has siloed data into its own repository. Getting data across all the assets collected into a single pool can be very difficult and time-consuming. But we can bring connectors via APIs or solutions like IBM App Connect and help customers consolidate data into a single repository. This repository can capture time series data, and then you have a starting point for building resiliency and sustainability into your business by extending asset lifecycles and reducing downtime and costs. Once you’re positioned for intelligent asset management — and building resiliency and sustainability into your business — you can reduce operational costs up to 25% and increase uptime and availability by 20%. Those are results that no one objects to.

Source: ibm.com

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AI in retail and the rise of the purpose-driven consumer

That retail has experienced extreme disruption in recent years is beyond questioning. Even before Covid turned the world on its head, headlines about the so-called “retail apocalypse” were near-ubiquitous in the media.

Since then, we’ve seen lockdowns, fluctuating openings and closings, some firms going out of business altogether, celebrations of essential retail workers and a surge in online shopping that brought record profits while yielding more ambiguous results for others. And now, with ongoing supply chain disruption, inflation and a tight labor market, it’s clear that the retail sector still faces substantial challenges.

But these challenges also represent opportunity, and harnessing the power of digital transformation will remain central to every business leader serious about thriving in the post-Covid world. Retail isn’t just big, it’s huge — the National Retail Federation expects sales to grow by as much as 13.5% to an estimated total of $4.56 trillion in the US in 2021.

And while we may not yet be living in the post-Covid era, the outline of what that “next normal” might look like is emerging. New research from NielssenIQ suggests that the widespread availability of vaccines is fueling a “cautious confidence renewal” among shoppers, even while priorities and shopping habits continue to be impacted by the pandemic.

But amidst all this uncertainty just what trends should business leaders be focused on?

The rise of hybrid shopping experiences

Each year analysts pay close attention to retail spending around the holidays, and this year the news was upbeat. Although in December sales dropped by 1.9%, this was offset by overall robust Q4 growth of 17.9% over the same period a year earlier.

As IBM CEO Arvind Krishna suggested in a recent keynote talk at the National Retail Federation (RTF), people appear to have moved from a “just in time” approach to shopping to a “just in case” model — though whether this trend will continue long-term remains to be seen.

If consumer spending patterns are evolving, so too is their relationship to the retail experience. And while the shift to online shopping has been substantial, digital transformation does not signal the end of physical shopping. New research from IBM’s Institute for Business Value (IBV) and the RTF indicates that consumers want a range of experiences beyond simply opening the door and picking up a parcel.

In fact, nearly three in four consumers (72%) report that they still rely on stores as part of their primary buying method. Meanwhile, hybrid retail — including experiences such as curbside shopping, or ordering online and picking up instore — is now the primary buying method for 27%  of consumers.

Strikingly, 36% of Gen Z consumers — so-called “digital natives” — prefer this hybrid model of shopping: the largest share of any age cohort.

Future proofing retail through AI

But while trends show us where we are now and were we might be headed, what can retailers do to future proof their digital transformation strategies?

Here AI represents a powerful opportunity to increase profits and deliver new and improved experiences, with IBM’s Krishna telling the audience at the RTF that we have so far only unlocked 10 percent of the technology’s potential.

We already know that AI can be used to power virtual assistants and automate checkouts. AI-powered logistics management can predict product demand by analyzing historical and location information, and it can get the right products in front of consumers at the right time.

But it’s also important to consider the wider impact of AI. More efficient, automated processes don’t just lead to increased profitability — they also have a human impact. The more that we can get machines to shoulder repetitive time-consuming work, lead to less stressed, more engaged employees and satisfied customers.

The importance of the purpose-driven consumer

Another increasingly important factor for business leaders to consider when pursuing their digital transformation strategies is: what are the broader environmental and social impacts of our actions?

This is not just a matter of satisfying the increasing number of regulatory obligations. Research from the IBV shows that 62% of consumers say they’re willing to change their purchasing habits to reduce environmental impact. Meanwhile “purpose-driven consumers” who seek products and brands that align with their values are on the rise and are now the largest segment of the buying population, representing close to half (44%) of the total. Digital transformation has a central role to play here also: for instance, Heineken recntly teamed with IBM to modernize its integration capabilities in a way that also supports the firm’s environmental and social responsibility initiatives.

The good news is that purpose need not be in conflict with profit. In fact, a recent analysis of business sustainability strategies by the IBV found that between 2018 and the first half of 2021 a select group of “transformational trailblazers” saw an estimated cumulative revenue growth of 51% — a difference of nine percentage points over their next best performing peers.

Meanwhile, according to Gallup, Gen Z and Millennials now make up nearly half (46%) of the full-time workforce in the U.S., and these age groups want to work for companies with ethical leadership. Successfully implementing sustainability strategies may therefore also make firms more attractive to job seekers, and help them overcome the challenges of a tight labor market. Indeed, according to research from PwC, 65% of people worldwide want to work for a company with a social conscience.

Source: ibm.com

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Industry 4.0 and the pursuit of resiliency

Downtime can cost a manufacturer upwards of USD 21,000 per minute. Fortunately, AI has evolved to accurately identify issues and take action. This advanced technology allows companies to easily add intelligent “eyes” to their operations with standard mobile devices — the same smartphones and tablets that you’re using right now. All to quickly identify defects in production outputs as well as remotely monitor assets for potential disruptions.

I talked with IBM expert Scott Campbell about this AI evolution and his current focus: helping clients intelligently manage their assets with Zero D, which stands for zero defects and zero downtime. Scott has had numerous product management roles within IBM. And almost all of them centered around some type of AI technology. First in financial environments, then with Red Bull racing (where his team used AI simulation to understand race dynamics), and now as the lead product manager for IBM Maximo and IBM TRIRIGA.

What’s the biggest challenge manufacturers face right now?

Every manufacturer knows that there’s a tremendous value if you can eliminate defects and stop rework. If you can keep your manufacturing facility running 24×7 without any downtime, it’s almost a given that there is ROI there. The challenge is how do you actually transition from a reactive environment — which is where most manufacturers are — to a proactive environment. So instead of thinking, we have a problem to fix, how do you think instead, we’re anticipating problems to fix before they actually become problems. The cool thing is, IBM has AI technology that is sophisticated enough to let a company do that effectively. But we have to make sure that it’s trustworthy. When a company looks at all this data, they have to believe in it. Otherwise they’ll go right back to reactive maintenance.

A lot of people talk about Industry 4.0, but I think the big challenge for many manufacturers is how do you even get started? How do you take something that’s transformational and evolve it over time? Because you can’t do this in a big bang approach, or a forklift upgrade approach. You have to evolve it. And you have to start somewhere.

Starting with defect detection is a good way to get introduced into an AI environment that’s fairly easy to understand. It’s pictures, it’s images. You can see the system is doing a better job than an individual can do, and that makes it easier to expand use of that technology. Once you begin to build trust in those results, it’s easier to use machine learning and AI technology for maintaining the assets running on the manufacturing floor. Then you understand the health of an asset, you know hey the odds are really high — a probability of 85 to at 95% — that this asset is going to fail sometime in the next 45 days, so let’s do something about it. 

And manufacturers are moving toward this?

Oh, yeah, you’re seeing it across the board. There’s a big North American auto manufacturer using AI visual detection and predictive monitoring, and they saw immediate results. It’s incredible how quickly they were successful just running a simple pilot. They found 30 defects in the first 30 days, which isn’t that big of a deal. But they were looking at one single connector in one point of their installation, tied to one specific problem for them. When they expanded that to multiple locations on their assembly line, they found up to 200 defects a day. So in the very first month they gained a USD 1.8 million savings on that one manufacturing line.

There are two parts to the Zero D story. Visual inspection and asset performance management (APM). Visual inspection uses computer vision models focused on quality inspection. APM uses machine learning models based on time series data to determine health of assets and probable failures in the future. Toyota is using Maximo Visual Inspection, and now they are also using the Maximo Asset Performance Management (APM) suite. They tested Maximo APM on some of their machinery that does liquid cooling and found that was another problem area for them. By implementing the software into this pilot, they are now able to monitor the asset health 24×7 and predict probability of failure in the future. It is the foundation for them to shift from being reactive and cycle-based, to practicing a proactive, reliability-centered maintenance strategy. This will be transformational for their entire organization.

Those are just two examples of where Industry 4.0 and how intelligent asset management has started to gain traction. Of course, there are lots of others, but those two examples are true showcases for transformational manufacturing processes.

Does adopting Industry 4.0 bear out all the way down the line to the customer?

Yes, it does, especially on two fronts: quality and meeting demand. For Toyota, quality is mission one. Fewer recalls and less warranty work (compared to other vehicle brands) drives customer loyalty, not to mention reduced costs for rework.

Then when it comes to meeting demand, it’s estimated that downtime costs on average about USD 21,000 per minute. That means within an hour, you have a million-dollar problem. And if you can’t meet the demand, somebody else will. There’s loyalty in car buying, but there is also availability, especially with the chip shortage.

When it comes to defects and downtime each topic seems big enough on its own. Why not tackle them separately? Why do you advocate handling them both at once?

Either one of them is critical. But you achieve true transformation when you attack them both at the same time. Because no matter how high your quality is, you can’t meet demand if you have downtime. Conversely, even if you’re super effective in your manufacturing processes but your quality inspection is poor, you’re just adding to your scrap heap or your rework at a tremendous pace.

That’s why it’s the combination of AI-based visual inspection for quality and asset performance management for predictive repair that lets you increase quality and production efficiency at the same time — and that helps build a sustainable and resilient business.

Do you have any other hard numbers around the savings that an intelligent asset management program could bring to a manufacturer?

Of course, this approach is applicable beyond the auto industry. It’s just a very good use case that folks understand. If you look at the rework of a defect — and it’s important to distinguish between a defect and rework — a defect can occur in the production line, but it only becomes rework if it goes undetected through final production. If you catch it, and fix it, before it gets to the next stage of the line, it’s no longer a defect. We emphasize detecting and correcting at the point of installation.

If a defect turns into rework work — which means if it’s either caught in final inspection or somewhere down the line, even potentially by the customer — then it’s about USD 300 per incident. So, if you think of that North American auto manufacturer who found 200 defects a day, they saved USD 300 multiplied by 200 defects multiplied by 365 days. That’s how you hit very large numbers very quickly in terms of saving.

Can you also talk about the savings from not over-maintaining an asset and only performing maintenance when it’s actually needed?

When our customers understand “$21,000 a minute,” they tend to create very rigid maintenance schedules. The problem is they have no knowledge of what actually needs to be fixed. It becomes hey we’re going to check everything once a week. There’s the idea that frequent maintenance schedules are cheaper even though it’s overkill.

But with an APM platform, you can reduce your maintenance and improve uptime — all at the same time! It is prescriptive in terms of understanding where you need to actually apply resources. It provides 24×7 monitoring of the health of assets, can detect anomalies before they become critical issues, and can predict the probability of failure in the future. Technicians are no longer tied to calendar-based scheduling. Because now a company has the data that indicates these assets are just fine and they’re not going to fail for another month, several months, or even years. This means technicians, who are becoming a scarce resource, can better schedule their time. And companies can utilize technicians much more effectively in the areas that have the highest value, based on data they can trust.

Source: ibm.com

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