What is a data fabric architecture?

To simplify data access and empower users to leverage trusted information, organizations need a better approach that provides better insights and business outcomes faster, without sacrificing data access controls. There are many different approaches, but you’ll want an architecture that can be used regardless of your data estate. A data fabric is an architectural approach that enables organizations to simplify data access and data governance across a hybrid multicloud landscape for better 360-degree views of the customer and enhanced MLOps and trustworthy AI. In other words, the obstacles of data access, data integration and data protection are minimized, rendering maximum flexibility to the end users.

With this approach, organizations don’t have to move all their data to a single location or data store, nor do they have to take a completely decentralized approach. Instead, a data fabric architecture implies a balance between what needs to be logically or physically decentralized and what needs to be centralized.

Thanks to that balance, there is no limitation to the number of purpose-fit data stores that can participate in the data fabric ecosystem. This means you get a global data catalog that serves as an abstraction layer, single source of truth and single point of data access with infused governance.

Six core capabilities are essential for a data fabric architecture:

1. A knowledge catalog: This abstraction layer provides a common business understanding of the data for 360-degree customer views, which allows for transparency and collaboration. The knowledge catalog serves as a library with insights about your data. To help you understand your data, the catalog contains a business glossary, taxonomies, data assets (data products) with relevant information like quality scores, business terms associated with each data elements, data owners, activity information, related assets and more.

2. Automated data enrichment: To create the knowledge catalog, you need automated data stewardship services. These services include the ability to auto-discover and classify data, to detect sensitive information, to analyze data quality, to link business terms to technical metadata and to publish data to the knowledge catalog. To deal with such a large volume of data within the enterprise, automated data enrichment requires intelligent services driven by machine learning.

3. Self-service governed data access: These services enable users to easily find, understand, manipulate and use the data with key governance capabilities like data profiling, data preview, adding tags and annotations to datasets, collaborate in projects and access data anywhere using SQL interfaces or APIs.

4. Smart integration: Data integration capabilities are crucial to extract, ingest, stream, virtualize and transform data regardless of where it’s located. Using data policies designed to simultaneously maximize performance and minimize storage and egress costs, smart integration helps ensure that data privacy. Protection is applied on each data pipeline.

5. Data governance, security, and compliance: With a data fabric, there’s a unified and centralized way to create policies and rules. The ability to automatically link these policies and rules to the various data assets through metadata, such as data classifications, business terms, user groups, roles and more are easily accessible. These policies and rules, which include data access controls, data privacy, data protection and data quality, can then be applied and enforced in large scale across all the data during data access or data movement.

6. Unified lifecycle: End-to-end lifecycle to composes, builds, tests, deploys, orchestrates, observes and manages the various aspects of the data fabric, such as a data pipeline, in a unified experience using MLOps and AI.

These six crucial capabilities of a data fabric architecture enable data citizens to use data with greater trust and confidence. Irrespective of what that data is, or where it resides — whether in a traditional datacenter or a hybrid cloud environment, in a conventional database or Hadoop, object store or elsewhere — the data fabric architecture provides a simple and integrated approach for data access and use, empowering users with self-service and enabling enterprises to use data to maximize their value chain.

Source: ibm.com

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A blazingly fast database in a data-driven world

A database is a crucial engine for a world becoming more data driven.  Businesses are more heavily relying on smart insights and emerging patterns to succeed.

Advancements in software and hardware had an interplay between the rising appetite for any organization making a data-driven decision. In this blog, one of the key inventors in modern databases helps illuminate the evolution of databases, business use cases and unique perspectives being at the ground floor of innovation.

Guest blogger: Chief Technology Officer and Co-Founder: Adam Prout

We founded MemSQL (the original name of SingleStore) in 2011. “Mem” signifies in-memory and “SQL” makes it clear that you could indeed achieve speed, scale, and SQL without giving up on the expressive power and advantages of relational algebra. Nikita Shamgunov (Co-Founder of SingleStore) and I were seeing the signals in the market. First there was a notion that relational databases could not scale to the speed of modern apps and thus NoSQL databases were rising in popularity. Some of the relational database vendors were slow to adapt during this period and some companies were starting to create specialized databases in-house to address the challenge. Secondly, hardware advancements that historically accelerated software workload by 50% each year was coming to an end in the early to mid 2000s.

At this point, CPUs began to be built with more processing cores, but the increase in processing power of each core slowed dramatically. Databases designed to be fast on systems with a single core or only a few cores required substantial redesign to run well on machines with many cores. This required more innovation on the database software side. One of the key challenges in distributed scale-out databases included how to deploy many hosts built with high availability and elasticity while keeping the familiar SQL interface. This helps our customers mitigate the risks and costs of managing complex ecosystems of tooling built around the mostly single-host SQL database technologies that existed at the time.

Co-developing with customers in gaming, banking and ridesharing

We then started looking for customers struggling to scale their existing SQL databases. Around 2011, we worked with a hot gaming company with a real-time analytics use case to understand what their users were doing in the moment to optimize the gaming experience by monitoring how users interacted with the game. The gaming company was also looking for early warning signs in customer behavior that may indicate bugs or performance issues impacting the gaming experience. Previously, this customer had run this analytical workload on other types of databases, but the conventional databases didn’t handle it very well. The customer also attempted to run it in a data warehouse, which wasn’t good at low latency streaming data ingestion and low latency query support. They also tried to run it on an operational database but it didn’t have the right storage technology to efficiently run complex analytical queries. Our database addressed these challenges to meet their goals.

We built a lot of features working with the first gaming customer and now wanted to turn to driving revenues at an accelerated pace. There was a banking customer that had a set of requirements that were very similar. For example, their applications ingest market data, ticker traffic, internal data, and other proprietary data streams to support banking needs. Their existing databases in use could not support the low latency, high availability and high query concurrency needs for the end-user facing apps.  Given that these apps and dashboards were built for their most important client segments, the high-net-worth investors with large portfolios with hundreds to thousands of positions, these apps could not go down or slow down as it can have serious consequences in their positions. Of course, their clients needed to see the live results such as the current market position and maintain the continuously updated view of order transactions using visualization of data feed.

Another example in the banking segment also combined these real-time streaming and analytic needs with elasticity and agility. This in-house application recommended how this bank should make trades. The use case of this bank required tracking of portfolio risk using high speed joins of position risk, index and fund composition, and other risk factors.

Interestingly these needs were not just for banking. Here is a rideshare company with a use case on real-time marketing segmentation and targeting application used by thousands of employees across the marketing, product, and leadership teams. SingleStore helps them provide detailed, real-time data on more than 300 different attributes across their rider and driver population. They can query things like behavior, cancellation rate, churn, days since last trip, etc., all with an average of 1 ms response time. They can see, by device, the language, location, status, and preferences of riders and drivers. If a driver has not taken a trip in the previous week, they can provide real-time incentives to those drivers to keep them on the road.

Technology underpinnings of blazingly fast databases

SingleStore has patented Universal Storage combining the qualities of rowstore and columnstore into one unique table type. That is, it has the very fast table scan performance of a columnstore and yet can support highly concurrent point read/writes with performance close to that of a rowstore. Our database also scales out horizontally using a distributed cluster architecture, providing high throughput and fast response time for query execution. Separation of storage and compute allows for cost savings as well as improved performance and elasticity. SingleStore can store data in blob storage without negatively impacting point read/write query performance like cloud data warehouses. This is why SingleStore’s single-core speed is 10x to 100x or more the speed of many legacy databases. It also maintains broad compatibility with the modern data processing ecosystem, accessible through standard SQL drivers and ANSI SQL syntax. SingleStore can all at once handle high throughput ingestion, low latency query, especially for streaming data, high performance read/write, scan, trickle inserts, and trickle deletes. We were excited to see our TPC benchmarking results and additional benchmarking tests.

It was the culmination of our effort as we pivoted to go beyond the in-memory databases i.e, toward building a more general-purpose distributed SQL database able to run a wide spectrum of workloads with strong performance. That’s when we iterated toward unified storage with an architecture that allows customers to run operational and analytical workloads simultaneously while scaling resources to power the next generation of data-intensive applications.

Adapting databases for hybrid clouds

We also started our managed service (cloud database-as-a-service offering) about 5 years ago and building features for the managed service was different. Our work to separate compute and storage with near unlimited blob storage was a table stake for the managed service. We had to rethink pricing and address the feature gaps. Existing databases tend to become silo databases within a particular cloud or even a particular region of a cloud. Instead of taking multiple copies of data and updating using different technologies, we want to help run this all transparently. This way, you can now take your data and run data processing tasks wherever it is convenient.

Nowadays, even large banking customers are interested in migrating off on-premises deployment and doubling down for the managed service deployment because their customers require the application to be in multiple regions in multiple clouds. We expect the demand for having a multicloud control plane for databases to become even higher.

Another key theme is using AI/ML within a database. Writing a tier 1 app is challenging for developers so how about making the database aspect much easier?  Intelligent databases with AI and ML can help optimize and self-tune different aspects of a customer’s application without needing developer intervention.

Experience to share with fellow technologies

I built databases my whole career. I developed code, ran a team of engineers, and became the Chief Architect, and then the Chief Technology Officer. Databases offer a diversity of technical areas that are in constant flux. You need to know basic machine learning, statistics, scheduling algorithms, data structures, network protocols, query languages, and runtime designs – all these pieces of computer science including distributed systems. It is endlessly interesting – you can master one thing and move onto the next thing while looking at what the next thing could be.

Working on a database with a small group of people in a start-up is special. When you are working in a large company, the ways people react to these challenges are heavily colored by their existing position in the market and strongly held beliefs. Engineers don’t always get to work on the right problems.

You don’t know the right answer going in and you feel the raw market forces that are more directly applied to your product or company – you can directly contribute to driving the next evolution of the market. The unique experience of looking to solve some of the hardest and highest impact problems is almost impossible to get outside the start-up environment. Imagine David and Goliath! This is ultimate for an engineer aspiring to gain lots of growth opportunities and upside potential.

Looking ahead

We are excited to partner with IBM as it has relationships with customers in ways that go beyond our relationship. With SingleStore-IBM OEM partnership, businesses can enjoy the best of both worlds — the blazingly fast database from SingleStore and the global scale and deep-expertise in data and AI from IBM.

Source: ibm.com

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Leadership during challenging times: Creating an employee-centric culture

Enterprises have been radically changing how they operate for the past two years as they replaced face-to-face interactions, adopted a hybrid model, and delivered a new experience for employees, customers, and partners. Leaders jumped from digital transformation awareness, and reluctance, to implementation to ensure they weren’t left behind. But a new trend has emerged—digital reinvention.

According to the latest McKinsey Global Survey, business leaders predict that by 2026, half of their revenues will come from products, services, or businesses that haven’t yet been created. This urgency for building new businesses reflects the belief that today’s products and services will be insufficient for addressing disruptions and meeting a sustainable future.

But this innovative trend surpasses external products and services. Many enterprises recognize that some of the innovative technologies that have taken hold in the last few years, like hybrid cloud and AI, could be used to digitally reinvent their own internal processes and empower them to become more resilient and user-focused. Not only does technology improve customer experiences by delivering strategic and innovative capabilities – it allows leadership to become employee-centric, which Paul Cormier, President and CEO at Red Hat and I discussed at the recent Chief Data and Technology Officer Summit, “Leadership During Challenging Times.

Employee centricity

Employees, at all levels, started rethinking their purpose, their values, and what they wanted to do with the rest of their lives. Trust and empowerment became vital. To succeed, leaders must create a culture where employees are enabled, trusted, and empowered to make decisions and not be afraid to make mistakes.

Remote working, well known by the open-source community, enabled leaders to change the culture of entire organizations – from hiring the best talent with no regard to location, to giving people a voice who used to miss out due to not being in the same physical space as the rest of their teams.

What does the future of enterprises look like?

Enterprises are becoming more intelligently automated, data-driven, and predictive; risk-aware and secure. Leaders are designing organizations for agility and speed by infusing AI across the foundational businesses functions: customer care, business operations, the employee experience, financial operations and, of course, IT operations.

As enterprises reinvent their business and re-architect their entire environment, having a flexible architecture is crucial. Working in a hybrid multicloud environment brings new complexity to this task. But new concepts that provide greater agility without sacrificing integration or governance, such as a data fabric architecture, are beginning to take hold. That will be the new trust as we move forward with these technologies.

To hear more from peers and learn tangible actions that you can customize and implement into your organization, watch the replay of CDO/CTO Summit “Leadership During Challenging Times.” I invite you to register for our CDO/CTO Summit series here. We will discuss AI in April and Data Fabric in June.

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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Why intelligent commerce is the future of business

Close to 60% of CEOs are planning for assumed economic growth, says Gartner. How are you thinking about growth? Are you considering the possibility of extending your business models? In today’s digital world, there are tremendous opportunities to expand your business and take advantage of new growth opportunities, whether it’s through creating a seamless experience between physical and digital experiences, moving to a subscription model or expanding your business to different marketplaces, B2B or B2C.

As you consider these possibilities, it’s imperative to keep in mind that all business is now digital, even when it’s in person. Think about a customer returning an online purchase to a physical retail store or an in-store retail associate ordering an out-of-stock item for a customer. In a world where all business is now digital, the fundamental driver for business growth is the customer experience.

Optimizing commerce through a great customer experience isn’t about turning on new technology or doing business through every possible channel. Instead, it’s about helping customers through their 100’s of micro-decisions across their buyer journey and engaging, delighting and motivating customers to convert. This requires the thoughtful, orchestrated execution of technology across your entire business. We call this intelligent commerce—a human-centric, experience-led, insight-driven approach to designing digital commerce solutions on any cloud to activate new revenue streams, reduce inefficiencies and accelerate time-to-value for your commerce investments.

Then what is dumb commerce? Unless you are really driving experiences across the channels that are most strategic to your business and unless you have the data, insight and the flexibility to expand your ecosystems to accelerate time to value, your commerce experience likely needs to be more intelligent.

Close to 60% of CEOs are focused on expectations of growth as their top objective and priority in 2022 and beyond. – Gartner, 2021

Turning commerce into your growth engine

In the years leading up to the pandemic, many companies moved digital commerce to a back seat of their strategic priorities. Digital commerce was a digital work horse, which was maintained and fixed when broken. It was often marginalized and discussed in basic terms of transactions, platforms and implementations.

While IBM has always approached digital commerce with a strategic lens, recent market disruption has caused our clients to rethink its importance. It’s a pivot to understand that the real promise of digital commerce is growth. Our strategic approach moves digital commerce to a level of intelligence to change the bottom line of the enterprise.

How to orchestrate experiences that drive growth across complex multi-cloud ecosystems

Based on thousands of consulting engagements with clients, IBM industry-leading research and even our own path to transformation, IBM identified three primary growth levers shaping commerce growth:

1. Experience orchestration to tame channel chaos

Orchestrating personalized and consistent experiences is increasingly complex as the number of commerce channels continues to explode. Think of experience orchestration as the fundamental components needed to realize the promise of intelligent commerce. The world is incredibly complicated and continuously changing. This means you need to have all the critical technology in place to ensure your business has the agility and capabilities to continuously deliver great customer experience

For example, to maintain and optimize workforce operations in times of disruption, Frito Lay expanded its e-commerce strategy and created a new solution to streamline frontline employees’ workflows. Frito-Lay is now positioned to fully function in a virtual environment, helping teams keep the same routines, stand-up times and release schedules.

Such best-in-class commerce experiences are enabled by human-centered business design and transformative technologies designed to be strategic for today, tomorrow and the future. Based on experiences with our own clients, enterprises designing and deploying omni-channel experiences can increase sales of up to 30% and realize up to a 300% increase in mobile conversions.

2. Data and insights-driven interactions to personalize engagement

Engaging and activating customers in a personalized commerce experience is a moving target due to several stressors—the economy, privacy rules, healthcare regulations, social changes, environmental concerns, and more. Businesses need a focused, dynamic and responsive set of tools to deliver a consistent exemplary commerce experience​.

With the fundamentals in place to enable experience orchestration, you can now access the critical data and insights needed to personalize customer experiences. You can understand who your customers are, what they like, their history with your brand and their buying patterns. Your strategy should focus on the type of relationship you want to have with your customers and their expectations of your brand.

IBM solutions activate buyers with the personalization and permissions enabled by AI, data and insight-driven interactions. IBM Consultants partner with clients and use insights to make the buying experience transparent through order and inventory intelligence. Our clients that drive personalization and engagement realize a 20-50% increase in customer satisfaction, net promoter score (NPS) and loyalty while doubling order size.

For example, with 90% of Audi UK’s car purchases starting online, IBM Consulting redesigned Audi’s website to reimagine its customers’ digital journey from initial inquiry to ownership. By unifying its mobile and desktop shopping experiences, Audi UK  increased  online sales inquiries by 59% despite an almost 30% industry decline in new UK car sales during the pandemic.

3. Ecosystem evolution to keep pace with technology innovation

Delivering desired commerce experiences is harder than ever as customers continuously adopt new technologies. Flexible, modern architecture enables experience evolution and curation so your business can keep pace with and benefit from new technologies. Growth isn’t just about supercharging what you already have. It’s about breaking new ground, expanding your business model and activating new revenue streams. Accelerators activate growth sooner and at scale.

For example, Swiss luxury watchmaker TAG Heuer built a new guided purchasing experience in a matter of weeks that helped bridge the physical gap due to COVID-19 restrictions and resulted in triple-digit growth.

From strategy and advisory through managed service, delivery and everything in between, IBM Consulting can help you operationalize these three growth levers to deliver the measurable outcomes that are most important to your business.

Does your business need intelligent commerce?

The first step to understanding where you fall on the intelligent commerce spectrum is to identify where there may be gaps in your commerce strategy. If you answer ‘no’ to any of the following questions, your commerce experience needs to be more intelligent.

◉ Are you adopting technology and providing the orchestration necessary to take advantage of the explosion of commerce channels?

Delivering desired commerce experiences is harder than ever as the rate of technology innovation accelerates exponentially. Customer experience expectations continue to increase as they adopt new technologies and new channels, which makes orchestrating personalized, consistent experiences increasingly complex.

◉ Are you taking advantage of data, insights and artificial intelligence (AI) to deliver targeted, personalized and contextual experiences to customers and new generations of buyers?

All customers want you to understand them, know them, anticipate them and help them at every touch point. And newer generations have incredibly high expectations for digital buying experiences. The ability to focus on the right data supporting the right experience is critical, and balancing personalization with respect is also critical.

◉ Are you empathetic to your customers’ changing needs and preferences, including sustainability?

Engaging your customers can feel like a moving target, and the need to dynamically communicate is constantly changing due to myriad external forces and influences—economic, privacy, health, social and environmental—impacting their decisions. Adapting to the disruption over the past few years has forever changed buyer behaviors. Customers expect a dynamic, holistic buyer experience across every in-person and online interaction.

The IBM approach to intelligent commerce

IBM partners with global industry leaders to transform commerce by orchestrating experiences and ecosystems to drive client growth. By using AI, smart data and analytics, customer insights are harvested to design personalized commerce interactions. Modern architecture enables deployment of omnichannel experiences, which activate new business models with new revenue sources, increase enterprise efficiency and enrich customer experiences.

Source: ibm.com

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Aviation operators rely on environmental intelligence technology as climate change alters global weather patterns

It’s happened to so many of us: we book a flight, make sure our luggage meets airline standards, head to the airport early to get through security, and when we reach the gate, the flight is canceled due to weather. Unfortunately, weather is the most significant cause of flight delays, and severe weather events are becoming more frequent and unpredictable.

Read More: 20 ways smarter asset management paves the way to more sustainable operations

Unforeseen storms, floods, wildfires and temperature changes, all exacerbated by shifting climate patterns, are critical problems for the airline industry. When freak storms hit, they threaten to inconvenience or harm travelers and ground crew, keep planes grounded and delay flights indefinitely. Preparing for these events is costly, and false alarms are bad for the bottom line and for customer satisfaction.

The aviation industry needs as much information about severe weather events as possible to understand how storms will affect their craft and personnel. Many airlines are looking to invest in proactive systems that can alert teams about severely dangerous conditions, helping them decide when to hold flights and when it’s safe to resume operations.

Even long-range forecasts provide information just three to five days out, which still leaves most airlines in a reactive space when it’s critical to be proactive. New predictive analytics technology provides the aviation industry with a better way to understand aberrant weather and respond faster to otherwise unforeseen environmental conditions.

Better technology reduces disruption

Better predictive analytics allows aviation operators a future view of the weather based on climate data. By looking beyond the immediate forecast, operators can anticipate events and plan ahead for cost-effective safety.

In the U.S., for example, Tornado Alley is moving eastward due to climate change, disrupting lives that were historically safe from that type of weather event. During this new shifting tornado season, an aviation operator using reactive weather forecasting technology will be able to shut down operations quickly. But an operator using environmental intelligence can understand where the most severe tornados may form next. They may have facilities on high alert from the beginning of the season, build those facilities with future climate patterns in mind, or even avoid new investment in high-risk areas.

This long-term thinking is just as crucial for building sustainability and reducing the industry’s significant emissions footprint. Aviation leaders recognize that sustainability and efficiency are the leading opportunities for growth, the best chance to stand out from their competitors, and vital for the long-term health of the industry.

Using enhanced environmental intelligence for improved safety

Environmental Intelligence (EI) uses the power of artificial intelligence to improve responsiveness to unpredictable weather conditions for the benefit of employees, guests, aircraft and facilities. The technology provides environmental data and forecasting capabilities to deliver greater insight into what weather is coming and how it might affect departure and arrival times, runway direction, grip and ramp movement, towing, and more. Ultimately, the technology allows aviation operators to be proactive and make long-term decisions that change from season to season instead of day to day.

One exemplary aviation leader is Sheltair, the nation’s largest privately-owned aviation network. Sheltair prioritizes safety and has earned ISBAH and NATA Safety First certifications at all of its bases. To further build its operational resiliency, the company  added the IBM Environmental Intelligence Suite (EIS) to its FBO operations network-wide. Using this technology, Sheltair can better monitor disruptive environmental conditions, flooding, and even air quality, receiving alerts when dangerous weather is detected.

This technology allows Sheltair to plan for the unavoidable, respond quickly to protect employees and customers from injuries caused by adverse weather conditions, detect high-risk weather events and mobilize facility and maintenance teams in advance to prevent damages to assets.

In a future where only change is certain, every part of the aviation industry needs to move from short-term reaction to long-term action.

Source: ibm.com

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DevOps or DevSecOps?

Currently, security attacks are getting more sophisticated and targeting a wider array of system components. This makes preventing and recovering from them more difficult especially when security knowledge and responsibilities are siloed within an organization. It is increasingly more important to ensure that everyone in an organization has a stake in security and that the company’s experts integrate more deeply with other teams. Many companies claim to make security a pillar of culture, but rarely do they invest in more than the occasional training. To truly make security a fundamental pillar, it must be embedded deeper within the organization’s engineering teams and software development life cycles (SDLCs). The latest trend in operationalizing security within tech organizations is the melding of DevOps and security professionals into a joint DevSecOps team and bringing automation, along the domain of quality assurance, into the security toolset to further reduce risk.

Experienced DevOps professionals have long understood their responsibility for keeping tabs on the security risks in their purview, but in many organizations they don’t necessarily have the tools or backing to delve more broadly into security. Often team members have I-shaped skillsets and responsibility areas and their window of involvement in security are kept very narrow and strictly within the operational activities of their team. This makes operationalizing security across the organization much more difficult. This is especially true in larger organizations where the collaboration barriers between teams and departments are more rigid and having security personnel siloed is itself a vulnerability. When security is only a small sliver of individual or team responsibilities, issues are found much later in the process and the risk level and cost of remediation rises.

This is mitigated by embedding security within other teams in the organization. Lately, the trend towards blended DevSecOps teams with broader security oversight helps address the challenges organizations face by removing the silos and barriers of collaboration within this area of the organization. This allows security to be shifted within company workflows facilitating discovery and mitigation of risks and vulnerabilities. It acknowledges the scenario that the later a vulnerability is discovered, timely remediation, spent effort and risk exposure are more costly. Ensuring security guardrails are in place earlier in the development cycle reduces the cost of security and compliance programs and reduces the likelihood of high risk issues making it to production without  a mitigation strategy.

A lot of companies claim security is everyone’s job. But often the culture of security ends at annual anti-phishing trainings and/or the occasional confidentiality discussion. Embedding security experts into teams like DevOps and ensuring experts are hired with enough security knowledge in their toolbox brings it deeper into the company’s culture. Once planted, those roots will grow. In many cases converting to the DevSecOps model will be mostly painless. The actual team process will largely stay the same regardless of the development model the team is using although this shift is especially effective in agile organizations. Workloads should also remain stable—if not decrease—as the time is no longer spent on fixing vulnerabilities in production.

While putting security experts in roles on a DevOps team is a start, it is ever more important to hire DevOps engineers with T-shaped skillsets. While they may not be deep experts, the critical part is that they have security and compliance skills in their repertoires. Their primary duties day-to-day may not be security focused, however in their work with other teams, their security consciousness and conscientiousness will rub off. and spread. They will find issues earlier in the pipeline and the teams they work with will will learn from them and gain the knowledge to spot and even prevent issues. They bring a concern for security standards into their interactions with other engineering teams and over time they help transform the overall culture of engineering into one that is more security conscious.

Automation also plays a key role in risk reduction. Much like automating tests to find bugs earlier, shifting security monitoring left and automating vulnerability-checks and adherence to security policies will save additional effort and money later down the line. While shifting to this DevSecOps model reduces risk, relying on manual processes only goes so far. Automation reduces the human element and the chance something will be overlooked. Properly designed automation tools shift the possibility of human error even further to where it is very easy to remediate. Having skilled DevOps engineers with security experience who can automate these activities pays off.

In addition, team empowerment is incredibly important. By shifting activities left and automating, risk can be reduced incredibly. However, it’s impossible to completely negate it. It’s critical for teams to be empowered to speak up and have proper ways to report any issues found. Making the changes to bring security into DevOps and automate the process mentioned will go a long way towards building the culture of security consciousness that is needed for this. Embedding security deeper in teams and making it part of their daily workflows and conversations shows employees that the company prioritizes security in their operations and cares about vigilance and conscientious reporting.

Adding security consciousness to the DevOps services available to the rest of the company will ensure that risks are considered earlier in the pipeline. This saves companies time and money in remediating those risks and will come at little to no cost to team workflows and velocities. Automation further reduces risk by shifting security even farther in the process and it reduces the risk of human error. These changes also make security a deeper part of company culture. Disseminating knowledge—and attention to the details related to security to the teams and employees with whom DevOps works closely—empowers the entire organization to make security a priority. Vulnerabilities and compliance issues will be found much earlier in the process reducing the cost of fixing them and the risk of production incidents.

Source: ibm.com

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HR 3.0: It’s time to reinvent HR

The human resources (HR) profession plays a heroic role in business. As companies deal with issues such as public health, resilience and rapid business transformation, the HR department has become more important than ever.

Historically, HR departments have largely been administrative, with responsibility for hiring, pay, compliance and basic job design. To push HR beyond traditional roles, forward-thinking companies need to focus on the employee experience and drive reskilling, cultural transformation and an evolution to new models of work.

Our research clearly shows when businesses make this shift, they far outperform their peers. That’s because employees who feel a sense of belonging, purpose, achievement and happiness at work are likely to perform at higher levels and to contribute above and beyond what’s expected of them. Unsurprisingly, they’re also less likely to quit. 

But today’s enterprises are far from universally able to create these experiences for employees despite investments in human resource management software, digitization and technology. There is also little evidence employees are more deeply engaged—or happier—than they’ve been in the past.  

Businesses that undertake strategic digital transformation have an unmatched opportunity to reinvent the employee experience and to put people’s needs first. As companies move data and applications to hybrid cloud environments, they can simultaneously transform processes, workflows and mindsets to become people-centric organizations. This transformation is referred to as HR 3.0. 

The HR 3.0 transformation

Companies are deploying new technologies at scale—especially those referred to as “exponential” because their impact scales so quickly—to make use of the massive volumes of data produced and captured by devices in an Internet of Things (IoT). Businesses use these technologies, especially artificial intelligence (AI) and automation, to build new business platforms and restructure the flow of work across extended ecosystems. 

The global pandemic ushered in a massive shift in where work gets done and with diminished human contact due to staff working offsite, enterprises must become inherently humanized, build engagement with remote employees, foster trust in in a climate of uncertainty and cultivate a resilient workforce capable of facing whatever the future may hold. 

HR 3.0: A business imperative that turns HR into an agile consulting organization

In its studies of hundreds of global companies, IBM found HR departments fall into three categories:  

1. HR 1.0, the traditional departments focused on compliance, administration and efficient service delivery.

2. HR 2.0, when teams have moved toward integrated centers of excellence and focus on training and empowering business partners to deliver solutions.

3. HR 3.0, which only 10 percent of companies have achieved, turns HR into an agile consulting organization, one that not only delivers efficient services, but also practices design thinking to push innovative solutions, cognitive tools and transparency.

IBM research shows why a radical reinvention of human resources is critical for organizations. More than two thirds of the executives we surveyed believe that the global HR function is ripe for disruption. Even more convincing, the best companies in the world—those outpacing all others in profitability, revenue growth and innovation—are extremely confident and committed about the need to reinvent HR.  

More than 2/3 of executives surveyed say the global HR function is ripe for disruption. 

We uncovered widespread agreement on five common characteristics that underpin HR 3.0: 

1. Deeply personalized experience-centric design 

2. Skills placed at the core of the enterprise 

3. Data-driven decision making powered by AI 

4. Agile practices for speed and responsiveness 

5. Consistent transparency to preserve trust and reduce reputational risk 

Employee experience is central in HR 3.0, as it drives a company’s overall enterprise transformation. The HR function becomes more automated and AI-driven, more data-centric and consultative and more agile.  However, achieving this future vision is not easy. Only 30 percent of companies tell us they are living some of the principles today and only one in ten are leading in all five. 

The exciting thing about our findings is that HR 3.0 is not an idea: it’s a reality you can achieve by deploying design thinking and highly intelligent cognitive tools, with a focus on transparency, inclusion and change.  

Every organization is rethinking work, ways to support people and the roles of technology and leadership. In a business landscape characterized by near constant disruption, HR 3.0 is the next evolutionary step.  

Below are areas with associated implications and impacts for the organization on the HR 3.0 journey.

The journey to HR 3.0 helps enterprise be resilient, responsive and inclusive by focusing on talent, employee needs and experience. Using AI, employees experience a consistent work life that develops, aligns with and informs a culture of inclusion.

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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20 ways smarter asset management paves the way to more sustainable operations

As companies address their role in mitigating environmental impacts, one thing is very clear. A more sustainable world needs the industrial world to build more sustainable operations.

The good news is that for heavy industry, sustainability has been on the forefront for years. Adopting environmentally sound practices helps conserve energy, reduce waste, and minimize regulatory costs, which is good for business, too — “doing well by doing good.” Asset management and reliability play a key role. That’s why the smartest asset management professionals have long aligned their operational metrics to reduce cost and waste and boost efficiency, all of which are critical to a sustainability strategy.

The age-old strategy of reduce, recycle and reuse still holds the key to improving the environment and the sustainability of industrial businesses, consider the impacts.

◉ Industrial businesses leverage and produce assets, and both have the potential for waste. It is estimated that 80% of the materials leveraged and created by manufacturers end up as waste that directly and indirectly impacts the environment. How can we reduce this impact?

◉ Consider that humans produce over 100 billion tons of material every year, but only 13% is recycled at a global level, and you realize that controlling assets, inventories and materials from cradle to grave is a critical aspect to building a sustainable future.

◉ Imagine if the world recaptured more of that 100 billion tons of material it creates and devised innovative ways to put it back into use. In the asset management world, reuse focuses on physical and digital materials that lessen energy costs and waste, as well as ways to put assets and materials back in play.

We looked back on thirty years of partnering with some of the most industrialized businesses on the planet to share ways that IBM Maximo users reduce, recycle and reuse their physical and digital assets and knowledge. Today we’ll spotlight 20 ways to help build more sustainable operations — and a better world — simply by doing a good job.

Tips to extend your asset life and reduce material impacts

1. Reduce the number of assets sent to landfill by using condition-based maintenance and remote monitoring to keep every asset in peak condition

2. Reduce unplanned downtime and unnecessary space utilization by maintaining the optimal inventories with MRO inventory optimization

3. Extend the lifecycle of assets by reducing performance issues that drive early replacement

4. Develop and track asset hierarchies and component-level detail to reuse parts from assets scheduled for disposition that could be reused in other assets

5. Improve daily performance with condition-optimized preventive maintenance scheduling

6. Tackle the lifecycle holistically with support for standards like ISO 14040 that support a full assessment of the impacts from deploying assets and products

7. Model disposition from the beginning to balance accounting with production value and future environmental impacts (and potential costs)

Tips to reduce energy and effort in your workforce

1. Reduce unneeded labor and improve wrench time with a focus on criticality of assets

2. Reduce required rolls with optimized scheduling to bundle maintenance jobs and inspections

3. Reduce in-house infrastructure with cloud-based solutions hosted outside the company datacenter

4. Recycle critical field skills with centralized functions for monitoring and peer guidance

5. Recycle logs and records (including video, documents, etc.) and use AI to identify the highest probability approaches that drive the right outcomes, and improve job plans

6. Reuse best-practice data, processes and real-time peer interactions (live video chats) to support just-in-time repair guidance and create virtual training material for new technicians that drive first-time-fix rates up

7. Reuse the knowledge of your industry with best-in-class data model, job plans, industry solutions

8. Engage in sharing and harvesting best practices in communities like the IBM Maximo community and user groups

Tips to reduce the use of harmful pollutants

1. Reduce overall fuel consumption/mileage with route optimization and mobile tools that give technicians the asset information they need, no matter where they are

2. Reduce costly and unnecessary environmental impacts with Environment, Health and Safety (EHS) tools that monitor and ensure compliance regulations

3. Reduce the use of chemical lubricants and standardize on less-harsh options that last longer

4. Have detailed data on all recyclable materials used in maintenance, and a disposition plan embedded in job plans

5. Track and identify alternate uses for lubricants and materials (even with other vendors) to ensure every ounce of utility is derived from supporting materials

By adopting and executing these 20 process suggestions that consider environmental impact while also creating value for the employees, shareholders, customers and the communities in which they operate, your business can deliver more sustainable operations and a healthier planet.

More resources for your sustainability journey

From reducing the complexity of compliance and reporting, to minimizing your overall environmental impact while also lowering cost, IBM is uniquely positioned to help. Schedule time to talk with one of our experts about how you can better measure, monitor, and predict your organization’s environmental footprint, accelerate your sustainability actions and drive real results.

Listen to the webinar “Doing well by doing good: why sustainable operations are good for everyone.” It’s a thoughtful discussion with global research firm, IDC and IBM on the role of sustainability in asset-intensive operations.

Explore the IBM Maximo Application Suite and discover how Maximo can create a more sustainable manufacturing operation.

Read more on the topic of sustainability and the role of technology in helping companies reduce their impact on the planet. Reports include Sustainability as a business strategy and The rise of the sustainable enterprise.

Source: ibm.com

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