4 failure patterns to avoid in cloud modernization

Cloud computing is often described as a savior for businesses. Early success stories have shown that cloud can be used not only for improving business operations but also as an invaluable tool for driving business growth. Innovative, cloud-based platforms such as customer relationship management (CRM), e-commerce and analytics are making it easier than ever for businesses to experiment and pilot cutting-edge capabilities to increase revenue and gain market share.

These success stories have opened the eyes of CEOs and senior business executives to the value of cloud computing. Our research shows that 86% of CEOs believe that cloud is essential to deliver the results they need over the next 2-3 years.

Yet many organizations are struggling to make the business case for their cloud investments, and upwards of 30% of cloud initiatives fail. The cloud is a new way of doing things, and for most companies, it requires a different set of skills, processes and tools. Many companies apply traditional practices and existing capabilities to the cloud and fail.

In our experience, there are four primary issues that hold businesses back from realizing value:

1. Business and IT misalignment

An overwhelming majority of cloud programs tend to be driven by IT organizations. However, the bulk of cloud value is typically unlocked within business operations. To realize that value, businesses must change the ways they work. But CIOs are rarely positioned to drive these changes themselves. Business executives, on the other hand, are reluctant to take responsibility for these cloud programs because they are uncomfortable working with the cloud (and often with technology in general).

2. Underestimation of technology complexity

CIOs consistently underestimate the technology complexity associated with successful execution of cloud modernization. Cloud undoubtedly appeals to CIOs that wish to “get out of the data center business” and focus on more value-adding capabilities. There is also the undeniable beauty and promise of a highly distributed, event-driven microservices architecture on cloud that can power the next generation of intelligent applications. However, despite the many virtues of cloud, businesses are discovering that they are unable to shed the full responsibility of platform and infrastructure management. Quite the opposite, many companies find that managing hybrid environments adds a significant layer of complexity to platform and infrastructure operations.

3. Over-indexing on organization

Although CIOs appreciate the need to change their operating model to be able to work in this new cloud environment, they often treat these changes as a “boxes and arrows” exercise. In other words, they shuffle and regroup resources rather than breaking down barriers, addressing inefficiencies, and fundamentally changing the way their teams work. The only way technology teams have a hope of keeping up with expectations is if they drive very high levels of automation. Unfortunately, many companies fail to explicitly invest in the automation and AI necessary to transform IT delivery to take advantage of these new technologies.

4. Poor financial discipline

Many IT organizations lack the financial management capabilities to measure and manage cloud value. Our research shows that less than 40% of cloud programs have a well-articulated business and financial case. Some technology leaders are less well versed in IT economics and lack an understanding of how operating decisions can lead to financial outcomes. Further, in many companies, there can be a lack of adequate visibility into assets and metrics. The lack of an analytically-driven culture makes it difficult to derive a clear view of the value and efficiency of their cloud initiatives.

Being aware of these four failure patterns is the first step to being a proactive leader who can lead successful modernization programs and deliver business results.

Source: ibm.com

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Planned versus unplanned maintenance and the impact on spare parts stocking strategies

At a chemical plant years ago, a maintenance technician made a hasty pump repair. A required repair part wasn’t available, so the technician did the unplanned maintenance with another similar-looking part. But the materials of construction weren’t rated for the pump’s service, and the pump casing ruptured and vented 1,300-degree oil into the atmosphere.

Luckily, the chemical plant was able to mitigate environmental harm, and no one was in the immediate area when the life-threatening failure occurred. But the accident was a severe wake-up call. The plant couldn’t just hope the right parts were around. It needed an effective strategy to maintain its maintenance and repair inventory.

Maintenance technicians often feel immense pressure to get production up and running after an operations breakdown. Unfortunately, this burden can lead them to take detrimental shortcuts to complete emergency repairs. Though the risks associated with breakdowns are often financial, poor maintenance stocking can also cause environmental and safety risks.

Optimize your MRO inventory stocking strategy

Maintenance effectiveness can be expressed as a ratio of planned maintenance to breakdown maintenance. Planned maintenance is comprised of all planned and scheduled activity (PM, PdM, corrective, etc.) while breakdown maintenance consists of emergency work, reactive work and break/fix work that is unplanned and requires an immediate response. A good target follows the 80/20 rule: 80% planned, 20% breakdown.

MRO inventory refers to critical supplies, spare parts and other materials needed for routine maintenance, repair and operations. If you are optimizing MRO inventory, you will need to determine a few things before considering the overall impact of your maintenance team’s planning capabilities. Does your inventory optimization tool:

 

◉ Dynamically assign criticality to your spare parts?

◉ Have a “where-used” feature to associate spare parts with their appropriate assets?

◉ Consider the asset/equipment criticality in the overall assignment of the spare part criticality?

◉ Calculate a moving-average total lead time rather than relying only on stated lead time?

It’s important to know how critical each part is before you apply forecasting algorithms and cost models. Tools designed for the manufacturing, service and retail industries have forecasting algorithms that focus on procurement and sales data rather than asset and work order data. These algorithms don’t factor in criticality and the business risk associated with criticality, which is crucial for MRO inventory stocking strategies. I like this analogy: If a grocery store runs out of bread, business is hardly interrupted, and the store does not shut down. However, in most industries, a lack of certain parts can prolong outages or potentially shut down operations completely.

Planned versus unplanned maintenance and the impact on stocking strategies

If you are effectively planning and scheduling 80% of your maintenance activities, planned parts should be ordered and available within the stated lead time. Proper planning provides a clear demand signal for spare parts, which eliminates the need to stock for planned work. Planning thereby reduces the need for stocking, maintaining, and managing spare parts. As such, you should stock enough parts to cover the 20% of unplanned work.

It’s important to track spare parts purchases against planned and unplanned maintenance. In addition to achieving the 80% planned work ratio, businesses must consider whether the scheduling window adequately accommodates supplier lead time. If parts cannot be delivered in time, maintenance may be forced to use safety stock to continue operations. When safety stock is depleted for planned maintenance, operations are placed at risk when an emergency arises. A stockout event often leads to increased stocking levels, which creates a ripple effect that increases maintenance budgets, reduces profit margins, increases space requirements and leads to surplus. Today’s surplus is tomorrow’s potential waste.

It is important that the planners have access to an inventory optimization tool that calculates a total lead time (including requisition to order time, supplier weighted average time and delivery time). Having visibility into total lead time allows planners to fully execute their planned maintenance and have the right parts available within the lead time window. If your planning is not effective, you will end up holding more inventory for longer periods, even with an inventory optimization tool. The forecasting algorithms will adjust stocking strategies to include all consumption (unplanned and planned) rather than planning and procuring as needed and keeping stocking levels appropriate for the 20% of unplanned work. Over time, this practice leads to more obsolete, wasted inventory.

Keep your maintenance planning effective by understanding total lead time for required parts. Keep your inventory optimized by stocking for the unplanned, emergent work. Find an inventory optimization tool that is designed for the unique requirements of maintenance activities, and you’ll be able to put the right measurements in place to ensure success.

Source: ibm.com

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How to put sustainability at the heart of your business

International businesses must mature more quickly into sustainable, future-proof enterprises that significantly contribute to the pressing environmental net-zero deadline of 2030. Pursuing the three themes of leadership, innovation and education will help them achieve this. 

Visionary leadership with a commitment to shared value

The world needs brave, visionary leaders. Business leaders must understand and empathize with sustainability and recognize the imperative for innovation across the business model.

Revisiting a business’s purpose and place in the broader business environment is a chance to position sustainability at the core of business operations and strategy. Overall, the leadership team must empower its employees to pursue shared value creation.

New metrics and KPIs must be created to help leaders understand how and when to reward new sustainability behaviors. Scenario planning as a regular leadership activity is critical. Having diverse stakeholder relations, including government, suppliers and community leaders, will bring insight into scenario planning. Leaders can leverage this insight to increase business agility and responsiveness to external shocks. AI-driven insights will encourage forward predictive planning rather than looking to the past.

Innovate: prioritize for impact, co-create, build new value pools and reinvent

First, businesses need to baseline their current operations and assess these against ESGs, GHGs, and SDGs. Doing this provides immediate priorities and identifies areas for innovation, like a rapid transition to renewables.

Work with the business leaders and key external stakeholders to co-create an innovation strategy grounded in sustainability and aligned to the overall business plan, outlining how the firm will create, capture, and share new value.

Reconsider the whole business model and reinvent it. Transition business models need to allow the “new” company to flourish alongside the “old” business that is slowly “decommissioned.”

Insulating the new business from old behaviors, metrics, processes, and reporting norms will be a challenge while allowing the new business full access to existing core competencies, expertise, and networks.

Education: citizens, employees, shareholders, stakeholders

State, national and international regulations are creating a broader market for sustainable products and services. This means businesses can help build customer-driven demand through education. When consumers become disconnected from the value chain of the products they buy, they are often unaware of the mix of raw materials, processing steps, or the global supply chain that brought it to them. Consumers need to understand the true value of products, the effects of their waste and the steps that sustainable businesses are taking to create broader societal value.

Investing in business change and education programs is a priority. To build sustainability knowledge throughout the workforce, businesses must create new “mental models” for how work will now get done. Businesses must also instill new business processes that reinforce a sense of community and identity around “shared value” business practices. Incentives and rewards are essential steps to support employee learning, consumer education and culture change.

Businesses must demonstrate commitment and transparency to shareholders in their sustainability and long-term initiatives. Companies must be able to articulate the total societal value their activities create to educate shareholders or else be judged by environment, social, and governance (ESG) metrics alone.

Sustainability is a mindset change and needs significant leadership, behavioral, metrics, partnership and operating model change. Technology innovation is a key enabler to sustainable business models.

Conclusion

Putting sustainability at the heart of business models drives complicated strategic change, and business leaders must be willing to compromise. Businesses must peel their business model down to its core value set and challenge who they are, how they operate, who they partner with and how they create and share value with the community.

First movers are demonstrating progress in sustainable solutions almost daily. The challenge is how to scale fast enough. Unfortunately, many incumbents will falter and continue exploiting their existing capabilities and assets, ignoring future-proof opportunities.

The world needs the first movers to succeed. We must champion sustainable societal and governance standards to build businesses that thrive in new sustainability-based markets with new terms of engagement and reduced environmental effects.

Companies that change their business models to create shared value will be the beacons of international business and the guardians of nascent “pay it forward” momentum.

Source: ibm.com

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How a solid partner relationship management strategy boosts revenue and drives value for your organization

In today’s competitive, digital-first landscape, embracing customer intimacy is the most important key to success. Fostering user intimacy ensures that one-time buyers become lifelong advocates. That focus extends beyond traditional B2C & B2B customers — your partners expect personalized experiences that make doing business easy and efficient. Creating true intimacy with your partners requires an intense focus on who is engaging with your products and services. It’s important to prioritize these relationships, because a positive partner experience is an essential part of fostering customer intimacy and ultimately driving equitable outcomes.

When companies seek to understand who their traditional B2C buyers are and how to serve them best, they naturally explore the partner relationships that products and services pass through en route to the end customer. Investing in partner relationship management (PRM) strategies not only provides insights and collaboration at inflection points and decisions along the sales process, it also creates access to end-customer market data.

If you want to generate customer intimacy while successfully driving more equitable outcomes, then your resellers, distributors and partners need processes and tools to effectively share inbound leads and ultimately book and deliver business.

What’s happening with channel sales (PRM) right now?

If you manage partnerships and sell through resellers, brokers, distributors or dealers, one thing is certain: your success depends on the success of your partner ecosystem. As of 2022, 75% of world trade flows through indirect channels. Current trends in the PRM space further demonstrate the importance of generating intimacy from your channel sales motions. These include:

◉ Increasing shifts towards SMB and indirect channels for revenue growth

◉ Channel conflicts and inefficiency in optimizing indirect channels

◉ Inability to use emerging collaborative tools to successfully drive indirect channel sales

◉ Lack of visibility into channel activities, reducing overall channel effectiveness

With this increased focus on channel sales, enterprises need to understand the features that enable success in today’s digital landscape. The route-to-market channel is anchored in these core business capabilities that creates ease for your partners:

◉ An account structure that aligns partners to geographies and tiers, tracks revenue and referrals, and reports operational and financial data in real-time

◉ A partner onboarding process that reduces cycle time with automated forms and flows that gives faster speed to value and saves and tracks status

◉ A deal registration process that includes guided, dynamic flows to capture and route info with clean, consistent registration experiences (UX/UI)

◉ A partner community with customized branding that allows partners to collaborate on business plans, campaigns and shared docs, including robust reporting and analytics

◉ Effective lead distribution so that the right leads get routed to the right partners and representatives that will reduce competitive issues and channel conflict

◉ Partner support that allows you to promote important information, rewards engagement with tiered incentives and enables streamlined case management

Overcoming common PRM challenges

When you optimize the PRM experience through all aspects of their journey (onboarding, enablement, deal management, training and engagement), you create a sticky partner network that maximizes value for your business. And creating that network matters now more than ever. In fact, by 2025, 80% of B2B sales interactions between suppliers and buyers will occur in digital channels.

But as promising as the indirect sales business model is, many businesses struggle to unlock its full value due to these common challenges:

1. Dated, manual business processes make it difficult to adapt to the changing needs of your channel partners. The culprit is often inflexible legacy systems that are overly complicated and clunky to manage.

2. Winning partner mindshare can be difficult when partners may carry dozens or hundreds of different products, including those of your competitors. Being easy to do business with is the way to stand out – but static portals don’t create the seamless experiences that partners demand.

3. The channel can present challenges with finding and accurately capturing data. Companies are tasked with making data-driven decisions to track and grow their businesses, but they can’t manage what they can’t measure. The lack of visibility stems from disconnected data that’s scattered across many systems.

To overcome these challenges, put yourself in the shoes of your users who are enabled by processes and technology. Part of fostering partner intimacy is understanding and helping them solve their pain points. Manual processes and static portals should become personalized, intelligent experiences. Clunky legacy look and feel must evolve into a more delightful B2C-like UI. Stale information should be replaced with fresh content and real-time data. And limited productivity must be overcome by making resources accessible on demand from anywhere.

Today’s businesses meet these needs and expand their growth by teaming up with their ecosystem partners, co-innovating to bring solutions to market faster, and co-selling collaboratively to accelerate deal closing and increase win rates. The most innovative companies partner with several co-sellers to offer a satisfying, whole-lifecycle experience to their customers.

By leveraging Salesforce Experience Cloud’s tools, you can become the preferred brand by giving your partners (dealers, distributors, agents) the resources they want. Strengthening your sales cycle with a strong PRM unlocks line of sight into the data flowing from interactions and helps ensure you provide the customer (and partner) intimacy your users expect.

Business outcomes of your PRM solution

Making it easy for your partners to do business with you makes business sense. By simplifying touchpoints and using the data at your disposal, you’ll maximize business value and build an industry-leading partner network. Implementing a consolidated, personalized, intelligent digital experience is the best way to achieve a variety of business outcomes:

◉ 46% increase in partner engagement

◉ 25% increase in channel pipe growth

◉ 33% increase in deal registration

When your partners learn that it’s easier to do business with you than with others, revenue will increase thanks to better lead management and partner enablement resources. Cost to serve drops as collaborative forums and knowledge bases let partners answer their own questions. The smarter and faster dashboards and analytic processes made possible by a partner experience help achieve other goals as well:

◉ Increase partner revenue and collaboration

◉ Increase partner retention, advocacy and satisfaction

◉ Reduce time to productivity and cost to serve partners

◉ Improve partner communications

◉ Manage partner performance

◉ Increase conversion and shorten deal cycle time

How to implement a PRM solution in four steps

Implementing a PRM solution focused on intimacy will set your partners up for success, so they can be more productive and close more deals. With a full-fledged partner experience, you’ll streamline processes, boost visibility, enhance collaboration and unlock a centralized hub for access to resources. Regardless of where you’re at in your PRM implementation journey, there are easy steps you can take to get started.

First, you need to identify the key elements of your PRM:

◉ WHO will be the key users of your partner experience

◉ WHAT business processes and rules will be integrated to start

◉ WHICH technology will be used to create the partner experience

◉ WHO from your organization needs to champion this effort

◉ HOW information will be captured, organized and structured

After taking these initial steps and regularly innovating within your experience, you’ll be able to see and analyze the progress your organization is making. You’ll know you’ve been successful when your partner experience:

◉ Delivers partners relevant and impactful information

◉ Enables partners to sell, serve and grow more effectively

◉ Provides a unified way to engage with the vendor

◉ Keeps partners informed about their leads, opportunities and business

◉ Drives productivity by eliminating friction and streamlining processes

◉ Fosters long-term partner relationships based on mutual success

IBM has guided dozens of Salesforce PRM strategy implementations, so we understand the best ways to empower and enable your channel strategy. Partnering with IBM is the best way to unlock these features and exceed your partners’ expectations for a positive experience. With tools and checklists in place, you’ll ensure that your PRM implementation drives customer and partner intimacy and maximizes value for your business now and in the future.

Source: ibm.com

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Optimize commercial spend and profitability in the life sciences

Apharma sales representative visits doctors with varying ability to prescribe a drug to their patients. A TV commercial blankets a region where few people need the advertised drug. A hospital specializing in rare cancer treatments wants to consider a newly approved therapeutic product, but the life sciences company has yet to engage with them. Wasted commercial spend and missed opportunities keep life sciences companies from reaching their full business potential. How do these misspends still happen, and how can companies address them?

Examine commercial spending habits

For decades, companies in the life sciences industry have invested their sales, marketing and advertising budgets uniformly across US geographic regions and channels. They struggle to optimally reach healthcare providers and patients. They overspend just to maintain the status quo, missing scores of unseen opportunities. Instead, companies need to target and invest strategically in geographies and channels with the highest potential return.

For example, many pharmaceutical companies still invest a high percentage of their overall budget on sales and marketing initiatives in geographies where their brands do not have a significant market access position. Additionally, significant investment is made in regions dominated by integrated delivery networks (IDNs) such as Intermountain Health, Kaiser Permanente, and Advocate. These organizations have a decision-making structure driven by their internal pharmacies and therapeutics (P&T) committee — not the individual health care providers (HCPs) — that determines whether a brand can be administered. It is therefore imperative for marketing, sales and market access to coordinate in tandem along with their center of excellence (CoE) support teams, such as commercial operations and analytics, forecasting, finance and contracting, to most efficiently deploy promotional dollars.

Differences in profitability observed across US geographies. Each bubble represents a blinded geography, sized according to revenue.

Use data and AI to optimize spend

Life sciences companies have a significant amount of data, more than enough to drive optimal commercial investment. But the data is complex, messy and decentralized, and comes in many shapes and sizes. Some examples of this data include:

◉ Third-party data: IQVIA (Xponent Plantrak, DDD, HCOS), PRA, Nielsen advertising and media data, social determinants of health (SDOH), Fingertip Formulary, co-pay, claims data

◉ Government data: TRICARE, CMOP, TMOP, FSS, VA

◉ Internal promotional data: details, samples, speaker program, omni-channel promotions

To make sure all this data is usable, companies need data analysts to architect and engineer the data, business rules and assumptions.

With the right mix of integrated data, an understanding of historical performance and implementing AI to get a forward-looking view, the life sciences industry can make far better decisions about securing contracts with key payers and determine which promotional channels are most effective for each geographic region. Differentially using promotional channels such as peer-to-peer, sales rep visits, tele-detailing and digital libraries will ultimately lead to optimal commercial spend across channels and geographies.

This idea is easy to grasp: Use the data to understand how best to distribute investments and resources, such as brand marketing and sales outreach. But because the data is so diverse, its value is not always immediately clear. It takes focused effort and expertise to cleanse, categorize and bridge this data effectively.

Managing and exploiting this data becomes much simpler with a data fabric. Instead of laboriously pulling all their data into a centralized location, life sciences companies can tie various elements together by using that data wherever it resides within the client ecosystem. Specifically leveraging data fabric across the hybrid cloud will enable companies to knit together complicated and diverse commercial data sets. After pulling the elements together, companies can analyze and benchmark the data by geographic region, promotional spend and discounts to provide historical insights on performance and cause and effect.

By leveraging AI and machine learning-driven insights and pathways in revenue and profitability across channels, we can best predict optimal commercial growth. Brand leaders can then prioritize investments across the various promotional and payer and provider channels for each geographic region, ensuring their therapies and medications are finding their way to the patient markets that need them the most. AI technology can optimize for differences in patient socioeconomic needs, enabling life sciences companies to target areas with pricing that aligns with the geography.

Optimizing commercial spend by geography informs brand, therapeutic and company strategy

What if…

…you can look up what geographic and brand mix drives the most profitable growth?

…you have an omnichannel view of which promotions are most effective in each geography?

…you have a framework that helps align all major organizational commercial stakeholders on brand, portfolio, and strategic execution to grow your business?

Source: ibm.com

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Predicting the winners: How IBM uses data and AI at the US Open to drive insights and fan engagement

As the US Open’s official technology partner, IBM Consulting works with the United States Tennis Association (USTA) to turn tennis data into engaging fan insights through AI and automation.

Artificial intelligence (AI) increasingly influences people’s understanding of data, whether we are a CTO making decisions at an enterprise level or a tennis fan deciding which match to watch. For the USTA — as with all our clients — IBM is committed to delivering explainable AI, following processes that allow people to understand and trust the results and output produced by machine learning algorithms.

This process begins with solid and reliable data. At the US Open, this comprises a massive volume of structured and unstructured data from a wide variety of sources:

➤ Data on 128 men and 128 women players, including age, height, weight, tour ranking and recent performance

➤ 7 million play-based data points, including serve direction, return shot type, rally count and ball position, captured throughout the tournament

➤ Language and sentiment from 100 million news articles

Let’s look at how IBM combines and analyzes this data to deliver statistics and analytics in natural language, and how we break open the “black box” of AI to deliver trustworthy, explainable insights that complement outside media sources.

How the IBM Power Index analyzes player momentum

Tennis is a game of momentum. Within matches, top players use it strategically. They can serve quickly when winning or max out the serve clock to break their opponent’s rhythm. When down two breaks in a set, they may cede momentum to their opponent to preserve their energy for the next set. This strategy is particularly useful in the best-of-five men’s singles format of a Grand Slam tournament like the US Open.

But momentum is also a factor from day to day and tournament to tournament. It helps explain long winning streaks and why qualifiers sometimes make deeper runs than expected through main draws. These streaks and upsets are captured and amplified by the news media, which can in turn enhance a player’s confidence, invigorate courtside crowds and affect match outcomes.

The IBM Power Index quantifies player momentum leading into the US Open and day by day within the tournament. This index complements the official ATP and WTA rankings that are based on a 52-week rolling window.

The Power Index incorporates over 25 factors. Player performance factors include win-loss ratio, win margin, rank differential, court surface, injury status, number and level of tournaments played and round progression. These factors are interpreted by IBM Cloud® Functions, a serverless programming platform that pulls statistical data from SportRadar.

The Power Index also analyzes media sources using the natural language processing capabilities of IBM Watson® Discovery. Sentiment around player performance and health is analyzed from industry punditry and fan perspectives, drawn from hundreds of thousands of trusted news sources.

From these analyses, a series of predictive insights are generated, including the following:

◉ Ones to Watch: A pre-tournament view of players whom the Power Index identifies as five or more positions higher than their current tour rank.

◉ Upset Alerts: Matches in which the Power Index favors the lower-seeded player.

◉ Likelihood to Win: A win probability model for each player. This statistic can shift daily to reflect the latest performance and punditry data.

Behind Match Insights with IBM Watson

Match Insights with Watson are AI-generated fact sheets that draw on natural language processing, AI and statistical analysis in addition to the Power Index. They provide at-a-glance data and help fans understand key factors affecting win predictions. Match Insights are broken down into three sections: “In the Media,” “By the Numbers,” and new at this year’s Open, “Win Factors.”

The “In the Media” section presents qualitative player information that was extracted using Watson Discovery, as described in the Power Index section above. For use in Match Insights, extracted sentences are evaluated for grammatical coherence and topic alignment, then sent to human operators to review and approve.

The “By the Numbers” section gives fans statistical insight in the forthcoming match. It draws contrasts by highlighting the most differentiated stats between the two players, then converts these stats into natural language.

“Win Factors” reveals the reasoning behind Match Insights. It explains how the AI model works and why a player is predicted to win by listing the top three factors, such as Power Index rating, winning record on this surface and head-to-head record. Win Factors are evaluated with AI Explainability 360, an open-source toolkit for understanding machine learning models.

In tennis, trusted, explainable AI lends validity to predictions around a spectator sport. But IBM solutions for trustworthy AI are also employed in higher-stakes environments. For example, Neighborhood Trust Financial Partners, which helps workers reduce and avoid predatory debt and build savings, uses the AI Explainability 360 toolkit to provide transparency and explainability on how AI algorithms will respond to their financial decisions.

IBM is using AI to transform data into insight in a variety of industries — helping Lufthansa agents address 100,000 customer queries a year, informing public health decisions for the state of Rhode Island and helping Citi auditors skip thousands of hours of manual transcription. Find out how you can unlock the full potential of your organization’s data with IBM.

Source: ibm.com

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How IBM Consulting and the US Open evolve the fan experience and accelerate innovation

IBM® has been the official technology partner of the US Open Tennis Championships for more than three decades, and the relationship goes much deeper than courtside logo placement. It’s an ongoing partnership delivering world-class digital experiences to fans, built on IBM’s open, flexible technology platform. “We need to constantly innovate to meet the modern demands of tennis fans, anticipating their needs, but also surprising them with new and unexpected experiences,” says Kirsten Corio, Chief Commercial Officer at the United States Tennis Association (USTA).

Read More: C9560-507: IBM Tivoli Monitoring V6.3 Implementation

Year after year, IBM iX, the experience design arm of IBM Consulting™, works with the USTA to integrate technology from dozens of partners, automate key business processes and use the power of artificial intelligence (AI) to transform vast quantities of tennis data to deliver key insights.

Bringing fans closer to the game they love

This year’s tournament features colorful personalities and compelling stories. But as host of a leading spectator event viewed by nearly 10 million people every year, the USTA is charged with delivering ever more engaging experiences. IBM Consulting asked: How can we use the digital experience of the US Open to serve the USTA’s mission and grow the game of tennis? How can we better serve fans with live scores, stats and player information while they watch a live match? How can we deliver the answers they need? How can we provide relevant and timely insights they can’t find anywhere else?

These questions led to several innovations: The IBM Power Index with Watson ranks player momentum and combines performance and punditry, queried through IBM Watson® Discovery, to create a “Likelihood to Win” prediction and highlight compelling matchups. Match Insights with Watson delivers head-to-head pregame analysis of every match, using natural language generation to translate historical statistics into easily read sentences. And US Open Fantasy Tennis, enriched with Match Insights, lets fans create and follow their own fantasy team.

A collaboration that drives innovation

Creating digital experiences that drive enthusiasm requires a human lens. To achieve that, the US Open digital strategy team partners closely with IBM iX, one of the largest business design consultancies in the world. IBM iX uses collaborative design thinking brought to life by the IBM Garage methodology — an end-to-end model for accelerating digital transformation — to address challenges within a variety of management frameworks including lean startups, human-centered design, agile and DevOps.

Stage 1: Co-create

At the co-create stage, squads agree on the nature of the challenges, prioritize them and conceptualize solutions. For example, for the 2022 US Open, a top priority was providing more explainability to the “Likelihood to Win” prediction.

Stage 2: Co-execute

At the co-execute stage, development teams build minimum viable products or solutions, and test them. Using this process, IBM and USTA developed the “Win Factors” feature, which shows the top three variables affecting the prediction such as head-to-head record, winning record on this surface or Power Index rating.

Stage 3: Cooperate

The cooperate stage is not simply about operational maintenance. It’s about ongoing performance management, improvement and product development. The USTA and IBM Consulting cooperate virtually year-round to develop and refine the digital experience, starting with a debrief after the tournament asking questions such as Where did we succeed? What could be improved? How can we be more efficient and effective?

Beyond solving the problems at hand, co-creating with IBM Garage can be a transformative experience for organizations, helping them prioritize their development queue, iterate solutions and evaluate them in a cycle of ongoing improvement. Using this method, IBM and the GRAMMYs delivered artist insights for live coverage based on IBM Watson analysis of millions of articles. IBM and the Masters® built a digital platform to scale the capabilities of the Masters Digital team.

Over 30 years in, IBM and the US Open continue to overcome new challenges and engage fans with new experiences. For a tournament, fan expectations and technology that are always evolving, this partnership keeps the USTA ahead of the ball.

Source: ibm.com

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3 new steps in the data mining process to ensure trustworthy AI

Sometimes as data scientists, we are often so determined to build a perfect model that we can unintentionally include human bias into our models. Often the bias creeps in through training data and then is amplified and embedded in the model. If such model enters a production cycle it can have some serious implications directed by bias such as false prediction of credit score or health examination. Across various industries, regulatory requirements for model fairness and trustworthy AI aim to prevent biased models from entering production cycles.

How does a data architecture impact your ability to build, scale and govern AI models?

To be a responsible data scientist, there’s two key considerations when building a model pipeline:

1. Bias: a model which makes predictions for people of different group (or race, gender ethnic group etc.) regularly discriminates them against the rest

2. Unfairness: a model which makes predictions in ways that deprive, people of their property or liberty without visibility

Detecting and defining bias and unfairness isn’t easy. To help data scientists reflect and identify possible ethical concerns the standard process for data mining should include 3 additional steps: data risk assessment, model risk assessment and production monitoring.

1. Data risk assessment

This step allows a data scientist to assess if there are any imbalances between different groups of people against the target variable. For example, we still observe that men are accepted on managerial position more frequently than women. But we all know that it’s illegal to offer a job based on gender, so to balance out the model you could argue that gender shouldn’t matter and could be removed. But what else could you impact by removing gender? Before acting this step should be examined with the right experts to determine whether the current checks are enough to mitigate potential bias in the model.

The goal of balancing the data is to mimic the distribution of data used in the production—this is to ensure the training data is as close as possible to the data used real time in production environment. So, while the initial reaction is to drop the biased variable, this approach is unlikely to solve the problem. Often variables are correlated and bias can sneak in through one of the correlated fields, living as a proxy replacement in the model. Therefore, all correlations should be screened before removing the bias to ensure its truly eliminated.

2. Model risk management

Model predictions have immediate and serious implications—in fact, they can change someone’s life entirely. If a model predicted that you have a low credit score it could affect everything in your life as you struggle to get credit cards and loans, find housing and get reasonable interest rates. Plus, if you don’t get a reason behind the low score, there’s no opportunity for improvement.

The job of data scientist is to ensure that a model gives the fairest outcome for all. If the data is biased, the model will learn from that bias and make unfair predictions.  Black-box models provide great results, but with little interpretability and explainability making it impossible to check if there are any red-flags to ensure fairness. Therefore, a deep dive into model results is necessary. Data scientist needs to assess the trade-off of interpretability versus model performance and select models which best satisfy both requirements.

3. Production monitoring

Once a model is developed by data scientists it is often handed in to MLOps team. When the new model data is put in production it can bring a new possibility of bias or enhance the bias which was previously overlooked without proper monitoring in place. Production data can lead to drift in performance or consistency, and infuse bias into the model and data. It’s very important to monitor models by introducing proper alerts indicating deterioration of model performance and a mechanism for deciding when to retire a model that’s no longer fit for use using a tool like IBM Watson Studio. Again, data quality should be tracked by comparing the production data distribution to the data used to train the model.

Responsible data science means thinking about the model beyond the code and performance, and it is hugely impacted by the data you’re working with and how trustworthy it is. Ultimately, mitigating bias is a delicate, but crucial process that helps ensure that models follow the right human processes.  This doesn’t mean you need to do anything new, but it’s important to rethink and reframe what we as data scientists already do to ensure it’s done in a responsible way.

Source: ibm.com

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Technology to support the journey to net zero

As companies around the word focus their attention on reducing greenhouse gas (GHG) emissions to deliver on their net zero commitments, the requirement for robust data and analytics to support this journey is intensifying. Technology vendors who service this market with carbon management software are the focus of a recent Verdantix Green Quadrant study.

We are delighted that IBM scored so highly in the 2022 edition of the Verdantix Green Quadrant: Enterprise Carbon Management Software study. The report is welcome validation of our leading position in the market and encourages us to continue on the path to expand key functionality and capabilities in this space. It also highlights the synergies we have made between Envizi, the IBM Environmental Intelligence Suite and other mission-critical software.

The Verdantix report focuses on key functionality required to deliver carbon management outcomes, including data quality control, renewable energy sourcing and the ability to measure and track physical climate risk. IBM achieved leading scores in all three of these criteria in the Verdantix Green Quadrant, achieving the highest cumulative score for enterprise carbon management. The value of this was reinforced by the report’s authors, who noted, “IBM offers customers a 360-degree view of GHG emissions across their operations alongside integrated tools for climate risk assessment.”

The report provides a detailed fact-based comparison of the 15 most prominent carbon management software vendors in the market. It includes a set of capability criteria that will drive value for any organization looking to assess different products in this sector, including:

◉ Data acquisition

◉ Data management

◉ Data modeling (scope 1, 2, and 3)

◉ Data quality control

◉ Carbon accounting methodologies

◉ Carbon emissions calculation engine

◉ Renewable energy sourcing and contracts

◉ Net zero strategy development and implementation

◉ Carbon disclosure management

◉ Physical climate risk

◉ Organizational data management

Data and AI are core to accelerating your sustainability journey

Once your sustainability strategy and goals are set, the next step is to establish a data and systems infrastructure to track ongoing performance and inform the operationalization stage, in which you embed sustainability decision making into daily business operations.

As the core data layer in your sustainability software stack, Envizi is designed to collect, manage and derive insights from sustainability data. Providing a comprehensive single system of record, it supports the integration technology required to send and receive data from data lakes or any other sources of data, such as metering systems, IoT platforms, utility providers, ERP systems and other third parties whose data is required to calculate a comprehensive GHG emissions footprint. This functionality is complemented by our Environmental Intelligence Suite, which incorporates weather, climate and environmental data to assess physical climate risk.

Envizi connects with a growing number of IBM operational performance improvement systems including TRIRIGA and Maximo. Further connectivity solutions are currently in development, with Turbonomic and the Supply Chain Intelligence Suite planned for delivery by the end of 2022.

Our practical approach aligns sustainability goals to business objectives. With open technology and consulting services, we work with companies to operationalize sustainability end-to-end by integrating and automating quality environmental, social and governance (ESG) data into daily workflows in a robust and auditable way. IBM’s breadth and depth of capabilities plus industry-leading research helps customers set, operationalize and achieve their ESG goals. IBM can help you accelerate your enterprise carbon management and sustainability journey.

Source: ibm.com

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To comply with GASB 87, government organizations need a better lease management solution

Over the past few years, the corporate world has worked through achieving compliance with lease accounting standards for real estate and assets. Adherence to ASC 842 and internationally, with IFRS 16, has been a challenging journey for many. Now it’s state and local governments’ turn to shine new light on their lease obligations.

Read More: C9560-680: IBM Control Desk V7.6 Fundamentals

Asset leasing is just as widespread across state and local governments as it is with private sector enterprises. While lease accounting requirements are usually applied to real estate and facilities assets, many other asset classes are commonly leased, such as IT equipment, warehouse space, warehouse equipment, water towers, vehicle fleets, cell towers and office equipment.

In the U.S., all leases associated with these asset classes must comply with regulations from the Governmental Accounting Standards Board (GASB). GASB 87, which took effect in 2021, is the new lease accounting standard issued by GASB to more accurately portray lease obligations and improve governmental financial statements.

Successfully navigating the complexities of GASB 87 compliance requires a cross-functional effort from stakeholders across the organization. Financial and accounting executives need to stay informed about the new accounting rules and the increased complexity associated with financial forecasting in the context of lease management and accounting. Real estate directors can increase efficiencies to reduce balance sheet impacts as they oversee facilities portfolios. And IT executives need to upgrade IT systems to manage finance, real estate and other core asset optimization functions.

What’s changed with the new GASB 87?

For all public sector entities, lease accounting and reporting are being wholly redefined. Under the old GASB 13 and GASB 62 standards, there was a determination about which leases had to appear on balance sheets. That’s no longer the case. Now, with minor exceptions, all leases are considered finance leases and are required to appear on balance sheets.

Further difficulty may arise when leases are scattered across departments throughout the organization. Facility leases may be part of core infrastructure groups, while transportation departments may hold vehicle or truck leases and IT may be leasing laptops, devices or datacenter equipment. There may be other unidentified specialty leases. Finally, there may be leases embedded in service agreements, which need to be identified and accounted for.

All of these leases must comply with GASB 87 standards.

Governments need a more sophisticated lease management solution

Government organizations ultimately obtain funding from legislatures and the implied consent of citizens. The ability to accurately project near-term needs and longer-term obligations through demonstrated financial control is imperative to establishing confidence to obtain future funding. GASB 87 provides an opportunity for improved financial transparency and a better understanding of asset portfolios.

Most government entities will have more than 200 leases, large ones will have far more. It won’t be possible to comply through manual efforts or spreadsheet tracking of leases. Accounting and finance leaders will have to invest in a lease management solution to help. It is not uncommon for an organization to keep documents in file cabinets, transactions in ERP systems, critical dates and options managed in spreadsheets, and workflow managed through e-mail.

Local and state government entities can focus first on integrating all leases and contracts into a digital repository to assess the portfolio. They must identify which contracts contain a lease and which are subject to GASB 87 compliance. There can also be embedded leases in some service agreements. The complexities of these contracts and leases can only be managed effectively with the right technology.

To manage real estate and asset lease contracts, organizations need a consolidated document repository and system of record. A 360-degree view containing lease history, metadata and documents with workflow is vital for contract management and regulatory compliance.

A good lease management solution does the following:

◉ Provides pre-built data structures and processes for all lease types

◉ Integrates with financial and other critical business systems

◉ Identifies underperforming and underused facilities

◉ Models alternative planning scenarios

◉ Compares financial and non-financial returns

◉ Alerts organizations to required lease accounting reviews

◉ Automates lease accounting controls

◉ Supports audits approvals and processes

Manage lease contracts with IBM TRIRIGA

IBM TRIRIGA® provides a comprehensive system of record repository to better manage real estate and asset lease contracts. It has long been among the leading integrated workplace management (IWM) solutions helping clients with real estate and lease management, space optimization, capital projects, sustainability and maintenance needs.

TRIRIGA has helped organizations manage changes in lease accounting standards and portfolio management for over 10 years, helping clients achieve ASC and IFRS compliance since those mandates went into effect.

Beyond delivering a complete solution for GASB 87 compliance, IBM TRIRIGA enables a greater sense of overall confidence in managing asset portfolios. Lease management executives can use TRIRIGA to create a repository and achieve compliance through every step of contract management, lease accounting, and lease administration.

Managing lease contracts is a complex process involving multi-step workflows that require an intelligent set of approvals and routings to provide the necessary controls over each action in the process. Once leases are executed, the monthly payment processing, OPEX management and payment adjustments can be administered. IBM TRIRIGA helps automate the entire process and reduce manual actions by enabling alerts on contracts that may trigger reviews or re-assessment. Users can replace spreadsheets with a digitized document repository and system functionality that helps to accurately determine the carrying value of lease liabilities and right-of-use assets.

TRIRIGA handles the full scope of accounting treatments to support GASB 87 compliance, from standard adoption and activation of leases to modification, reassessment and ultimately expiration or termination.

Once contracts and leases are in order, finance directors and real estate executives can also take a fresh look at portfolio planning and transaction management. TRIRIGA also supports improved sustainability and environmental management, facilities maintenance, capital projects and facilities optimization. Executives can transform the future course of their institutions with the confidence of their stakeholders.

IBM is a leader in the industry with years of experience helping worldwide clients with lease accounting compliance and portfolio management. We are now making this experience and expertise available to state and local government clients. It’s easy to grow with TRIRIGA, using our solution beyond GASB 87 compliance to create an ideal, comprehensive solution for you.

Source: ibm.com

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Press one for call center modernization

Like countless other industries and departments, customer support has undergone a massive transformation. Today I am excited to share more about how IBM is expanding its collaboration with NICE, launching a new integration that allows users to connect AI-powered virtual agents with their call center in a matter of minutes.

It sounds funny to talk about “the evolution of the contact center” when so few of us give customer support a second thought, until it’s a nightmare. Think back though, 10 or even as recent as 5 years ago, what was your expectation of support? More than likely it was a 1-800 number with a looping elevator-esque melody where, eventually you are met with an agent you hope can solve your issue. Fast forward to today, how do you interact with brands? Or more aptly, what is your expectation of support? If you’re like most people, the days of trying to outwit an IVR by pushing “0” over and over are a thing of the past and you expect brands to not only be available on the channel of your choice but also able to solve your problem in the best way given your specific situation.  At IBM we have seen our clients respond to these new expectations by shifting their strategy from one of “customer support” to one of “customer care”.

To keep pace with changing customer expectations and support these new “always-on”, personalized experiences, more and more companies are embracing cloud and AI to help modernize their call centers. As leaders in conversational AI platforms and contact center as a service, IBM and NICE are working together to make it even simpler to build, deploy and scale AI-powered virtual voice agents within your contact center  — without writing a line of code. Even small and medium firms can take advantage of this to build contact centers that offer amazing experiences to customers at lower costs.

Here’s how the combined solution works. When someone calls a company to get customer service, IBM Watson Assistant answers the call and asks how it can help. With market-leading AI it will listen to the caller and find the best answer or take care of a task. If the caller asks to speak a human agent, Watson Assistant will transfer the caller to agents on the CXone platform and pass along a transcript of the conversation so that the agent can pick up the conversation without making the caller repeat themselves. And, because this integration is out of the box, a non-technical contact center administrator could deploy a Watson Assistant-powered contact center in as little as a few minutes.

Powerful virtual voice agents, without coding 

Watson Assistant is a conversational AI platform designed specifically for contact center managers and administrators. It has a simple interface and offers an easy method to create human-like dialog to automate customer interactions and help callers solve their problems. Unlike other solutions in this space, it doesn’t require the expertise, or expense, of developers, AI engineers or data scientists. Customer care experts are able to build conversation flows, or actions, and Watson Assistant’s powerful natural language understanding and AI take care of the rest.

Watson Assistant can also easily connect to the company’s back-end systems —customer relationship management platforms, databases, and customer-support ticketing systems. This lets the administrators create virtual agents that can do more than just talk, handling complex use cases and tasks that require customer record retrievals, document searches, or creation of support tickets — right from the intuitive, low code experience.

What’s more, you can then connect Watson Assistant to CXone without the need for setting up telephony infrastructure or writing custom code. Set up and configuration takes 10 minutes even for novices.

Connect, anticipate and delight with CXone

Part of the IBM Ecosystem, NICE’s collaboration with IBM combines the companies’ strengths to help solve the most complex challenges in business and society for clients with hybrid cloud and AI. NICE CXone is a worldwide leader in AI-powered self-service and agent-assisted CX software for the contact center, and beyond. Imagine the possibilities when your customers are effortlessly guided to quickly resolve their needs directly on your digital properties or matched with a well-prepared agent. Plus, with predictive analytics and embedded artificial intelligence (AI), your team can resolve issues faster, personalize each experience, and forge deeper loyalty with each customer.

With its comprehensive platform combining digital entry points, journey orchestration, smart self-service, prepared agents, complete performance, artificial intelligence- all embedded with purpose-build CX AI and based on a native open cloud foundation, NICE CXone provides innovation that customers have come to rely on to enhance and transform their customer experiences every time, on every channel–in the contact center and beyond.

Source: ibm.com

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4 methods of successful cloud modernization leaders

Cloud computing is a major driver of business innovation. Cloud-based platforms in customer relationship management (CRM), e-commerce and analytics are rapidly improving business operations and driving business growth. Cloud modernization can deliver measurable value in weeks or even days. This is a huge shift from the traditional IT delivery model, which can take months or years to yield results.

These successes have created lofty expectations and placed increased pressure on CIOs to accelerate cloud modernization. Therein lies the curse of the cloud: CIOs are being crushed under the weight of expectations that cloud technology has inspired in the industry but are unable to get out from underneath these expectations due to challenges of resources, planning, and mindset.

The good news is that there are patterns and best practices that leading companies have used to successfully address modernization imperatives while delivering business results. Successful modernization programs share several common traits.

1. Instill joint business and technology ownership

Rather than treat cloud modernization as a technology project, leaders put the business vision at the center and develop a plan to unlock value from cloud. This is done by forming a cloud leadership team that is cross-disciplinary and brings together business and technology executives. Leaders in cloud deliberately diversify their teams to better understand and interact with different personalities, cultures, regions, business segments and challenges. This team is responsible for developing and executing the cloud strategy roadmap.

For example, one IBM client was struggling to prioritize their cloud modernization efforts and formed multiple “Cloud Pods” focused on specific functional and departmental business imperatives. These pods were made up of business and technology leaders and teams across the enterprise and tasked with identifying the most valuable use cases and opportunities for cloud. Rather than just addressing the technology transition of application workloads, they enabled cross-disciplinary focus to develop, prioritize and plan cloud initiatives that drove business value.

2. Build the backbone for cloud control

Successful leaders establish a cloud architecture foundation along with strong controls to manage and govern the way that cloud services are used. They also invest in training development and operations teams on cloud best practices and build the scaffolding or library of cloud-ready patterns and components that can be reused across the enterprise. For example, one client found that complexity quickly grew as they adopted cloud across the enterprise. We recommended and implemented a “Cloud API Academy” to train their development teams on best practices in API development. In addition, they formed a “Cloud Adoption Council” to govern major development efforts and ensure that they conform with standards and best practices.

3. Digitally transform IT

Leaders apply digital thinking and technology to their own operations. This includes leveraging data and AI to develop self-healing operations capabilities which automates end-to-end application delivery. Leaders also create a cloud operations center that is responsible for converting operational best practices, driving automation and self-service. One client began a digital transformation by exploring end-to-end automation opportunities across IT as they moved to cloud. This included an exhaustive inventory of key processes and tasks that were candidates for automation. The company then prioritized these areas for automation and applied consistent tooling and standards across these processes.

4. Establish FinOps

By putting an operating model in place, leaders gain transparency into the financials and operations of IT. They then systematically train IT leadership on the foundations of financial management in technology. This allows them to measure and evaluate cloud investments in the same way they measure other business investments. Reporting and chargeback (or “show back”) mechanisms give management insight into how cloud is being used as well as the associated costs. One client implemented FinOps capabilities which included setting up a cloud cost center, mapping cloud services to consumers and allocating costs to lines of business. All of this was integrated into their self-service cloud foundation so that reporting was automated from the point that cloud resources were provisioned. Across our clients where we implement FinOps, we routinely find savings of 10-30% in cloud service and technology costs.

Modernization is imperative for companies in nearly every industry. Cloud is a powerful enabler of modernization, but it is not a panacea. To avoid being crushed under the weight of increased expectations of cloud, CIOs must partner with business leaders to ensure joint accountability for success. In addition, they must build the capabilities to manage and monitor their cloud platform to address the added complexity that it brings to their landscape.

Source: ibm.com

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Commercial autonomous drone advancement

Since Amazon announced the testing of drone deliveries nearly a decade ago, commercial drones have continued their flight across a variety of industries. Organizations around the world deploy commercial drones for deliveries or to gather video or images with an onboard camera. Drone inspections are already popular in several industries, but commercial drone usage received a boost with the recent announcement that the UK is set to become the world’s largest automated drone superhighway in two years.

What are autonomous drones?

The European Union Aviation Safety Agency defines an autonomous drone as “able to conduct a safe flight without the intervention of a pilot […] with the help of artificial intelligence, enabling it to cope with all kinds of unforeseen and unpredictable emergency situations.”

Read More: C1000-059: IBM AI Enterprise Workflow V1 Data Science Specialist

If drones can use artificial intelligence (AI) to determine when to take off, which direction to fly, how to deal with external factors and how to return once the mission is over, there will be less need for pilots or drone operators.

Why are unmanned air system traffic management and “beyond visual line of sight” critical to the future of commercial drones?

Many countries are drawing up regulatory frameworks for low-altitude traffic management to accommodate the future of drones. This framework will cover roles, responsibilities and protocols to share data as part of drone operations. In the U.S., federal agencies are creating Unmanned Aircraft System Traffic Management (UTM). In the UK, the Civil Aviation Authority (CAA) is working toward something similar.

Just like road vehicles, identification of a drone is one part of the requirement for UTM, and the FAA in the US already requires all drones to be identified. There are also plans to include Remote ID for drones that will provide identification and location information that others can obtain.

In the U.S., a drone operator is legally required to have visual line of sight (LOS) to the drone. To enable large-scale commercial drone usage like the UK’s superhighway, the regulatory framework needs to allow for “beyond visual line of sight” (BVLOS) piloting.  Some countries with large amounts of remote locations, such as Iceland, Norway and Sweden, have already enabled BVLOS as means of supporting isolated communities.

How can drone-in-a-box help?

Frost & Sullivan defined drone-in-a-box in a 2018 report on drone delivery: “Sensor, communications, hardware and software technologies have advanced to the point that innovative companies can offer semi- or fully autonomous vehicles that can be automatically launched and recovered to base stations or enclosures. These solutions are often referred to as ‘drone-in-a-box’ because structures are required to recharge, protect, or recharge and protect drones between mission legs or between different missions.”

Today many companies invest in drone-in-a-box as a mainstream component of future industrial drone operations. At least two other features will become part of this component as 5G becomes the connectivity infrastructure:

1. Transfer of the drone imagery from the base station to the processing organization to enable near real-time decision making.

2. Support for edge computing, so the drone can be directed based on what it sees during an inspection.

What supports will maximize benefits from commercial drone inspections?

When adopting drone monitoring technology into an enterprise, it is vital to create a plan that maximizes efficiency. By combining drone-captured images and videos with these seven technologies, enterprises can automate workflows and improve the productivity of their business operations.

1. Storage for imagery

Drones capture high-resolution videos of the infrastructure or asset that needs to be monitored during a drone inspection. In general, a 4K-resolution video taken at 30 frames per second needs about 760 MB of storage for every minute recorded. Recording drone footage for even just a few days can add up to terabytes of storage. Therefore, enterprises have realized cloud storage is a cost-effective way to store and back up footage for later analysis.

2. Image stitching

Stitching videos or images together allows companies to see the full structure of an asset rather than spending time and money to monitor footage for changes. This is particularly useful in large structures like bridges or construction sites. This efficient tool helps managers identify issues and observe the pace and progress of solutions.

3. Other relevant datasets to support analytics

Initially, analytics may simply compare the change in the asset over time. When additional aspects of data are included, planners can achieve richer interpretation, analysis and pattern discovery. For instance, when data on the rate of rusting, types of rust, types of structural damage from rust and types of weather patterns are used with drone images of a bridge, it assists in predicting areas of potential structural damage versus superficial changes.

Urban planners can quickly complete planning activities when existing data of nearby topography, buildings, roads and infrastructure are used along with drone images of a particular area. Similarly, the use of weather data such as temperature, wind direction, potential for rain and past data of wildfires may help experts monitor and identify a change more quickly than with just the drone images.

4. Finding patterns with people

A bridge inspector who has spent 20 years on the job is able to look at a particular crack or concrete spalling and immediately tell if it is a cause of concern or not. The expert inspector considers depth, color, location and other factors to make this assessment. Human expertise and knowledge help identify patterns to create relevant datasets that can train computers.

5. Computer vision

Computer vision trains AI to identify the same patterns an expert inspector would see. For example, by training computers to identify imagery of a variety of concrete spalling, AI can automatically monitor images of the bridge to locate defects. This complementary drone solution eliminates the need for people to go through hundreds of hours of drone footage.

6. Rules and decision making

Once people identify a set of patterns in the imagery and teach AI to do the same, organizations can set up business rules. For example, if a particular type of structural defect is found on the roof of a house, run the drone inspection again in x months to see if there is a change. In a more critical scenario, such as if the construction blueprint and actuals are out of sync, a variety of departments will be prompted to act immediately.

7. Digital twins

Drone mapping of a building or set of structures, such as a telecommunication tower, can help create a digital twin. This digital twin can then help companies understand how the physical asset is functioning based on real data. For instance, with a digital twin, organizations can study and predict the ways a hurricane could impact the position of mobile antennas on a telecommunication tower. These predictions can inform engineers of individual towers that will need maintenance in advance of such an event. Moving from reactive to predictive maintenance can save organizations from downtime and support greater efficiencies of business operations.

Evaluating drone use in your organization

Automate inspections and reducing manual effort with drones will drive significant growth in the coming years. The global drone services market is projected to grow from USD 9.56 billion in 2021 to USD 134.89 billion by 2028 at a CAGR of 45.9% in forecast period 2021-2028. As this market grows, these industries will demand more specialized drone services.

Source: ibm.com

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