From principles to actions: building a holistic approach to AI governance

Today AI permeates every aspect of business function. Whether it be financial services, employee hiring, customer service management or healthcare administration, AI is increasingly powering critical workflows across all industries.

But with greater AI adoption comes greater challenges. In the marketplace we have seen numerous missteps involving inaccurate outcomes, unfair recommendations, and other unwanted consequences. This has created concerns among both private and public organizations as to whether AI is being used responsibly. Add navigating complex compliance regulations and standards to the mix, and the need for a solid and trustworthy AI strategy becomes clear.

To scale use of AI in a responsible manner requires AI governance, the process of defining policies and establishing accountability throughout the AI lifecycle. This in turn requires an AI ethics policy, as only by embedding ethical principles into AI applications and processes can we build systems based on trust.

IBM Research has been developing trustworthy AI tools since 2012. When IBM launched its AI Ethics Board in 2018, AI ethics was not a hot topic in the press, nor was it top-of-mind among business leaders. But as AI has become essential, touching on so many aspects of daily life, the interest in AI ethics has grown exponentially.

In a 2021 study by the IBM Institute of Business Value, nearly 75% of executives ranked AI ethics as important, a jump from less than 50% in 2018. What’s more, suggests the study, those organizations who implement a broad AI ethics strategy, interwoven throughout business units, may have a competitive advantage moving forward.

The principles of AI ethics

At IBM we believe building trustworthy AI requires a multidisciplinary, multidimensional approach based on the following three ethical principles:

1. The purpose of AI is to augment human intelligence, not replace it.

At IBM, we believe AI should be designed and built to enhance and extend human capability and potential.

2. Data and insights belong to their creator.

IBM clients’ data is their data, and their insights are their insights. We believe that data policies should be fair and equitable and prioritize openness.

3. Technology must be transparent and explainable.

Companies must be clear about who trains their AI systems, what data was used in training and, most importantly, what went into their algorithms’ recommendations.

When thinking about what it takes to really earn trust in decisions made by AI, leaders should ask themselves five human-centric questions: Is it easy to understand? Is it fair? Did anyone tamper with it? Is it accountable? Does it safeguard data? These questions translate into five fundamental principles for trustworthy AI: fairness, robustness, privacy, explainability and transparency.

AI governance: From principles to actions

When discussing AI governance, it’s important to be conscious of two distinct aspects coming together:

Organizational AI governance encompasses deciding and driving AI strategy for an organization. This includes establishing AI policies for the organization based on AI principles, regulations and laws.

AI model governance introduces technology to implement guardrails at each stage of the AI/ML lifecycle. This includes data collection, instrumenting processes and transparent reporting to make needed information available for all the stakeholders.

Often, organizations looking for trustworthy solutions in the form of AI governance require guidance on one or both of these fronts.

Scaling trustworthy AI

Recently an American multinational financial institution came to IBM with several challenges, including deploying machine learning models in the hundreds that were built using multiple data science stacks comprised of open source and third-party tools. The chief data officer saw that it was essential for the company to have a holistic framework, which would work with the models built across the company, using all these diverse tools.

In this case IBM Expert Labs collaborated with the financial institution to create a technology-led solution using IBM Cloud Pak for Data. The result was an AI governance hub built at enterprise scale, which allows the CDO to track and govern hundreds of AI models for compliance across the bank, irrespective of the machine learning tools used.

Sometimes an organization’s need is more tied to organizational AI governance. For instance, a multinational healthcare organization wanted to expand an AI model that was being used to infer technical skills to now infer soft/foundational skills. The company brought in members of IBM Consulting to train the organization’s team of data scientists on how to use frameworks for systemic empathy, well before code is written, to consider intent and safeguard rails for models.

After the success of this session, the client saw the need for broader AI governance. With help from IBM Consulting, the company established its first AI ethics board, a center of excellence and an AI literacy program.

In many instances, enterprise-level organizations need a hybrid approach to AI governance. Recently a French banking group was faced with new compliance measures. The company did not have enough organizational processes and automated AI model monitoring in place to address AI governance at scale. The team also wanted to establish a culture to responsibly curate AI. They needed both an organizational AI governance and AI model governance solution.

IBM Consulting worked with the client to establish a set of AI principles and an ethics board to address the many upcoming regulations. This effort ran together with IBM Expert Labs services that implemented the technical solution components, such as an enterprise AI workflow, monitors for bias, performance and drift, and generating fact sheets for the AI models to promote transparency across the broader organization.

Establishing both organizational and AI model governance to operationalize AI ethics requires a holistic approach. IBM offers unique, industry-leading capabilities for your AI governance journey:

◉ Expert Labs for a technology solution that provides guardrails across all stages of the AI lifecycle

◉ IBM Consulting for a holistic approach to socio-technological challenges

Source: ibm.com

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Is your conversational AI setting the right tone?

Conversational AI is too artificial

Nothing is more frustrating than calling a customer support line to be greeted by a monotone, robotic, automated voice. The voice on the other end of the phone is taking painfully long to read you the menu options. You’re two seconds away from either hanging up, screaming “representative” into the phone, or pounding on the zero button until you reach a human agent. That’s the problem with many IVR solutions today. Conversational AI is too artificial. Customers feel they’re not being heard or listened to, so they just want to speak with a human agent.

IBM Watson Expressive Voices 

Luckily, there is a way to fix that problem and make the customer experience more pleasant. With IBM Watson’s newest technology of expressive voices, you will no longer feel like you’re talking to a typical robot; you’ll feel like you’re talking to a live human agent without any of the wait time. These highly natural voices have conversational capabilities like expressive styles, emotions, word emphasis and interjections. Not only do these voices relieve the customer frustration of feeling like they’re talking to a bot, but they also contribute to the goal of call deflection from human agents. It’s a win-win for customers and businesses.

Read More: C1000-003: IBM Mobile Foundation v8.0 Application Development

Best suited for the customer care domain, the voices will have a conversational style enabled by default; however, the voices also support a neutral style which may be optimal for other use cases (newscasting, e-learning, audio books, etc.). Have a listen to the expressive voice samples below:

◉ Expressive Emma voice

◉ Expressive Emma voice with empathetic style

◉ Expressive Emma voice with uncertain style

◉ Expressive Emma voice with cheerful style

Emotions, Emphasis, Interjections

As humans, we convey emotion in the words we speak, whether we realize it or not. We tend to sound empathetic when apologizing to one another. We sound uncertain when we don’t know the answer to something, and perhaps cheerful when we finally discover the answer. The ability to convey emotion is what makes us human. IBM Watson’s expressive voices can express emotion in order to better convey the meaning behind the words, ultimately reducing customer frustration when dealing with today’s phone experiences. Your voice bot will sound empathetic when telling the customer their package is delayed or cheerful when they’ve successfully helped the customer book an airline ticket.

Emphasis is another important aspect of human speech. Did you say Austin or August? Did you say you lost the card ending in 4876? IBM expressive voices support word emphasis so that your bot can better convey the desired meaning of the text. Users can indicate the location of the stress with four levels – none, moderate, strong, and reduced.

Interjecting with words like hmm, um, oh, aha, or huh is another feature of human speech that IBM expressive voices now support to enable an interaction that feels more natural and human-like. The new expressive voices will automatically detect these interjections in text and treat them as such without any SSML (Speech Synthesis Markup Language) indication. There’s an also an option to disable the interjections when it’s not appropriate (e.g., ‘oh’ can be used to spell out the number 0 or as an interjection).

How to Get Started with Expressive Voices

Expressive voices and features will be available in US-English first in September 2022, followed by other languages in early 2023. The US-English expressive voices are Michael, Allison, Lisa, and Emma. For customers using the V3 version of Michael, Allison, or Lisa, switching to the expressive voices shouldn’t cause disruption as it will still sound like the same speaker, but with a more natural and conversational style. It’s easy to start using the new voices – simply indicate the voice name in the API reference, just like any other voice.

In summary, IBM’s new technology of expressive voices is the next level of conversational AI. It checks the box when it comes to an engaging and natural experience that mirrors that of a human agent. The new voices relieve the customer frustration of feeling unheard and drive call deflection from human agents.

Source: ibm.com

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A modern cloud data platform is the foundation of all intelligent supply chains

In a world where disruptions and complications are inevitable, strong supply chains are more essential than ever before. As highlighted in the new thought leadership paper “Building intelligent, resilient and sustainable supply chains,” the necessary transformation improvements are not just a question of manufacturing, logistics or transportation. They’re fundamentally a question of timely and accurate data, both from inside the enterprise and from the ecosystem of supply chain partners. For years, enterprise supply chains have rested on the shaky foundations of disconnected, unverifiable and untimely data. When things go wrong, enterprises turn to war rooms with often aged data and competing sources of truth. This approach results in too much executive energy seeking to understand where the business is, and not enough time spent on the forward-looking decisions essential to driving the business.

The transformative results of a digital twin+ unlock significant value, including a 5%–10% decrease in product wasted.

Even when supply chain transformation initiatives consider the implications of data, they often do it too late in the process, as a hygiene issue. This limits improvements to the realm of visibility, rather than surfacing actionable insights, making it harder to achieve operational success and realize value. Every high-performing supply chain is only as good as the data that fuels it. If you want to transform supply chains, you must internalize this truth before you start. Clean, connected data will be the foundation of next-generation supply chain operations. Additionally, if you want accurate and timely data, you need to collaborate across enterprise boundaries. With the right data foundation, you’ll be empowered to build better capabilities, such as capturing real time changes in demand signals, proactively identifying exceptions in orders and deliveries, and dynamically adjusting the business to avoid emergencies and escalations.

To put data at the heart of transformed supply chains, organizations need to take three key strategic actions:

1. Create a unified data fabric as the foundation to exchange supply chain data

In today’s data-rich world, data inherently lives in silos and is not harmonized to easily drive insights and actions. For example, much of the data that supply chain analysts use lives outside of ERP systems in quality systems, manufacturing execution systems (MES) and warehouse management systems (WMS). But you can enable easy data exchange by using the cloud to create a data fabric that instantiates a common data model across the enterprise. Cloud-based data fabrics enable the consumption and publishing of core data through services and APIs. In turn, these support downstream and collaboration tools to visualize data while applying intelligence to workflows and processing.

Beyond these performance improvements, the new data foundation means that supply chains can offer completely new capabilities that support better business models. For example, you can build insight-driven relationships with customers and deliver products “as a service.” IBM Systems does this by supporting long-term engagement with hardware customers. Based on usage data, support professionals can predict when new hardware might be needed and respond more quickly to service interruptions. Many capital-intensive products are good candidates to deliver “as a service,” but only if the provider has sufficient insight to support these products throughout their lifecycle and deliver the service seamlessly.

2. Use a digital twin+ to go beyond data visibility into process orchestration

Visibility solutions and data warehouses have incrementally improved the transparency of supply chain operations, but there is a limit to how much benefit they can provide. The solution is to pair the control tower with a digital twin into a so-called “digital twin+”. This model enables intra- and inter-enterprise data-driven processes, and delivers benefits such as improved accuracy in demand signals and early warning on supply chain disruptions or transportation delays. The result is a platform that thinks, listens, learns and acts, while establishing transparency and trust in the process. The transformative results of a digital twin+ unlock significant value, such as ~1%–3% of cost of goods sold (COGS), 5%–10% decrease in product wasted, and increased speed to market. (Representative results based on IBM Consulting supply chain engagements.)

A digital twin+ leverages the right technologies for each business driver, such as:

◉ Internet of things (IoT) for quality, geolocation and asset performance data

◉ Machine learning and artificial intelligence (ML/AI) for advanced forecasting, dispute resolution and disruption management

◉ API/service to stand up a flexible, componentized architecture

These technologies leverage the rich data from the entire ecosystem to drive insights and processes across the value chain.

3. Use a case-based approach to adopt specific components and score quick wins

Although it’s essential to have an overarching vision for your supply chain transformation and do the work of building a data foundation, don’t overlook the potential for that data to deliver quick ROI in well-defined areas. For example, you can deploy technology accelerators to focus on targeted outcomes:

◉ Leverage IoT and sensor data to improve asset utilization and minimize downtime

◉ Infuse AI/ML to increase the efficiency of operational processes such as purchase order creations, safety stock, and reorder points

◉ Identify and correct master data anomalies that create repetitive supply chain disruptions

Taking a pragmatic approach to solving supply chain disruptions and infusing innovation into the process can drive significant business outcomes. As an example of how these efforts can add up, consider how IBM Consulting recently helped IBM Systems transform the global supply chain that supported their USD 10 billion server business.

◉ Mitigating disruptions in days instead of hours

◉ Resolving persistent supply chain challenges 95% more efficiently

◉ Cutting supply chain structural costs by 10%

To see more about how clean, connected data is the foundation for transformative supply chains, read the new thought leadership paper “Building intelligent, resilient and sustainable supply chains” today.

Source: ibm.com

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Eli Manning and the power of AI in ESPN fantasy football

Eli Manning was the obvious choice. For the last six years, IBM has been working with ESPN to infuse AI-generated insights into their fantasy football platform. But we needed someone who could help us tell the story; someone who could grab the attention of fantasy football enthusiasts, introduce them to the artificial intelligence of Watson, and encourage them to embrace the era of data-driven decision making. (check out Eli’s visit to IBM Research here)

Why Eli? No, it’s not because I’ve been a New York Giants fan my whole life. And no, it’s not because the Giants and IBM are both nicknamed “Big Blue.” While neither of those things hurt, we ultimately chose Eli because he has so much in common with IBM.

Let me explain. Back in 2016, IBM formed a partnership with ESPN. In this relationship, we use IBM’s advanced analytics and AI capabilities to analyze the massive amount of data produced by fantasy football. We then serve up insights that help guide the roster decisions of ESPN’s fantasy football users. Today, those insights take the form of two features:

◉ Trade Analyzer with Watson, which uses AI to analyze player statistics and media commentary to help team managers understand the value of a potential trade.

◉ Player Insights with IBM Watson, which helps fantasy managers estimate the potential upside and downside of a matchup, analyze boom or bust chances, and assess injuries.

Why is IBM in the fantasy football business? Great question. Two reasons: First, we’re solving a very real business problem for a valued partner. ESPN’s Fantasy Football may look like fun and games, but it’s also serious business. More than 11 million people play on ESPN’s fantasy platform. And it’s a critical form of digital engagement for ESPN, one that also drives consumption of related football content, both digital and broadcast. Just like IBM’s other clients, ESPN is operating in a highly competitive market, and requires constant innovation to improve the customer experience. Using AI to produce insight at scale addresses a critical need for ESPN, just as it does for IBM clients in other industries.

The second reason is more self-serving. Simply put, ESPN Fantasy Football offers IBM a powerful platform to demonstrate our capabilities to millions of people. Both Trade Analyzer and Player Insights are produced by transforming vast quantities of data into insights that inform decision making. We’re analyzing the performance statistics of all 1,900 players in the league. But the numbers don’t always tell the whole story. So we’re also using the natural language processing capability of Watson Discovery to mine insights from millions of blogs, articles and podcasts produced by media experts (see here to learn more). Last year alone Watson served up more 34 billion AI-powered insights to ESPN fantasy players.

Which brings me back to Eli. When Eli Manning joined the New York Giants back in 2004 as the number one pick in the draft, many Giants fans thought he would be the second coming of Joe Namath: a big star in the big city. But Eli was more subtle than that, more Ordinary Joe than Broadway Joe. There were no flashy fur coats and movie star girlfriends. Just an understated, workman-like grit that resulted in two championships. An understated assassin who let his actions on the field speak do all the talking.

How is this similar to IBM? Well, it’s been 17 years since IBM sold its ThinkPad business to Lenovo. That was the last time our iconic “eight-bar” logo appeared on a consumer-facing device. But despite this lack of visibility, our work has never been more consequential than it is today. It’s not flashy, but our technology and expertise support the operation of the most mission-critical systems on the planet: electrical grids, airlines, telecommunications networks, banks, government services, and many others.

Technologies like hybrid cloud and AI are powerful, complex, and often difficult for people to comprehend. They operate behind the scenes, in data centers and back offices. But they are critically important to our clients. That’s why we showcase the work of IBM Consulting through partnerships like the Masters, the US Open, and ESPN’s Fantasy Football. And that is why Eli Manning is helping us tell our story.

Source: ibm.com

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Moving beyond spreadsheets with IBM Planning Analytics

My journey with IBM Planning Analytics started with an early morning phone call to tell me that a member of my team had died, suddenly and unexpectedly. Not only was his loss a personal tragedy, it was a tragedy for the whole organization. Our teams relied heavily on his decades of expertise to help us plan and forecast strategically for the future.

The company had been through tough times overall. An expensive enterprise resource planning (ERP) implementation meant there was no money left for other systems, and we’d been forced to run our budget process on a complicated network of 27 linked spreadsheets. Fred was the only one who knew how they worked and suddenly he wasn’t around.

If there was ever an example of key-person risk, this was it.

A world without spreadsheets 

We stumbled our way through the next budget process as best we could, until we came across IBM Planning Analytics with Watson. We could see, for the first time, a world that could exist without spreadsheets. We could see a world where people worked together on a common tool using a common approach to unite and agree on data-driven decisions for the good of the business. Better still, it was a world that didn’t rely on a single person.

But the story doesn’t end there.

Making sense of the data

Once we’d moved off the spreadsheets, we discovered the power that comes from managing data. We found countless problems with our master data, all of which had been masked through spreadsheet aggregation. We had been blissfully unaware of these challenges for years and now it was time to address them. By having full visibility of our data with IBM Planning Analytics, we could finally make sense of all our data together.

These problems were not trivial. In fact, we found examples where our product costs were materially misstated and discovered we’d been selling some products lower than what it cost to make. Through manual updates to spreadsheets, and working at a high level, errors – even seemingly blatant ones – were hiding in plain sight.

There’s little doubt in my mind that our investment in IBM Planning Analytics paid for itself several times over. Not only did we mitigate the key-person risk, which is honestly all we wanted to do, but we gained so much more. We made the organization value its data and want to put data to work for the good of the business.

Unlocking the value of data with the promise of AI

It’s often hard for leaders to see the value in analytical tools. Spreadsheets seem fine, but they’re not. They lull you into a false sense of security. Not only is the business logic linked with the spreadsheet owner, a risk on its own, but the sheer simplicity of the spreadsheet conceals the countless treasures within.

The promise of AI is tantalizing. It can provide insights that humans could never find. But realistically, can we ever hope to get there if the business still thinks in rows and columns? Our expert colleague’s untimely death was a tragedy, but we thank him every day for his legacy. I encourage business leaders that want to make a true impact on their bottom line to explore a continuous integrated planning solution like IBM Planning Analytics, which eliminates the manual work and helps to:

◉ Enable automated planning processes

◉ Encourage cross-functional collaboration

◉ Embed predictive AI capabilities for more accurate predictive forecasting

If you want to learn more, including how to create multidimensional plans, budgets and forecasts, explore interactive dashboards and reports, and discover pre-built solutions by industry or use case, you can get started today with a 30-day free trial or request a demo of IBM Planning Analytics with Watson.

I also encourage you to join the IBM Business Analytics live stream event on October 25, to hear more case studies on how others have used Planning Analytics to accelerate decision making.

Source: ibm.com

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How does addressing sustainability change how businesses understand value?

International businesses are under intense pressure and expectation to adjust to sustainable development. This requires them to make sweeping changes to their business models and value propositions. Most recently, the pandemic response has given businesses a window into their level of exposure to external challenges and how innovation and bold strategic change can lead to new markets, transformed ways of working and, importantly, open pathways into new areas of growth.

As we continue to advance our concept of sustainability, businesses must reconsider and broaden their definitions of value.

Changing concept of value

Businesses and organizations have to reconsider how they create and understand value. Traditionally, companies viewed themselves as having a secure position along a value chain, providing the inputs and outputs of raw materials to produce goods to realize their role in adding to the value chain. But modern organizations up and down the ecosystem are increasingly choosing to create new concepts of business value. These new options draw together ecosystem players’ respective knowledge, skills and capabilities across the customer and partnership networks to create new opportunities.

How does being sustainable create value?

There are compelling findings from across industries that sustainability can create a very positive business outlook, but it does require a new way of understanding how businesses can create value. Incorporating sustainability into the heart of operations can support the design and creation of new products and services, improve the brand and deepen customer loyalty. It can also reduce operating costs, increase the support from financial investors, and encourage company pride and commitment among employees.

Companies have started in earnest to create new value, using such strategies as new low-carbon products like steel and meat-free dishes and a transparent supply chain across retail and fast-moving consumer goods (FMCG). Furthermore, investments in green products support the sustainability mission and create whole new pools of value.

Why haven’t more businesses made a move to sustainability?

There are many reasons why businesses are not yet sustainable or whole-heartedly on the sustainability change journey.

Here-to-date success for international businesses has revolved almost exclusively around financial results: substantial revenue, the pursuit of profit, positive returns for the shareholders, and typically focusing on quarterly progress.

The pressure to perform for company shareholders has encouraged a belief that sustainability is a cost and should be managed with a dedicated but isolated corporate social responsibility (CSR) investment. This view has been reinforced by company sustainability reporting not being integral to business unit strategic decisions and P&L business unit operations.

In addition, many sectors with powerful vested interests have deliberately questioned sustainability and climate change research and action, delaying progress across their industry.

Despite decades of campaigns for sustainable development and climate change warnings, most individuals have not altered their behavior. The “intention-action” gap remains wide open; behavior change has been slight due to embedded habits, social norms, optimism bias, a preference for near-term loss avoidance instead of future gain, or a sense of futility. Whether as leaders, customers or employees, individuals have not led the charge for sustainability, so the “intention-action” gap remains.

Sustainability and new value creation

However, there is increasing momentum across global and local organizations towards a concerted business transformation with sustainability at heart. The first movers, be they in retail, FMCG or global consulting, are demonstrating that they can create a greater pool of value for themselves by investing in and fostering the creation of value within the communities in which they operate. Social innovation can help businesses differentiate and save on costs by creating new products, enhancing productivity across the value chain and improving the whole business environment for all customers and companies.

What does this look like in practice?

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

Centering sustainability at the heart of strategic decisions, operations and culture is not just a good thing to do; it is the most astute and economically wise thing to do. That is where future value is and will be found.

IBM is looking at long-term value creation and is committed to building sustainable societies by investing in cultivating the skills of local communities. Building a talent supply chain beyond our current employees makes organizations more sustainable. We believe skills-rich communities encourage fair societies. IBM is committed to being part of the global ecosystem of talent by building sustainable skills in local and global communities. IBM has committed to training 30 million people across the globe by 2030.

Businesses can be sustainable and profitable. The key is to reconsider the definition of value by thinking about creating shared value and seeing the bigger, longer-term richer picture rather than purely the numbers.

Source: ibm.com

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Extended Planning and Analysis (xP&A) in action

Extended Planning and Analysis (xP&A), is not a new concept for IBM clients who use IBM Planning Analytics with Watson, formerly known as Cognos TM1. For the past several years, clients have embraced the need to tie operational decisions to the financial impact from both planning and analysis perspectives. For instance, a Director of Operations may want to increase production for the upcoming selling season, but they must first understand the impact on the business overall.

Read More: C2010-555: IBM Maximo Asset Management v7.6 Functional Analyst

There are many operational considerations, from labor, staffing and production capacity — such as machinery and warehousing — to ensuring the business has the capital needed. All these factors need to be considered, and fortunately, IBM Planning Analytics with Watson has helped clients do this for years.

Financial and supply planning for a national blood service organization

A national blood service, and long-time Planning Analytics client, has started implementing a financial planning solution to better plan, forecast and analyze the cash flow needs and improve reporting to the leadership team and Board of Directors. Once the team fully understood the capabilities of Planning Analytics, they saw an opportunity to improve salary planning, a key part of the financial planning process. From that the HR team expanded the salary plan to include the components of staff planning including hiring and attrition.

Another way the team used Planning Analytics was to plan for the supplies needed for the collection of blood from donors. They created a planning application that schedules nurses and technicians who collect specimens and accounts for the supplies needed, from orange juice, bottled water, and cookies to medical supplies like tourniquets, blood bags, type testing kits and more.

As this company can attest, extending beyond the core finance function to plan for people, activities, and other areas has been part of Planning Analytics for years.

Financial and HR planning for a television production company

Another great example of Planning Analytics in action is with a television production company that, like many clients, was initially focused on financial planning. After the team had their financial planning and forecasting running well, they turned their focus on how to better-run their business. As a ‘job-shop’, where each TV program is a job, one area of focus was cost planning by job. The team created a job planning application, starting with staff planning as one of the largest cost components. Then they extended to include overhead and expense allocations, and eventually created a weekly Show Cost planning module to understand the contribution of each show to the overall production company’s results.

Supply chain planning for a global contract specialty manufacturer

A global contract specialty manufacturer, with deep expertise in manufacturing know-how, supply chain insights, and product design, uses Planning Analytics for nearly every ‘non supply chain’ use case in their organization. From financial analysis and reporting, forecasting, reserves reporting, aged accounts receivables, and treasury cash balance and forecasting to working capital, HQ allocations, local tax adjustments, and income tax in interim periods, all of these planning analytics solutions are integrated to ensure changes in one area, like cash forecasting, can be reflected in the overall working capital analysis.

No matter the industry, Planning Analytics is a continuous, integrated business planning solution that helps run some of the best companies in the world. Those who use IBM Planning Analytics with Watson understand the benefits of integrated planning that are not realized when doing ‘connected’ planning in spreadsheets or other traditional tools. If you want to learn how to create multidimensional plans, budgets and forecasts, explore interactive dashboards and reports, and discover prebuilt solutions by industry or use case, get started today with a 30-day free trial or request a demo of IBM Planning Analytics with Watson.

Source: ibm.com

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Mitigating demand volatility to improve forecasting: an intelligent workflow from IBM and SAP

Organizations are continuing to emerge from the lingering effects of the pandemic and ongoing supply chain disruptions. They are focused on reviving their strained supply chains and trying to understand their vulnerabilities and risk areas. What most are finding is that volatility remains in full force and continues to have detrimental impacts on planning and executing their supply chains.

One of the ways the SAP and IBM partnership are helping clients is through the joint creation of a new supply chain solution for demand planning. This unique fusion of SAP Integrated Business Planning (IBP) and IBM machine learning algorithms and proprietary data sets can help companies better manage volatility in the supply chain, even in the case of unforeseen disruptions like a pandemic.

The IBM Institute for Business Value’s Smarter Supply Chain Study details why this intelligent workflow solution is needed. When asked how challenging the pandemic had been for planning demand, 62.7% of supply chain executives answered, “Extremely Challenging.” And 64% of the same group said “Demand Volatility” was an extreme challenge to deal with in current conditions. The results from this study paint an all-too-familiar picture of how disruptive events in the supply chain lead to pronounced volatility, which has a bullwhip effect throughout the remaining areas of a supply chain. All this volatility comes at a steep price: for example, out-of-stock conditions alone amount to nearly USD 1 trillion in lost sales every year.

IBM Continuous Intelligent Planning with SAP IBP

This is why IBM and SAP teamed up to develop helpful intelligent workflows as part of our evolution partnership. The first workflow centers on this demand volatility challenge and provides key demand-sensing capabilities to help companies better forecast for short-term planning. Here’s how it works:

◉ SAP IBP generates a traditional, time-series-based forecast that accurately projects demand across the mid- to long-term planning horizons to help clients gain visibility into future needs.

◉ IBM’s Demand Sensing Intelligent Workflow focuses on the short-term horizon and “senses” recent, actual demand signals and their potential influence on the forecast to limit the chance of surprises. It analyzes both internal data sets (such as point-of-sale (PoS) and warehouse withdrawals) and external data (such as weather forecasts, IBM’s COVID-19 Risk Index and social sentiment) and draws correlations between these factors and the forecast.

◉ It then quantifies these correlations into short-term forecasts and passes them over to IBP through a custom integration layer. Our approach produces a comprehensive demand plan that is accurate across all planning horizons and helps to better account for volatility by incorporating a much larger collection of data sets than typically used.

By using this intelligent workflow, companies can expect benefits including:

◉ Improved forecast accuracy by 20–30%

◉ Reduction of inventory levels by 5–10%

◉ Detection of early signs of disruption and shifts, and a better understanding of their interdependencies and impacts

◉ Reduced stock-outs, leading to higher customer satisfaction

There are more supply chain disruptions on the way and the resulting volatility that accompanies them. Is your supply chain ready? Is your organization struggling with demand volatility and mitigating it in your demand plans? Is supply chain volatility impacting your ability to meet customer needs? Make your digital transformation a reality by bringing intelligence to your entire enterprise.

Source: ibm.com

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In times of stagflation, calculated growth is key to success

As inflation dominates public discussion, many senior executives are preparing for an economic downturn with margin compression rates that have not been seen in over 40 years. Barron’s reports a falling profit margin in industrials, with CAT expected to fall to 13.4% from 15.3% and Deere & Co to decline to 20.9% from 22.2%. Year-on-year inflation in the OECD climbed to 10.3% in June 2022, the sharpest price increase since August 1988.    

Source: OECD

Cost-cutting is not enough

Inflation supply chain disruptions, geopolitical uncertainty and unprecedented government spending all drive this projected downturn. Margin compressions will likely happen faster than any downturn in history as inflation merges with demand decline. Many executives are cutting costs to help preserve their margins in preparation for this scenario.

But in the 2008 recession, the most successful enterprises were those able to sustain (and even accelerate) growth during the recession. Businesses that emerged successfully from past recessions invested in growth and cost restructuring, while businesses that failed or merely survived lost sight of growth and only focused on cutting costs. When enterprises act intelligently and strategically, they can stimulate growth in tough economic times.

Technology accelerates growth under stagflation

In today’s digital environment, advanced technology, data and AI will play a much bigger role in driving growth. From 2008 to present, the share of ecommerce retail sales increased from 3% to 14% and the worldwide IT spend increased from $3.4T to $4.5T. Enterprises across all industries, including manufacturing, retail and financial services, increasingly identify more as tech companies than as companies in their own sector.

Even during a period of stagflation, enterprises can apply technology to drive rapid and sustainable business growth. This can be achieved by striking a careful balance between cost optimization and growth through methods like:

Pricing/bundling optimization:

By developing a more granular view of customer preferences, buying behaviors and segmentation, enterprises can tailor bundles and pricing to capture a greater share of wallet.

Cognitive care:

By leveraging AI and customer 360 analytics, enterprises can develop a real-time view of customer sentiment and adjust customer service to quickly meet the needs of the customer. This can lead to improved customer sentiment, lower churn rates and opportunities for upselling.

Digital channel expansion:

By expanding the channels where they sell their goods and services, enterprises can reach a broader audience. Enterprises should expand their social media presence and tailor it to specific population segments. They should also develop their presence in the growing metaverse.

Intelligent customer experience:

By leveraging technologies like IOT, sensors and real-time analytics, enterprises can optimize customer experiences and upsell services.

Ecosystem plays:

There is power in numbers. By developing or participating in an ecosystem of customer products and services (such as rewards programs), enterprises can expand their sources of revenue. This becomes particularly powerful when combined with other tech-enabled growth initiatives, such as intelligent customer experience.

Data monetization:

This largely untapped opportunity allows enterprises to package data and use it in ancillary services. This data may be gathered through the course of doing business, or it may be manufactured through the enterprise’s own products and services. For example, auto manufacturers can use telemetry data to create “lock-in” with intelligent fleet management solutions, services, diagnostics, prognostics and analytics, expanding the surface area of their sales force and driving greater market share.

IBM can guide your technological innovation

Executing these tech-enabled solutions can be challenging. It is often difficult for enterprises to understand where their opportunities lie, and technology implementation often requires time and a complex set of integrations to scale impact. Finding the talent needed to execute on these initiatives can be time-consuming, and legacy stakeholders often view these programs as temporary and don’t take ownership of initiatives. Additionally, the cost of technology programs can be high, particularly in the current environment with compressed margins.

IBM has technology and strategy expertise in 170+ countries, with successful implementation experience across many sectors. Our 12 research labs, 52 innovation centers and 57 studios worldwide, combined with our partnership with more than 100 ecosystem firms, provide the tools to help enterprises emerge stronger after a recession. Technology, data and analytics will enable sustainable growth in the coming years.

Source: ibm.com

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How to stay ahead of ever-evolving data privacy regulations

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

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

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

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

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

Adopting a privacy-centric approach built around a data fabric

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

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

Build a foundation using a common catalog and metadata

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

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

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

Operationalize data privacy through automation

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

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

Govern data and allow self-service consumption

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

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

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

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

Source: ibm.com

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How prioritizing sustainability affects and evolves business strategies and operating models

International businesses are operating in a world suffering a powerful sense of malaise with the status quo. Environmental and societal issues have shot to the top of national and international agendas, and the pressure for action is intense and pressing. Businesses must respond to change; they should anticipate high state intervention, explicit societal demands from citizens and higher levels of scrutiny from investors and regulators. The number of issues, institutions and interests pressing for change indicates a compelling need for international businesses to overhaul their core strategies.

Are international businesses open to sustainability?

First movers in the international business community, like Unilever, IKEA and Patagonia, have embedded sustainability throughout their business models. Others, like Ørsted, a Danish multinational power company, are rapidly reinventing their business model to provide renewable energy. To accomplish this, Orsted is swapping fossil fuels for offshore wind power. Unfortunately, most international businesses are considerably behind these global leaders, with many not accepting that averting climate change or global poverty is part of each business’s social contract.

How would prioritizing sustainability change business strategies and models?

Consumers who “reduce, reuse, recycle” will expect different experiences and services from the businesses and organizations they entrust with their money, data and wellbeing. Sustainable markets will be profitable, innovative, collaborative and open, and the design principles that reorient them to sustainability will direct them to long-term and inclusive growth.

New sustainability-driven innovations and capabilities create significant change for a business

Putting sustainability at the heart of your business strategy puts your business on a new path of innovation and expanded capabilities. Practical sustainability-related innovations depend on product, process and business model innovation. This includes innovative models like a move to renewables, a circular economy that doesn’t rely on the consumption of finite resources, as-a-service models, maximized product utilization to rethink the concept of waste, and product innovation.

What follows are some examples of operating model impacts of sustainability-driven innovations.

New technology innovation: impact of renewable innovation

Adopting renewable energy technologies will have diverse impacts on industries. Some industries, like technology consulting, can adjust to renewables relatively smoothly by switching energy providers and installing solar panels on offices and data centers. Other industries are more power-intensive.

Mining is an especially interesting use case for heavy industry’s move to renewable energy. The mining industry must make more significant changes to their operating models to incorporate renewable energy technologies, including overhauling remote fleets of mining and construction vehicles or investing in private power stations as well as new technologies capable of creating steel without burning fossil fuels.

New organization innovations: the impact of leveraging external ideas

The very activity of attempting to address sustainability can drive business model innovation. Tackling a wicked problem like the complexity of global sustainability requires ideas, a lot of them, ideally through open innovation. Open innovation requires a fundamental transformation in a business’s understanding of who can create value. This should no longer be limited to the R&D departments. Instead, organizations need to develop employee confidence and skill across all business units to envision new ways of working. This requires enterprise design thinking programs to build ideation skills, updated governance models to empower teams to change their ways of working, the recruitment of new talent, and corporate partnerships bringing diverse expertise to established teams.

New organization innovations: right to repair

The UK and EU are updating legislation to discourage “premature obsolescence,” alongside existing legislation to encourage recycling. Companies with business strategies that include planned obsolescence will have to rethink their ideas about value. If a product like a washing machine can no longer be designed to last just 5 years before replacement, then entire enterprise business processes must be reconsidered: design, manufacturing, pricing, sales and servicing, and arguably the company’s entire value set. Sustainable consumption will directly challenge the whole business model of low-cost operators and their extensive global supply chains.

New organization innovations: transparency in labor arbitrage

Pressure to reduce prices and increase agility and volume has led to extensive globalization of the supply chain for many international businesses. However, the employment practices in many developing countries and firms far down the value chain can be exploitative, allowing for lower wages, longer working hours and less unionized workforces. Therefore, sustainable retail practices must address workforce and supply chain strategies that knowingly or unknowingly source products made by forced labor or prison labor. Legislation such as the UK’s Modern Slavery Act and France’s Corporate Duty of Vigilance is pushing organizations to bring transparency to their sourcing strategies, making them accountable for employment along their whole supply chain. This step requires a rethink of the scope of “supplier relations” and a broadening of the skills and responsibilities of a company’s strategic buyers to develop long-term sustainable value.

New market innovations: Ecosystem collaboration

As the renewables market is still in formation, there are enormous opportunities for value pool creation, from first-mover innovation with new sustainable product designs and “complementary assets” needed to power the renewables market. For example, to support electric vehicles’ demands on the grid, three industry group leaders from energy, technology and automotive came together to create Equigy, a new electricity management and power-grid usage business. Equigy uses a blockchain-based platform to crowd-balance energy. It’s one example of a complementary asset for renewables addressing a new market need.

New market innovations: Circular economy

One powerful proposal fast gaining ground is the “circular economy,” which challenges the current “linear value chain” business model and addresses the waste and loss of potential value at the end of the value chain. The circular economy aims to replace the concept of waste by looping products back into its business, which means companies need new processes, technology and teams enabled to recycle, refurbish, harvest for spare parts or resell to new customers. This requires a significant change to an organization’s operating model; to service and repair products requires available parts (which aren’t welded together), service vans with the right size and geographical spread, and cross-skilled engineers.

New market innovations: product utilization creating new markets

When businesses move from a product to as-a-service, it can significantly change what is valued, foregrounding factors like durability of the product, ease of repair and ongoing customer service. A typical car is an unutilized asset, for many barely leave the driveway or go beyond picking the kids up from school. The latent value of the car is an example of how increasing a product’s utilization can create new markets and partnerships, requiring new teams, products, services and talent. As an example, we have seen an expansion of new ride-sharing businesses like Uber, Lyft and Didi and new business ventures for BMW and Daimler.

How does a business demonstrate the impact of its sustainability actions?

Being a sustainable organization requires concerted effort. There are evolving international frameworks, metrics and standards to help businesses and organizations guide, focus and report on their progress.

GHG: The GHG Protocol is a common standard framework that measures greenhouse gas emissions across the public and private sectors. This protocol recognizes all three scopes of an enterprise’s emissions: direct emissions from owned or controlled sources, emissions from the generation of purchased energy, and indirect emissions that occur upstream and downstream throughout the value chain.

ESG: Environment, social and governance reporting expands the definition of success to include more than financial considerations. Environmental factors include energy efficiency and reduced water pollution. Social factors include community development and diversity and inclusion. Health and safety and governance factors include corporate risk and integrity oversight or mechanisms of executive board governance.

Global investor and consumer pressure, along with pointed legislation, are succeeding in getting businesses to adopt ESG frameworks. ESG reporting is being reinforced by legislation, and the EU is leading the development of such legislation through the Climate Benchmarks Regulation (2020), Sustainable Finance Disclosure Regulation (SFDR) (2021), and EU EcoLabel (pending). However, adoption has been inconsistent, which often leads to claims of “greenwashing” by businesses the public perceives to be misrepresenting their sustainability progress.

While ESG frameworks serve to help businesses shape their sustainability decisions and transparently report progress against the different ESG criteria, they can also cause confusion. The different frameworks each bring their own “flavor” of ESG metrics and indicators, as they target the needs of different stakeholder groups: investors, governments or businesses.

The fear of greenwashing, and the doubt in the efficacy of much ESG reporting, is rooted in several thorny characteristics: ESGs are based on self-reporting, with few independently recognized reviewers or assessors. An absence of globally recognized metrics and benchmarks makes it hard to assess what good progress really looks like. Different companies, investors and geographical regions each must weigh the importance of the various environmental, social and governance metrics differently. Unless investors really understand a company’s business model and industry, it will remain difficult to fully scope out effective sustainability efforts.

Notwithstanding these setbacks, businesses need to embrace ESG and GHG reporting. They need to follow emerging best practices and be transparent with their data and metrics. Investors and the public expect companies to be organizationally agile as ESG reporting frameworks mature, and to take on more ambitious operating model changes to deliver improved ESG metrics. This degree of change and agility requires leaders to work across their ecosystem to drive industry-wide concrete change, and to share their data outside their business to forge a new, broader definition of success in their industry. ESG tracking and reporting should reflect a business’s long-term and evolving commitment to a successful sustainability journey.

Sustainability will bring significant change and opportunity to the company’s operating models

Sustainability is not integral to most industry and international business models, as seen by the decades-long failure of businesses to own the cost of environmental destruction and social exploitation that many have caused.

The challenge of designing, commercializing and adopting successful innovations will require significant investment and endurance from the business and ecosystem leadership.

Source: ibm.com

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Decades of empowering efficient data decisions

Data is everywhere. It grows exponentially year by year, and it is our duty to keep up with its overwhelming volume and complexity. The thing is, we’re so focused on conquering our data that we often forget this battle to understand it has been one we’ve been fighting since the beginning of time. However, we’ve always overcome this and been able to synthesize and communicate our data findings throughout the years.

Data complexity simplified by the digitization of data storage

One of the most prevalent times in data evolution was the Information Explosion of 1961, in which there were tremendous economic and technological innovations due to a rapid increase in the production rate of new information. This sudden overload of information was overwhelming to the masses, which resulted in numerous companies being unable to make clear and accurate decisions due to the newfound complexity and volume of their data. 

 IBM’s contributions to developing digital data storing technology set the precedent for the standardized method of data storage for years to come. This method was later justified in 1996 when digital data storage was proven to be more cost-effective than storing information on paper, as stated in the 2003 IBM Systems Journal paper, “The Evolution of Storage Systems.”  

How has IBM helped businesses organize, store, and leverage their data from the 1920s until today?  

 “We are a business with a single mission – to help our customers solve their particular problems through the applications of data processing and other information handling equipment.” – Thomas Watson Jr., Former Chairman and CEO of IBM, 1970 

 IBM has long been a data leader throughout history. As a company, we have been entrusted with organizing data on a national scale, made revolutionary progress in data storing technology and have exponentially advanced trustworthy AI using aggregated structured and unstructured data from both internal and external sources.  

 Here are some of the key moments in IBM’s data and AI journey that showcase the evolutions of organizing, storing and leveraging data. 

Did you know IBM helped with organizing data at a national scale? 

1928 Punch Card & U.S. Census:  

◉ IBM punch cards became the industry standard for the next 50 years, helped organize U.S. data at a national scale, and enabled other large-scale projects like the US Census. 

1936 Social Security made possible by IBM: 

◉ IBM worked with the U.S. government on the U.S. Social Security Act of 1935 and tabulated employment records for 26 million Americans. This was the largest accounting project of its time and really helped demonstrate IBM’s bandwidth for organizing data. 

What does the evolution of data storing technology look like at IBM? 

1964 System/360: 

◉ The System/360 ushered in an era of computer compatibility—for the first time, allowing machines across a product line to work with each other. 

1970 Relational database: 

◉ The relational database called for information stored in a computer to be arranged in easy-to-interpret tables which allowed non-technical users to manage and access large amounts of data. Most databases today still use this structure.  

1971 World’s first floppy disc: 

◉ Invented by IBM, this is one of the industry’s most influential products ever, making data storage more powerful, affordable, and portable. 

1981 The IBM 3380: 

◉ The IBM 3380 –a storage solution meant to be used alongside a computer– gave users the ability to store up to 2.52 billion characters of information, while new film head technology allowed data to be read and written at three million characters per second—two-and-a-half times the previous rate. 

2021 2-nanometer chip: 

◉ With 50 billion transistors on a fingernail-sized chip – the densest to date – this IBM innovation holds the potential for greener data centers & safer autonomous vehicles. 

How has IBM leveraged data for AI Advancement? 

1956 AI Before AI:  

◉ Arthur L. Samuel programed an IBM 704 – a large-scale computer designed for engineering and scientific calculations– to play checkers and learn from its experience. This is considered the first demonstration of artificial intelligence. 

1997 Defeating the reigning chess champ:  

◉ The IBM Deep Blue supercomputer defeated the best chess player in the world at the time. This led to thinking computers taking a giant leap forward towards the kind of AI that we know and use today, such as current image or speech recognition used on cellphones. 

2000 Deep Learning: 

◉ Deep learning attempts to mimic the human brain and helps with enabling systems in clustering data and making predictions with incredible accuracy. It has raised the bar for image recognition and even learning patterns for unstructured data. 

2022 The Mayflower Autonomous Ship Project:  

◉ With no human captain or onboard crew, the Mayflower Autonomous Ship (MAS) used AI and the energy from the sun to travel further into the ocean to uncover more unexplored parts of the sea. The ship has recently docked in Plymouth, Boston on June 30, 2022. 

IBM’s most recent moves in Data & AI 

The volume of data continues to grow exponentially, and organizations are faced with challenges due to managing the quality of their data, research states that,   

1. Bad data costs companies an average of $15 million. 

2. 73% of business executives are unhappy with their data quality. 

3. 61% of organizations are unable to harness data to create a sustained competitive advantage. 

Thus, why we have made efforts to help companies improve their business practices through data analysis. IBM’s data fabric approach prioritizes helping enterprises elevate the value of their data architecture, and through initiatives such as Customer 360 –which helps to reduce data quality issues in applications and optimizes business’ insights on customers.  

Most recently IBM has acquired Databand.ai, the leading provider of data observability software that helps organizations fix issues with their data before it impacts their bottom-line –including errors, pipeline failures and inadequate quality.  

This acquisition highlights our dedication to helping companies improve their businesses and highlights our continuous evolution of data and AI innovations. 

 Ultimately, as a data leader our goal is to help you organize, store and leverage data while deriving insights from complexity. Data is everywhere and will continue to grow exponentially, but the more quality data you have, the clearer you see. 

Source: ibm.com

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