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Bridge the data literacy skills gap with data storytelling

Being a data-driven organization goes well beyond building a modern data architecture. With vast amounts of data flowing through the enterprise, the challenge lies in making sense of all of that complex information so that everyone, not just the data scientists or machine learning engineers, can interpret it for better decision-making.

For CDOs and other leaders within different lines of business, this means fostering a culture that prioritizes data literacy: the ability to read, understand, create and communicate data. Too often, data is presented in mysterious figures that are difficult for key stakeholders to understand. In fact, poor data literacy is the second-biggest internal roadblock to the success of the CDO’s office, according to the 2021 Gartner Annual Chief Data Officer survey.

To get data literacy right, organizations have to make data more approachable for non-technical experts. This means moving away from confusing charts, dashboards, graphs and complicated visuals. Instead, organizations need to humanize data and AI by creating visually compelling stories that resonate with people and transform data into actionable knowledge that drives business results.

Bridging the data literacy gap with a culture of data storytelling

Enter data storytelling, the ability to convey data not just with numbers but with engaging narratives and visuals. Creating a narrative context is important, because it brings data to life and ensures that the message it’s delivering is meaningful and relevant. Adding data visualization elements enhances the story and makes large amounts of data more digestible. When done right, data storytelling can be a powerful tool to communicate and demystify the data science.

While organizations most often relay on a combination of UX/UI designers and BI specials, when non-technical business stakeholders start developing data storytelling skills it can spark a chain reaction across teams, LOBs and the organization as a whole. In that moment, one person with data storytelling skills will lead a group of individuals to make a better, data-driven decision, But those data savvy individuals also can impart their know-how to other coworkers and inspire their teammates to hone their data storytelling skills and shape their own daily workflows.

This helps to cultivate a collaborative data-driven culture from within that gives business teams access to the strengths and skills of everyone to solve problems better and innovate faster.

The power of established data-literacy initiatives and data storytelling programs

Too often, business stakeholders blindly follow data created by algorithms. To make sure this doesn’t happen, organizations need experts who can challenge those algorithms by asking critical questions of the data and interpreting it correctly. Understanding and telling stories with data is a pivotal part of ensuring employees are still critically thinking about data, questioning it and interpreting it correctly, because it includes more people in the conversation.

The resulting unified data-driven culture brings together data visualization specialists, data scientists and software developers, executive management and other stakeholders with the goal of everyone speaking a common language made of data.

To create this culture of data literacy, organizations can start by developing a business strategy at the executive stakeholder level. Once the business strategy is clear, data leaders like the CDO can craft a data strategy that helps achieve those business goals that includes data literacy initiatives to ensure adoption and success.

Launching data-literacy initiatives and data storytelling programs will help everyone, from the C-suite to all other key stakeholders, gain the skills needed to discover data insights, trends and patterns relevant to solving business problems. Training also empowers teams to use data as a competitive differentiator.

The post Bridge the data literacy skills gap with data storytelling appeared first on Journey to AI Blog.



Approaches to long-term planning with IBM Planning Analytics

In our collective rush to react to ever-changing marketplace dynamics and shifts in the economy, it’s easy to focus on short-term plans, to the neglect of long-term planning. Today’s leaders need to have several plans – short-term, medium-term, and long-term.

Different plans for different needs

How do these plans differ? A short-term plan is designed to show granular details for a limited time frame. This is often updated monthly, although we have some clients updating their plans on a weekly basis. One of our clients follows a process where local managers update their plans on Mondays and Tuesdays, have the regional managers review the data on Thursdays, and allow senior management to analyze and assess the data on Fridays. Each Monday they start the process over.

Most organizations utilize a medium-term plan that looks out anywhere from a few quarters to a full year. Most people will think of this as a standard monthly forecast with data at a bit more of a higher level, but still somewhat details.

A long-term plan often goes out multiple years. Many companies create a 5-year plan, although some industries such as entertainment and pharmaceutical often create 20-25 year plans. A long-term plan is a high-level view of the business. It’s not nearly as granular as short, or even medium-term plans. The plan does not get down to the level of looking at a GL account or a customer. It’s a measuring tool and a defined way of reviewing the progress of the company. In short, long-term planning helps to set the company’s direction.

The essentials of long-term planning

The long-term plan gives you guidance on how to answer several questions, including:

  • How can we expand the company?
  • How can we look into acquisitions?
  • What products, geographies, and verticals can we or should we add?
  • What products no longer make sense?
  • How do debt payments impact cash flow?
  • What type of labor, buildings, locations, and equipment do we need?

A long-term plan can be considered a proactive approach to risk mitigation, enabling companies to plan, think ahead, prepare for, and lessen the impact of potential negative effects. At Revelwood, we recommend two approaches to long-term planning: the growth percent approach and a driver-based approach.

We often see both of these methods used when performing long-term planning in IBM Planning Analytics with Watson:

Growth percent approach

The growth percent approach allows you to adjust groups of data (accounts, departments, etc.) by increasing or decreasing the values from the previous year. Some clients prefer to simply use a single percentage (example: reduce all expenses by 2% each year for the next five years) whereas some clients prefer to include more variation (example: reduce utilities expenses by 2% next year, by 3% the following year, and by 4% for the next three years). But no matter what level of detail is used, Planning Analytics’ powerful scripting tool will perform the entire long term plan in a matter of seconds.

Driver-based approach

A driver-based approach uses operational activity to calculate key variable revenues and expenses. This approach allows you to simplify the input by defining a set of drivers and creating calculations that use the drivers.  For example, a single driver of “units sold” can be used to immediately calculate revenue, COGS, and some of your variable expenses using the tool’s efficient calculation engine.

Mitigate risk with long-term planning

Long-term planning is your company’s assurance against planning to fail. There’s a reason why Franklin’s quote has lasted through the years. And it should be the motto of every planning team.

Learn more about long-term planning by watching our on-demand webinar – Long-Term Planning in IBM Planning Analytics.

If you’re ready to try IBM Planning Analytics, access the no cost trial here.

 

The post Approaches to long-term planning with IBM Planning Analytics appeared first on Journey to AI Blog.



IBM named a leader in the 2022 Gartner® Magic Quadrant™ for Data Quality Solutions

Data is the new oil and organizations of all stripes are tapping this resource to fuel growth. However, data quality and consistency are one of the top barriers faced by organizations in their quest to become more data-driven. So, it is imperative to have a clear data quality strategy that relies on proactive data quality management as data moves from producers to consumers.

Unlock quality data with IBM

We are excited to share that Gartner recently named IBM a Leader in the 2022 Gartner® Magic Quadrant™ for Data Quality Solutions.

Access the full report here.

Read more ›



Building a culture of data-driven decisions and insights with IBM Business Analytics

Organizations are managing and analyzing large datasets every day, but many still need the right tools to generate data-driven insights. Even more, organizations need the ability to bring data insights to the right users to make faster, more effective business decisions amid unpredictable market changes.

Meeting business goals with data insights

By building on our existing portfolio of business intelligence (BI) and planning analysis solutions, our clients are transcending manual and siloed analysis processes to optimize financial targets, sales goals, and operational capacity requirements. In doing so, they are putting their data to work to better meet their business objectives. Ultimately, every user, regardless of skill, can now feel empowered to make well-informed data-driven decisions.

Lessons from the IBM Data and AI Forum

Most recently we held an event at the IBM Data and AI Forum in Germany (available on demand here) where we shared the latest news in our business analytics portfolio. This included announcing the release of IBM Business Analytics Enterprise, which includes IBM Planning Analytics, IBM Cognos Analytics and the new IBM Analytics Content Hub.

During the event, we had the pleasure of hearing from many clients, including ALH Gruppe, a leading finance and insurance company in Germany who’s been using IBM Cognos Analytics with Watson for over a decade to support decision-making of all kinds, and recently tried our Business Analytics Enterprise solution. Mr. Oerthle, Head of Analytics Reporting & Infrastructure, ALH Gruppe shared, “With the new IBM Analytics Content Hub, we are able to connect internal stakeholders to multiple different BI solutions for easier, faster access to self-service data, enabling better outcomes for our end customers.”

If you missed the event and want to learn more about the new capabilities announced, here’s a quick recap of the exciting announcements:

IBM Business Analytics Enterprise

To unify the analytics experience, we’ve created a suite of our enterprise class business intelligence and planning analytics solutions, which includes the Analytics Content Hub. This suite of solutions helps transform the way clients can access, manage and consume business insights. Designed to allow users to view planning and analytics dashboards from multiple vendors, the IBM Analytics Content Hub brings all IBM and other common business intelligence tools into a single personalized view. With IBM Business Analytics Enterprise, users discover and access analytics and planning tools in a streamlined experience. As mentioned, this new pre-bundled enterprise analytics suite includes IBM Planning Analytics, IBM Cognos Analytics and the new IBM Analytics Content Hub.

IBM Planning Analytics as-a-Service on AWS

To help clients gain high availability and elastic scaling on demand, we’ve brought the power of IBM Planning Analytics as-a-Service on AWS. Clients can now request access to procure IBM Planning Analytics as-a-Service on AWS. This helps to boost forecast accuracy and consistency while driving faster time to insights. The full version will be available on AWS later this year.

IBM Planning Analytics Engine

The scaling capabilities of IBM Planning Analytics are unmatched, and we are continuing to build on this momentum. We are excited to announce IBM Planning Analytics Engine, a modernized distribution of TM1 for Kubernetes. Think – same TM1, different deployment architecture. Designed with resilience in mind, it’s available in IBM Planning Analytics 4.5.1, on-premises or other cloud providers.

With the amount of data and technology organizations have nowadays, it’s no longer possible to rely on simple spreadsheets to predict and plan for future business needs. Most organizations understand the power of analytics and business intelligence (BI) to plan, forecast and shape future business outcomes; however, for many, the analytics tooling and the insights they produce are still locked away in data silos. With the power of IBM’s business analytics solutions, organizations can gain access to real-time data, eliminating manual spreadsheets and organizational silos. They can meet their planning and forecasting needs, and ultimately transform their organization from what’s coming, to what’s next.

We are here to help! Explore our newly released eBook on the four steps to making better business decisions, or watch the on-demand sessions of the IBM Business Analytics launch event to hear more from our customers and partners on the power of using Business Analytics to make well-informed business decisions.

The post Building a culture of data-driven decisions and insights with IBM Business Analytics appeared first on Journey to AI Blog.



How Newcomp Analytics partners with IBM to advance clients’ supply chain insights

When Newcomp Analytics started working with chocolatier Lindt Canada more than 15 years ago to support their supply chain, Lindt had no full-time IT personnel for analytics. Lindt now has a team of 10, including a business intelligence (BI) manager and BI developer analysts. Yet Newcomp continues to be an essential and trusted partner, helping the company keep up with the high volume of analytics solutions it needs to address. “Newcomp has a track record of delivering with no surprises,” says John Walter, IT Director at Lindt Canada.

Helping clients close the business analytics skills gap

What makes Newcomp so invaluable to clients like Lindt? The company’s up-to-date expertise with IBM Cognos Analytics and their close relationship with IBM are key factors. Brian Simpson, VP, Analytics & Performance Management at Newcomp Analytics says “Newcomp has been a strong partner with IBM for many years, dating back to the early days of Cognos Analytics. IBM has the best channel ecosystem in the market today… it’s like a well-oiled machine. They are the standard to which we hold all other vendors.”

“There’s a lot of demand for analytic skills in general, a lot of demand for Cognos Analytics… and organizations are all fighting for a limited number of resources,” says Brian Simpson. “As an IBM business partner, we can bring those skills in a temporary capacity to the organization, help them with the heavy lifting, and get the project completed, so they don’t have to have a roadblock of needing to recruit and train analytics professionals—they can do the project while building those skills in-house.”

Lindt has used Cognos Analytics for more than 20 years as an analytics solution for its sales and marketing functions. The application provided these teams with valuable business intelligence and trend analyses across a wide variety of variables from single SKUs to product categories, from store-by-store sales to regional trends, and temporal factors such as seasonality. These insights supported the company’s double-digit growth in Canada during that time.

Extending business analytics to supply chain management

Though a supply chain management team doesn’t directly influence sales, cost-to-serve factors such as transportation and palletization can have a significant influence on profitability when delivering hundreds of millions of dollars’ worth of chocolate.

Unfortunately, Lindt’s supply chain management team had been under-serviced. Left to their own devices, they had resorted to using legacy reporting tools such as Excel that required manual gathering, slicing and dicing of data. Consequently, this data was siloed, unshareable, hard to use, lacked quality and governance controls, and could not be used in automated processes.

Newcomp drew on their technical ability and extensive industry experience with CPG metrics, collaborating with Lindt to understand their business challenges and where to optimize. Working with Lindt’s key stakeholders on the supply chain team, they identified key priorities for migrating the team from its legacy tools to Cognos Analytics’ modern data analytics toolset. Lindt’s satisfaction using the application in its sales and marketing capacity bolstered their decision to expand it to the supply chain management while also supporting other key components of their technology stack including Microsoft SQL Server and Microsoft Server SSIS.

By incorporating new data feeds from transportation providers and warehouses and aggregating these to the master dataset, Newcomp developed a cost-to-serve dashboard in Cognos Analytics. Now Lindt could ask new questions and draw new insights: Why do we spend more shipping to certain retailers? How can we drill into the data to identify underlying factors and get a better outcome?

Advancing clients’ strategic data analytics capabilities

The solution, according to Brian Simpson, was “A huge advance in Lindt Canada’s business intelligence capabilities.” It helped Lindt’s supply chain management team reduce their reliance on Excel, and reduced time and effort. The data ingestion process improved data quality and governance; automation also improved data quality by eliminating manual merge and preparation of calculations. A consolidated view of data is now available through the enterprise data warehouse and through Cognos Analytics. Overall, the solution has increased the speed-to-insight and ability of Lindt’s supply chain team to share and visualize high-level KPIs from their own dashboards and data sets. It has also freed up the executive team’s time to focus on more strategic activities.

Next steps for Newcomp and Lindt: building a dynamic cube in Cognos Analytics and exploring how the company can use IBM Planning Analytics to improve forecasting and data-driven decisions for competitive advantage.

Newcomp has a strong partnership with IBM, maintaining its certifications and expertise to stay at the forefront of business analytics solutions. This in turn makes Newcomp a trusted client partner for companies such as Lindt Canada, consistently delivering value across a growing range of business functions.

Read the detailed case study to learn more about the work Newcomp Analytics and Lindt Canada are doing. To learn more about IBM Business Analytics, watch the replay of the Business Analytics Launch Event, where you can hear more case studies on how others have used IBM Cognos Analytics and IBM Planning Analytics to accelerate decision making.

The post How Newcomp Analytics partners with IBM to advance clients’ supply chain insights appeared first on Journey to AI Blog.



Learning from CEOs: Collaboration and connectivity are keys to navigating sustainability

It’s the most frequently identified challenge CEOs expect to face over the next two to three years. It’s more vexing than regulation, cyber risk, and even supply chain disruptions. It’s sustainability, reveals IBM’s Institute for Business Value 2022 CEO Study “Own your impact: Practical pathways to transformational sustainability”.

As pressures from a broad set of stakeholders, most notably executive boards and investors, drive CEOs to define and pursue sustainability goals, CEOs are taking action. Nearly half of the 3,000 executive respondents name sustainability a top priority, an increase of 37% from 2021. Nearly all CEOs say they’re at least taking their first steps in piloting their sustainability strategy. More than one-third of the world’s largest companies have committed to net zero, and further efforts around biodiversity and water preservation are already on the horizon. The private sector’s commitment to climate action has never appeared to be so clear.

But many businesses have a long way to go. CEOs identify a range of challenges both within and beyond their organizations, and only a quarter report active implementation of their sustainability strategy across their entire enterprise. And as the business sector looks to deliver on its commitments, governments, public institutions, and non-government agencies can not only learn from and inform the actions of enterprise and industry leaders but must serve as essential partners in the journey.

 

Confidence at the helm, less so on charting the course

CEOs are bullish on sustainability. In the last five years, CEOs report their investments in sustainability have more than doubled as a percentage of revenue. Furthermore, the study found that over 80% of CEOs worldwide expect these investments to drive better business results in the next five years. Nearly half of CEOs think that sustainability will accelerate business growth.

But CEOs, particularly those most invested in sustainability, are also clear-eyed about the challenges. Most admit uncertainty around ROI and nearly half struggle with adequate insights from their data. Fewer than 1 in 4 agree that sustainability requires financial tradeoffs, but many acknowledge the need to redefine and re-calibrate how they measure performance. A common thread linking many of these challenges is the need to get more out of data, faster.

A group of CEOs in the study stood apart. These CEOs are taking personal responsibility, investing in a data foundation, embedding sustainability throughout their organization, and engaging ecosystem partners. Facing increasing regulatory scrutiny, demands for clear and consistent reporting, and a recognition that they do not have all the answers, these CEOs remain confident in the strategies they have defined. And their strategies extend beyond the boundaries of their companies.

 

A rising tide lifts all boats

No organization is an island. There’s an interconnected web of players across which sustainability goals must be defined and pursued, from partners and suppliers to the communities that organizations touch. Everyone has an essential part in turning sustainability goals into action, but leadership is required.

Forward-looking leaders are taking advantage of open innovation and establishing collective ways to mutually drive sustainability efforts to accelerate success. Aided by hybrid cloud technology, they can break down traditional silos and integrate their objectives with their ecosystem partners. Dreaming bigger and acting with confidence, CEOs are taking charge and building entirely new alliances to find more effective ways of accelerating sustainability together.

Leading CEOs are not pursuing sustainability objectives as an extension of their business but incorporating it into their core operations and culture. With data and AI playing a critical role, these CEOs are integrating sustainability into their digital transformation, and reshaping fundamental aspects of their organizations. Consistent with findings from IBM’s companion report, “Sustainability as a transformation catalyst,” organizations integrating sustainability and transformation strategies are outperforming their peers across multiple financial measures and appear poised to pull even further ahead. While the challenges are real, the business results and positive societal impacts from these leaders help chart a course for the future that is better for all of us.

 

Where the CEO leads, others can follow: Learn from the experts

Findings and lessons from these transformational leaders will be discussed in an upcoming AI for Good webinar at 10:00 AM ET on October 25, 2022. Sign up here for a moderated discussion and interactive session where Sheri Hinish, Global Services and Alliances Lead for IBM Consulting Sustainability Services, and I will be joined by other thought leaders in this compelling and critical space.

Sheri Hinish and Haynes Cooney

Learn how CEOs inspire new partnership models to turn sustainability goals into action. Executives Deirdre White, CEO, Pyxera Global, Sandra MacQuillan, Executive Vice President & Chief Supply Chain Officer, Mondelēz International, and AI thought leader Johan Steyn will discuss sustainability strategies that drive better business outcomes during the UN AI for Good webinar, Creating sustainable business growth with a smaller footprint.

Deirdre White, CEO, Pyxera Global, Sandra MacQuillan, Executive Vice President & Chief Supply Chain Officer, Mondelēz International, and AI thought leader Johan Steyn

IBM’s United Nations AI for Good sponsorship is a year-long conversation with individuals and organizations who are considering the entirety of AI in hopes that it can help us all better understand how it can foster the accomplishments of all the targets across the United Nations’Sustainable Development Goals. Sign up for the UN AI for Good newsletter to get updates and join us for all the programming here:https://aiforgood.itu.int/engage/.

The post Learning from CEOs: Collaboration and connectivity are keys to navigating sustainability appeared first on Journey to AI Blog.



Empower your organizations to make smart workforce decisions

Over the past few years, the workforce has evolved more than ever. According to the U.S. Bureau of Labor Statistics, 4.25 million people quit their jobs in Jan. 2022, up from 3.3 million in Jan. 2021. And a 2021 report by the Society for Human Resource Management shows more than 40% of U.S. workers are actively seeking a new job or plan to do so soon. To stay resilient in these changing times and be ready for the future, it’s critical that organizations reexamine their human resources (HR) planning tactics. More specifically, organizations must turn to a holistic planning solution that enables data-driven decisions to improve their workforce needs without losing individual focus.

Why HR Planning?

One of the most important parts of HR planning is enabling collaboration and transparency across all teams. Consider a mid-sized company and all the different line managers associated in making salary, incentive and promotion decisions. HR must consider the following:

Performance ratings

  • Who are the top performing employees?
  • How do we retain them?

Staff movement

  • Are there new hires?
  • Who is changing roles internally, and is there a promotion involved?

Salary bill

  • What is the spend for each team and across the organization?
  • What are the increases and bonuses?
  • What long-term incentives can we provide to ensure sufficient lock-in is in place?

Job market data

  • Are we providing equal pay for equal work across race, gender and disabilities?
  • How does our remuneration compare to that of our competitors/industry and the market in general?

Considering all of these factors above, an organization should invest in a holistic, extended  planning and analysis solution to bring together all the data impacting performance ratings, staff movement, planned salaries, job market data, and more in a single, consolidated view. This way, there’s no hidden data, but rather full transparency in the decisions being made – with the help of continuous, integrated planning at the heart of your business.

Governance and compliance in HR planning

Instead of focusing on manually pulling together disparate data in rows upon rows of Excel-based spreadsheets, which often leads to errors in a process that should be foolproof, your organization should look to solutions that can streamline your existing HR planning processes. Within IBM Planning Analytics, for example, you can build and maintain the right responsibility hierarchy and workflow that ensures the relevant managers are planning, reviewing and driving HR planning decisions to the next level.

If your planning processes are audited, having an extended planning and analysis (xP&A) solution like IBM Planning Analytics is a game-changer in tracing precisely who entered what data into which models used in the planning process.

HR planning for longevity and success

HR planning isn’t just about having the right employees; it’s also about recognizing and retaining your performant employees and encouraging workforce longevity and success. Aside from salary, stocks and monetary incentives, training and educational development are key parts of successful HR planning and staffing execution. Many organizations offer employee training programs, certifications, learning hours and more. But how are they keeping track of such metrics? What does continued success look like?

We’ve seen that IBM Planning Analytics can help HR teams better track and measure workforce success factors. In turn, this helps justify the investment in employee training programs and identify the ones best-suited to boost skills and performance. The result can be seen in improved bottom-line performance and ROI for a variety of organizations. With IBM Planning Analytics, you gain insights into performance metrics and employee morale, helping you prevent attrition and turnover challenges using trends that highlight risks much earlier.

Continuous workforce planning

Now more than ever, the steps organizations need to take to develop and enhance their existing HR planning processes is critical to support the entire workforce, which we know to have a significant impact on the success of the organization at large. Employees are the foundation of every business. Having a reliable, integrated planning and analysis tool is key – not only for your first-line managers to make better decisions, but for your entire organization to trust that fair and data-driven decisions are being made. We’ve seen solutions like IBM Planning Analytics help organizations to do just that.

 

To learn more about IBM Planning Analytics, you can get started today with a 30-day free trial.

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 IBM Planning Analytics to accelerate decision making.

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IBM Watson and ESPN use AI to transform fantasy football data into insight

If you play fantasy football, you are no stranger to the concept of data-driven decision making. Every week during football season, an estimated 60 million Americans pore over player statistics, point projections, and trade proposals, looking for that elusive insight that will guide their roster decisions and lead them to victory.

But numbers only tell half the story. And for the past six years, ESPN has worked closely with IBM to help tell the other half.

Every football season, millions of articles, blog posts, podcasts and videos are produced by the media, offering expert analysis on everything from player performance to injury reports. But for decades, this treasure trove of expertise went largely untapped by fantasy footballers, who could only consume a tiny fraction of this highly valuable content. Not anymore.

To identify and distill the insights locked inside this sea of “unstructured” data, ESPN collaborated with IBM to teach Watson the language of football. And today, using the natural language processing of Watson Discovery, ESPN serves up billions of AI-powered insights to the 11 million people who play fantasy football on the ESPN Fantasy app.

AI-powered insights that enhance the fantasy football experience

Fantasy sports is more than fun and games. It’s also a USD 8.8 billion industry. And for ESPN, fantasy football is a critical driver of digital engagement. To keep its experience fresh and competitive, ESPN needs to introduce new features and enhancements that drive customer satisfaction and new membership.

“We want ESPN to be the destination for all fans playing Fantasy Football, whether it’s their first time or they’ve been managing a league for 20 years,” says Chris Jason, Executive Director, Product Management at ESPN. “To meet that bar, we have to continuously improve the game and find ways to enhance the experience with new innovations.”

To help, ESPN partnered with IBM Consulting using the IBM Garage methodology to better understand the kinds of data-driven insights fantasy players want. Together, they created two unique features that are now integrated into the ESPN fantasy football app: Player Insights with Watson and Trade Analyzer with Watson.

Using deep neural networks and advanced natural language processing, Player Insights with Watson combines the analyses of both structured and unstructured data to help fantasy managers compare players, estimate the potential upside and downside of starting a particular player and even assess the impact of an injury. It lets a fantasy owner visualize the risk-and-reward scenarios, see trends over time and field a more competitive team.

“Because we’re incorporating insight from media experts, it presents a more comprehensive analysis of a player’s potential on any given week,” says Aaron Baughman, Distinguished Engineer, and Master Inventor with IBM Consulting.

Player Insights are built on containerized apps using Red Hat OpenShift running on the IBM Cloud. A machine learning engine pulls dozens of models from cloud object storage (running in Dallas and Washington, D.C., to ensure continuous availability).

Watson ingests and analyzes millions of news stories, opinion pieces by fantasy experts and reports on player injuries. The resulting insights are then correlated with traditional statistical data on more than 1,900 players across all 32 teams to help fantasy managers decide which players to start weekly.

Encouraging trades and transactions

Trade Analyzer with Watson uses those same Player Insights to evaluate potential trades. A visual UI shows a fantasy manager which positions they need to fill to improve their roster. These team needs are prominently displayed at the top to guide the manager’s trade journey. When one manager proposes a transaction, Trade Analyzer automatically assesses the strengths and weaknesses of both rosters.

When managers initiate transactions with each other, Trade Analyzer with Watson delivers trade insights, which feature a grade for each athlete involved in the trade and a grade for the overall value of the trade. As a result, with one look, managers can tell if their trade is a good deal. Once the managers have these insights, they can move ahead with the trade, cancel it or edit the trade package.

“An active league is a fun league,” says Jason. “So we want to encourage roster moves and trading between teams. These features help us do just that.”

Throughout this six-year partnership, Watson has produced hundreds of billions of AI-powered insights for ESPN and its viewers. In just the first week of the 2022 season, users proposed more than 6 million trades via ESPN’s platform. And last year alone, IBM served more than 34 billion AI-powered insights through the ESPN fantasy app.

Trade Analyzer and Player Insights with Watson use AI to foster better decisions by fantasy managers. But they also make ESPN Fantasy Football more fun and engaging. And the partnership with ESPN allows IBM to demonstrate AI’s ability to transform massive quantities of data into meaningful insight, a capability business leaders are looking for in every industry.

The post IBM Watson and ESPN use AI to transform fantasy football data into insight appeared first on Journey to AI Blog.



How IBM Planning Analytics can help fix your supply chain

IBM Planning Analytics, or TM1 as it used to be known, has always been a powerful upgrade from spreadsheets for all kinds of planning and reporting use cases, including financial planning and analysis (FP&A), sales & operations planning (S&OP), and many aspects of supply chain planning (SCP). As far back as the 1990s and early 2000s there were companies, like the one discussed in this podcast episode, that took advantage of TM1’s power to support full integration of their financial and supply chain planning processes.

Build planning models to improve supply chain management

The challenge faced by every company is matching supply with demand. In a perfect world you would know precisely how much of your product the market desires, and you would be able to produce and ship exactly that amount to every location where your customers would be waiting, ready to buy.

In lieu of a perfect world, what do you do? You plan. Plans help you explore the consequences of your decisions in advance so you can understand your hedging options: Do I build up inventory here? Do I need to find new suppliers there? Do I have enough cash to fund these investments while also covering day-to-day operations?

You also build planning models to capture relationships and constraints so that you can change your driver assumptions and immediately see the impact on resources and capacity over time. Having the ability to build and use models in this way is fundamental to managing supply chain and financial risk through activities like “what-if scenario planning”, as explained in this blog post. Time matters too: your models must be quick to run, so analysis can be done before the assumptions are out-of-date. As such, planning becomes a continuous rolling activity as the lines between “plan”, “budget” and “forecast” are blurred.

Since there are clear cross-functional business correlations between demand and sales, supply costs and Cost of Goods Sold, it’s not hard to argue for supply chain and financial planning models to be integrated across the Extended Planning and Analysis (xP&A) cycle. However, the reality of this is complicated by several factors including:

  • Differing time horizons and cadences: Days/Weeks vs Months/Quarters
  • Differing levels of detail: SKUs/ Products vs Product Groups/ Lines of Business
  • The need to collaborate, share data and agree on definitions across organizational boundaries and systems

Choosing the right technology to support xP&A for your strategic goals

A growing number of forward-looking companies are successfully navigating these complexities using IBM Planning Analytics, a technology capable of supporting secure collaboration, fast automated data acquisition, driver-based and AI-powered predictive modeling, and, unique in the market, the handling of large amounts of detail at scale without sacrificing performance.

With the right technology-foundation in place, it becomes easier to tackle the business alignment questions, starting with designing an end-to-end integrated business planning process that will lead efficiently to a consensus forecast (or plan).

The first step is always the unconstrained demand plan.

Even when supply constraints seem overwhelming, it’s still important to have this view, so you can take action to overcome the constraints in the future. Depending on the patterns of your business, predictive models can play a significant role in improving the accuracy of your demand plan, while also saving time through automation, as experienced by Arthrex, a global medical device company.

The next step is to start layering on constraints.

In a manufacturing, distribution or retail context, this is the supply plan. The supply plan is typically anchored in capacity and can combine manufacturing capacity, supply capacity and labor capacity.

Then, everything comes together.

With everything in the IBM Planning Analytics dashboard, it’s now possible to see where and when capacity shortfalls (or excesses) are imminent and explore options for mitigating situations in accordance with strategic goals.

IBM Planning Analytics can help your teams modify assumptions such as production capacity and labor allocation across a variety of scenarios in real-time, and immediately see the impact on all related metrics including constrained demand, inventory, sales, costs, and cash. QueBIT’s webinar includes a demonstration with IBM Planning Analytics of the interplay between all these components, beginning with the demand plan and ending with the impact on financial statements. You can also find a more nuanced explanation of the relationship between supply chain decisions and financial KPIs here.

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

The post How IBM Planning Analytics can help fix your supply chain appeared first on Journey to AI Blog.



AI Governance: Break open the black box

It is well known that Artificial Intelligence (AI) has progressed, moving past the era of experimentation. Today, AI presents an enormous opportunity to turn data into insights and actions, to amplify human capabilities, decrease risk and increase ROI by achieving break through innovations.

While the promise of AI isn’t guaranteed and doesn’t always come easy, adoption is no longer a choice. It is an imperative.

Those businesses that decide to adopt AI technology will have an immense advantage, according to 72% of decision-makers. Furthermore, 59% of executives claim AI can improve the use of big data in their organizations, facts about artificial intelligence show. (IBM Global AI Adoption Index 2022.)

What is stopping AI adoption today?

 

3 main reasons why organizations struggle with adopting AI

1. Lack of confidence to operationalize AI

Many organizations struggle when adopting AI. This is due to:

  • An inability to access the right data
  • Manual processes that introduce risk and make it hard to scale
  • Multiple unsupported tools for building and deploying models
  • Platforms and practices not optimized for AI

“According to Gartner 54% of models are stuck in pre-production because there is not an automated process to manage these pipelines and there is a need to ensure the AI models can be trusted.” (Gartner AI in organizations survey.)

Well-planned and executed AI requires reliable data backed by transparent, automated tools and explainable processes. Success in delivering scalable enterprise AI necessitates the use of AI tools and processes that are specifically made for building, deploying, monitoring and retraining models.

2. Challenges around managing risk

Customers, employees and shareholders expect organizations to use AI responsibly, and government entities are demanding it. This is critical now, as more and more share concerns about brand reputation with their use of AI. No one wants to be in the news for the wrong reasons. Increasingly we are also seeing companies making social and ethical responsibility a key strategic imperative.

3. Scaling with growing AI regulations

With the growing number of AI regulations, responsibly implementing and scaling AI is a growing challenge, especially for global entities governed by diverse requirements and highly regulated industries such as financial services, healthcare and telecom. Failure to meet regulations can lead to government intervention in the form of regulatory audits or fines, damage to the organization’s reputation with shareholders and customers, and revenue loss.

 

The solution: AI Governance

AI governance is an overarching framework that uses a set of automated processes, methodologies and tools to manage an organization’s use of AI. Consistent principles guiding the design, development, deployment and monitoring of models are critical in driving responsible, trustworthy AI. These principles include:

  • Know your model: Model transparency starts with the automatic capture of information on how the model was developed and deployed. This includes capturing of the metadata, tracking provenance and documenting the model lifecycle. Model transparency promotes explainable AI driving trusted results that build public confidence, promote safer practices and facilitate further AI adoption.
  • Trust your model: Complying with rules, regulations and driving AI that minimizes bias requires well defined and automatically enforced enterprise policies, standards and roles. Manual manipulation of data and models can introduce costly errors with far-reaching consequences. In addition, the automation of enforcement rules for validation drives model retraining and reliability to address drift now and over time.
  • Use your model: Transparent and explainable AI requires the automation of the analysis of model performance against KPIs while continuously monitoring in real-time for bias, fairness and accuracy. The ability to track and share model facts and documentation across the organization provides backup for analytic decisions. Having this backup is crucial when addressing stakeholder, customers and concerns from regulators.

At IBM we believe AI governance is the responsibility of every organization to adhere to ethical, explainable AI, respecting individual rights, privacy and non-discriminatory practices. Responsible AI requires upfront planning, automated systems and the governance necessary to drive fair, accurate, transparent and explainable results.

 

The three foundational capabilities of the IBM AI Governance solution

IBM AI Governance is a new one-stop solution built on IBM Cloud Pak for Data. It is designed to help businesses meet their regulatory requirements and address ethical concerns through software automation. It drives a complete governance solution without the excessive costs of switching from your current data science platform.

Everything needed to develop a consistent transparent model management process is included in IBM AI Governance. This includes repeatability and the ability to capture of model development time, metadata, post-deployment model monitoring, and to customize workflows. IBM AI Governance is built on three critical principles, meeting the needs of your organization at any step in their AI journey:

1. Lifecycle Governance: Monitor, catalog and govern AI models from anywhere and throughout the AI lifecycle

  • Automates the capture of model metadata across the AI/ML lifecycle to enable data science leaders and model validators to always have an accurate, up-to-date view of their models. Lifecycle governance enables the business to operate and automate AI at scale to ensure that the outcomes are transparent, explainable and devoid of harmful bias and drift. This increases the accuracy of predictions by identifying how AI is used and where corrective action is indicated.

2. Risk Management: Manage risk and compliance to business standards, through automated facts and workflow management

  • Model risk management is used to identify, manage, monitor and report on risk and compliance initiatives at scale. Dynamic dashboards provide clear, concise customizable results that enable a robust set of workflows, enhanced collaboration and helps to drive business compliance across multiple regions and geographies.

3. Regulatory Compliance: Help to proactively ensure compliance with current and future regulations

  • Translate external AI regulations into a set of policies for various stakeholders that can be automatically enforced to ensure compliance. Users can manage models through a dynamic dashboard and that tracks compliance status across all policies and regulations.

Where do you go from here?

Register for AI Governance webinar

Learn more about how IBM is driving Trustworthy AI

IBM Expert Labs team can work with you across all stages of the AI lifecycle to help deliver trustworthy AI solutions at scale and speed.

The post AI Governance: Break open the black box appeared first on Journey to AI Blog.



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