Master the art of Data Science

Editor's Notes

• #3: We all know data is the foundation for businesses to drive smarter decisions. Data is what fuels digital transformation. But, it is Artificial intelligence (AI) that unlocks the value of that data, which is why AI is poised to transform businesses with the potential to add almost 16 trillion dollars to the global economy by 2030. However, adoption has been slower than anticipated. Business leaders not only need to understand the power of AI, but how they can fully unleash its potential and operate in a hybrid, multicloud world. This presentation aims to demystify AI, present common AI challenges and failures, and finally, provide a unified, prescriptive approach (which we call “the AI Ladder”) to help organizations unlock the value of their data and accelerate their journey to AI.
• #4: Across an array of use cases, AI pioneers are employing a core set of new AI capabilities and shaping the future of work. They’re leveraging AI to: Predict and shape future outcomes Empower people to do higher value work Automate decisions, processes, and experiences Reimagine new business models that are trusted, transparent, and deployable anywhere Some of our clients paving the way are: Geisinger (Predict and shape future outcomes) – One sepsis patient dies every two minutes in the US, but over 80 percent of deaths are preventable with prompt diagnosis and care. Geisinger partnered with IBM and used the data science tools available in IBM Watson Studio to develop machine learning models capable of analyzing thousands of patient records and medical journals. Working with IBM, Geisinger has successfully built a predictive model for sepsis mortality based on real-life EHR data, that has helped researchers identify clinical biomarkers that are associated with the higher rates of mortality from sepsis.  Woodside Energy (Empower people to do higher value work) - Woodside, Australia’s largest independent energy company has been a global leader in oil and gas for over half a century. Their secret? Hire and develop heroes. This formula has helped Woodside build some of the largest structures on the planet, in some of the most remote parts of the ocean, and safely transport the energy they produce to people around the globe. To ensure the next generation could successfully carry the torch, Woodside knew they had to harness the instinctual know-how of their best employees. This goal — to create a cognitive business to augment and share their tribal knowledge — is what led Woodside into an industry-first partnership with IBM and Watson. Experian (Automate decisions, processes, and experiences) -- Experian collects and aggregates information on over one billion people and businesses including 235 million individual US consumers and more than 25 million US businesses. Currently they use a rules-based system to determine if a file should be automatically loaded or checked. However, this system misses may correct files, resulting in additional human labor to check files and load them manually. Experian worked with IBM Watson Studio to reduce the number of files sent to be checked by approving them directly in the load process using machine learning techniques to classify these documents and descriptive analytics to get a better understanding of how the current rules are working, and successfully reduced 96% of false positives and 95% of false negatives. Legalmation (Reimagine new business models) -- With intuitive IBM® Watson® offerings, LegalMation developed a first-of-its-kind AI platform to automate routine litigation tasks. Supported by the IBM Watson ecosystem, the company quickly launched its solution for drafting early phase response documents, helping legal teams save time, drive down costs and shift strategic focus.
• #5: But those AI pioneers are just some of the few. There are thousands of clients IBM is helping put AI to work, and while these clients are on various stages in their own journey, they all have one thing in common -- they are transforming their business through the use of AI.
• #6: However, to ensure a successful AI strategy, organizations need to understand how to adopt and implement the technology and realize there will be failures along the way. In order to turn AI aspirations into outcomes, organizations need to overcome three major AI challenges: data complexity, skills, and trust.   AI Challenge: Data Complexity Data is the foundation and fuel for businesses to drive smarter decisions particularly as they embark upon digital transformations. The problem is that while 90 percent of business leaders list improving the use of data as a top priority, only 15 percent of them are actually getting what they need from their data. As a result, the majority of businesses have a plan to build a system of insights to become data-driven and have declared the journey to AI as a strategic priority. Data is the lifeblood of AI, and if organizations don’t solve for its complexities, their progress can be slowed by data siloes, incomplete data, and the appropriate approach to governance.   AI Challenge: Skills Data is the lifeblood of AI, but you also need skills , such as knowing how to code, understanding and building deep learning and machine learning models, to bring AI to fruition. The challenge is that AI skills are rare, and therefore in high demand, so there’s a shortage of skilled workers available to hire. This makes it even more important that the technology being built and used is more easily accessible to everyone within the business, regardless of skill level.   AI Challenge: Trust For organizations to truly embrace and scale AI across the entire businesses, they need to break open the ‘black box’ of AI and trust the systems. It is critical to ensure AI recommendations or decisions are fully traceable – enabling enterprises to audit the lineage of the models and the associated training data, along with the inputs and outputs for each AI recommendation. As more applications make use of AI, businesses need visibility into the recommendations made by their AI applications. In the case of certain industries like finance and healthcare, in which adherence to GDPR and other comprehensive regulations present significant barriers to widespread AI adoption, applications must explain their outcomes in order to be used in production situations.   For AI to thrive, and for businesses to reap its benefits, it is imperative that organizations are able to address these three challenges to ensure they have trust their AI systems, have the right skills across their organization, and access to their data, no matter where it resides.
• #7: As companies look to harness the potential of AI, they need to use data from diverse sources, support best-in-class tools and frameworks, and run models across a variety of environments. However, 81 percent of business leaders  do not understand the data and infrastructure required for AI. ​ According to MIT Sloan, “No amount of AI algorithmic sophistication will overcome a lack of data [architecture]…bad data is simply paralyzing.”​ ​ Put simply: There is no AI without IA (information architecture).
• #8: IBM recognizes this challenge our clients are facing. As a result, we’ve built a prescriptive approach (known as the the AI ladder), to help clients overcome these challenges and accelerate their journey to AI, no matter where they are on their journey. It allows them to simplify and automate how organizations turn data into insights by unifying the collection, organization and analysis of data, regardless of where it lives. By climbing the ladder to AI, enterprises can build a governed, efficient, agile, and future-proof approach to AI. Furthermore, it is an organizing construct to the Data and AI products and services offered by IBM and our business partners, and it is the technology foundation to unify how those products and services work together.    What we have learned from AI pioneers is that every step of the ladder is critical. AI is not magic and requires a thoughtful and well-architected approach. For example, the vast majority of AI failures are due to data preparation and organization, not the AI models themselves. Success with AI models is dependent on achieving success first with how you collect and organize data. The AI ladder has four steps (often referred to as “rungs”):   Collect: Make data simple and accessible.Collect data of every type regardless of where it lives, enabling flexibility in the face of ever-changing data sources. Organize: Create a business-ready analytics foundation.Organize all the client's data into a trusted, business-ready foundation with built-in governance, protection, and compliance. Analyze: Build and scale AI with trust and explainability.Analyze the client's data in smarter ways and benefit from AI models that empower the client's team to gain new insights and make better, smarter decisions. Infuse: Operationalize AI throughout the business.Operationalize AI throughout the business - across multiple departments and within various processes - drawing on predictions, automation, and optimization.   Then, spanning the four steps of the AI ladder is the concept of Modernize, which is how clients can simplify and automate how they turn data into insights by unifying the collection, organization and analysis of data, regardless of where it lives, within a multicloud data platform.
• #9: What is meant by "modernize"? Modernize means building an information architecture for AI that provides choice and flexibility across a client's enterprise. As clients modernize for an AI and multicloud world, they will find that there is less "assembly required" in expanding the impact of AI across the organization.   IBM has the depth and breadth of capabilities to help clients: Deploy an information architecture for AI Prepare data for a multicloud world Infuse AI everywhere, with confidence Harness the flexibility and growth of open source technologies Speed time-to-value with unprecedented simplicity & agility   Our lead-with products supporting this rung are:​ IBM Cloud Pak for Data IBM Cloud Pak for Data Systems
• #10: Most organizations are still in the early days of determining how and where to use AI to advance their business agenda. But, interest is growing, largely because AI has the potential to solve one of the biggest challenges we face: we’re drowning in data – 2.5 quintillion bytes – generated every day, but we’re starved for insights. AI helps us take advantage of all this data, much of which is dark and inaccessible, and make better decisions from the insights it can produce, while creating net-new or augmenting existing workflows across the entire organization. Building on projects with thousands of organizations around the world, we’ve observed that organizations choose to: Speed time to value with pre-built AI apps for common use case (e.g., customer service, financial planning) Automate knowledge work and business processes Employ AI-assisted business intelligence and data visualization Automate planning, budgeting and forecasting analytics Customize with Industry vertical AI-driven frameworks Innovate with new business models intelligently powered by AI Our lead-with products supporting this rung are:​ IBM Watson Assistant IBM Cognos Analytics IBM Planning Analytics IBM Watson Discovery
• #11: AI is all about the ability to build, deploy, catalog, and manage models, which is what IBM’s AI portfolio provides. It gives you the ability to: BUILD models for making predictions. Watson Studio supports this.  DEPLOY to put custom models into production, in an application or business process. This is where the model starts to make predictions for the client’s business and it happens with Watson Machine Learning.  CATALOG for data discovery and activation. The Watson Knowledge Catalog allows users to access, curate, categorize, and share data and assets wherever they are. MANAGE to operationalize and automate the management of models and tools across your business, with trust and transparency. IBM’s answer for that is Watson OpenScale.   All of this together is our AI portfolio that you can access on the private cloud, on the public cloud, on prem, or on desktop. Watson Application Services are packaged applications that we bundle together to provide the user with AI in a box.  AI Open Source Framework: This is, without a doubt, the future of the market. We built our AI portfolio on open source to make it secure and managed so that our clients can put it to use without the risk or extra cost.  IBM Cloud Private for Data: IBM’s AI portfolio is designed to deploy on our unifying platform. This is our prescriptive approach—one platform, supporting a multi-cloud environment, that brings all of a client’s data and AI capabilities into one set of collaborative workflows and governance capabilities. 
• #21: Without having to move to a new database we have DS solution that will work for you – coud, local dektop, connectors “we are not making money on where your data is hosted” – you don’t have to move your data These are things I’m running into Building upon previous slides what are we introducing today? Meets your wants while solving your problems Low to high DS skills spectrum How does WS fit into the trends – looking at BMC for cloud + WS  whatis our value to customer; how are we differentiating from SPSS Modeler? 30 min filling in BMC 30 mins which messages do we want to articulate given strwngths as offering and where we’re going Goal: crisp & differrentiating Okay so now diving a level deeper – and again each of these will go back to the 3 values we have on the board WS = DS platform where your whole team can work together – meaning DS, LOB expert, business analyst, or anyone working on the DS platform – it’s very easy to bring all these people with different levels of expertise on the 1 platform of WS The reason all these varying levels of expertise can work together is because of these tools you see on the right DS are rare resources – they each have their own preferences of tools or languages that they want to work with so we have open source tools like R studio and Jupyter Notebooks where DS can come in and write their own code by choosing their own language (R, Scala, whatever they want), and go ahead and get started At the same time, if someone is not really into coding but has the knowledge, they can use visual tools - like SPSS Modeler, Data Refinery, & Model Builder – where they have a drag and drop UI they can use to build a model normally and get predictions Both sides are available in the same platform of Watson Studio. You can give different access to different people. The whole point is that WS really reduces the barrier between your resources and the DS journey – anyone can get started regardless of their expertise  which brings us back to the strategy of increasing your team productivity You can see on the right, we provide different technologies and frameworks to support building models - you can use Spark or if you're working with Deep Learning we support all the other favorites so you can choose any of these and work on Studio Let's say you're into deep learning, but you are not a coder, we have a drag & drop interface where you can build a deep learning model, expose that node, and use it in some other applications We provide all these cutting edge infrastructure technologies so you can take advantage of any of them while you are working in WS Again: easy, fast, and increases your team productivity Again getting started very quickly to increase productivity
• #22: So What is Watson Studio Desktop? What are the key capabilities? What benefits do you offer?