Modern Predictive Analytics: Empowering Businesses with AI, Schemes and Mind Maps of Mathematics

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A business guide

to modern predictive

analytics

What’s inside

• Why this guide?
• The big picture
• Why predictive analytics and AI matter
• The tipping point for AI adoption
• How can AI augment your business?
• Climbing the AI ladder
• What are your solution options?
• Taking the next step
• Key takeaways
• Glossary
• Why combine decision optimization?

4

The big picture

As the AI revolution takes hold, businesses are increasingly asking their data science teams to tackle the big questions. As a result, data scientists are expected to do much more than work on one-off research projects. They need to find repeatable, automated ways to provide real-time insights for day-to-day decision-making. To meet these expectations, data science leaders not only need to be able to explain the potential of modern predictive analytics technologies to business stakeholders—they also need to deliver the results. The ability to define and execute a successful data science strategy will be one of the key differentiators between leaders and followers in the years ahead. This is no simple task. Building up your data science capabilities will involve the following activities:

• Attracting and retaining a disparate team of skilled specialists
• Empowering them to collaborate seamlessly
• Putting sound governance structures in place to ensure that predictive models can always be trusted by the business Above all, data science and business teams need to find new ways to collaborate effectively. These methods include understanding what predictive analytics can do and identifying the areas where AI will drive business advantage.

How do our customers behave?

Why are our markets fluctuating?

What makes our business strategies

succeed or fail?

What will happen next?

How are the projected funded?

Where are the buying centers?

5

Why predictive analytics

and AI matter

Predictive analytics is not a new concept. Statisticians have been using decision trees and linear and logistic regression for years to help businesses correlate and classify their data and make predictions. What’s new is that the scope of predictive analytics has broadened. Breakthroughs in machine learning and deep learning have opened up opportunities to use predictive models in areas that have been impractical for most business investments—until now. Enterprises are seeing an unprecedented confluence of intuitive tools, new predictive techniques and hybrid cloud deployment models that are making predictive analytics more accessible than before. This situation has created a tipping point. For the first time, organizations of all sizes can do the following activities:

• Embed predictive analytics into their business processes
• Harness AI at scale
• Extract value from previously unexplored “dark data”— including everything from raw text to geolocational information If you can evolve from departmental, small-group AI projects and advance toward an enterprise data science platform, your organization stands to gain significant competitive advantage. Those who don’t seize the opportunity risk falling behind the curve.

$77.6 billion

will be spent on cognitive

and AI systems by 2022

(Source: IDC)

7

How can enterprises integrate analytics

into our business processes?

Before: Generate static reports for manual analysis by business experts. Now: Seamlessly embed predictive models into new apps and enterprise applications.

How do enterprises inject artificial intelligence

into modern applications?

Before: A total disconnect between application development and data science teams means each deployment is a custom process. Now: The data science lifecycle is designed to create a standardized, repeatable process for AI integration.

How do enterprises implement governance?

Before: Ad hoc adherence to policies at departmental level, with minimal visibility or traceability. Now: A coherent governance and security framework enables enterprise-wide policies to be enforced at scale.

How can enterprises progress on their

analytics journey?

Before: Each step from descriptive to predictive and prescriptive analytics requires separate tools, skills and investment. Now: An integrated platform supports analytic progression, simplifies onboarding and grows with you as your needs change and skills develop.

→ Back to Table of Contents

8

How can AI augment

your business?

In theory, adopting a modern approach to predictive analytics should be straightforward. The technology is no longer an obstacle, and better tooling is lowering the barriers to entry significantly. However, in practice, delivering value can still be a challenge. It’s especially easy for business stakeholders to get caught up in the hype around AI and have unrealistic expectations of what data science can achieve.

Defining use cases

The first task for data science and business leaders is to work together to identify concrete, practical use cases where modern predictive analytics can deliver value. Some use cases may be generally applicable across most industries, such as the following examples:

• Product recommendation and “next best action” models for sales and marketing teams
• Contact center automation for customer support teams Other use cases may be specific to a particular industry, department or even team within a business. These tend to be more difficult to execute, but they have a greater potential to unlock unique competitive advantages.

Which business functions are

leading business investment

in AI systems?

sales and marketing

customer support

(Source: Forrester Research)

→ Back to Table of Contents

Industry-specific use cases

Innovative organizations across many industries are already investing in building their own predictive models to solve specific business problems. The next two pages highlight just a few of the potential applications for AI and predictive analytics across several major industries.

Commercial banking

Commercial banks use predictive analytics for the following tasks:

• Assess market and counterparty risk on trades
• Assess credit risk for loan applications
• Detect fraudulent transactions in real time
• Harness predictive modeling to accelerate loan approval processes

Insurance

Insurers use predictive analytics for the following tasks:

• Detect fraudulent claims
• Optimize quotes and premiums by assessing relevant risks for each applicant
• Predict hazardous weather events to reduce auto insurance claims

Energy and utilities

Utilities use predictive analytics for the following tasks:

• Manage vast networks of physical assets
• Forecast production and demand patterns
• Predict outages before they happen
• Plan for supply and demand

Government

Governments rely on accurate statistics to inform policy-making across many areas, including the following use cases for predictive analytics:

• Detect benefit fraud
• Predict usage patterns for public services
• Optimize waste management and traffic flows

Manufacturing

Manufacturers use predictive analytics for the following tasks:

• Keep production lines running smoothly by modeling product quality and detecting defects
• Optimize warehouse management and logistics
• Develop sensors for autonomous vehicles by using machine learning models

Retail

Retailers use predictive analytics for the following tasks:

• Manage customer loyalty programs
• Boost cross- and up-selling by making targeted recommendations based on customer profiles and sophisticated propensity models
• Enable accurate demand forecasting

Food

The food industry uses predictive analytics for the following tasks:

• Automate data collection and analysis on food health
• Predict and warn of potential health outbreaks to enable rapid intervention
• Protect companies’ sensitive data, making it safe for competitors to collaborate

Climbing the AI ladder

Infuse - Operationalize AI with trust and transparency Analyze - Scale insights with AI everywhere Organize - Create a trusted analytics foundation Collect - Make data simple and accessible

Data of every type, no matter where it lives

Achieving success with modern predictive analytics is a journey. It’s important to pitch AI strategy at the right level for a business, taking both technical and organizational maturity into account. Data science and business leaders need to work together to define the best and fastest way to deliver business value. From the technical perspective, you can visualize AI maturity as a ladder. The first step on the ladder is data collection, because without data, you won’t have anything to analyze or model. The next step is data organization. Add metadata for governance and discoverability, to ensure that the right data is always available to the data scientists who need it. While data collection and organization are important topics, they’re beyond the scope of this guide. Instead, let’s focus on helping climb the following top two levels of the ladder:

• Analyzing data by building, training and testing predictive models
• Infusing AI into operations by deploying those models into production as part of your applications

What are your solution options?

Interact with pre-built AI services

Watson application services

AI open source frameworks

Build Watson Studio Deploy Watson Machine Learning Manage Watson OpenScale Catalog Watson Knowledge Catalog Unify on a multicloud data platform

IBM Cloud Private for Data

The AI portfolio from IBM offers everything you need to reach the top rungs of the AI ladder. Pre-built AI services such as Watson Assistant and Watson Visual Recognition help you address common use cases quickly and efficiently, delivering value fast. When you’re ready to start developing your own AI solutions, Watson Studio and Watson Machine Learning provide seamless workflows for building, training and deploying predictive models. These solutions empower you by harnessing both state-of-the-art IBM tools and the best open source AI frameworks. Watson Knowledge Catalog provides robust data governance and discoverability for models and data, while Watson OpenScale helps you monitor and manage models in real time—boosting accuracy, increasing explainability and mitigating bias. IBM Cloud ™ Private for Data unifies access to all these capabilities and provides a powerful multicloud data platform. IBM Data Science Premium add-on for IBM Cloud Private for Data provides the additional data science productivity capabilities such as SPSS Modeler and Decision Optimization to accelerate the time to value and increase the chance of your AI/ML project success.

Taking the next step

Depending on their level of progress on the AI ladder, businesses may have different requirements based upon the level of predictive analytics adoption across their organization.

Starting out

When businesses begin building their data science capabilities, they often start with ad hoc projects—developing models to answer specific questions or support research projects. With solutions such as Watson Studio Desktop, data scientists can work 24x on their own computers or laptops and sync up with a wider team when needed.

Growing up

When data science is adopted widely, different departments need to deploy their models, connect them to data sources and infuse them into production applications. Watson Studio and Watson Machine Learning make it easier for departmental data science and IT teams to collaborate across this lifecycle.

Going enterprise-scale

Once AI is embedded into business-critical processes, building a central platform is vital in order to manage and govern models and data. IBM Cloud Private for Data can provide the infrastructure and tools required for a comprehensive, multicloud platform that acts as a single point of control.

Getting practical

Whether you’re a data scientist or a business leader, the best way to learn how the modern predictive analytics portfolio from IBM can transform your business is to experience it for yourself. Try one of the following tutorials to get started:

Perform a machine learning exercise

Dive into machine learning by performing an exercise in IBM Watson Studio using Apache SystemML. Learn more

Create a scoring model to predict heart rate failure

Use IBM Watson Studio to build a predictive model with IBM Watson Machine Learning. Learn more

Predict equipment failure using IoT sensor data

See how IBM Watson Studio can analyze multivariate Internet of Things (IoT) sensor data and predict equipment failure. Learn more

Analyze open medical datasets to gain insights

Use IBM Watson Studio to run machine learning classifiers and compare the outputs with evaluating measures. Learn more

Shape and refine raw data

Work with IBM Data Refinery to prepare large data sets for predictive analysis. Learn more

Key takeaways

The modern predictive analytics portfolio from IBM offers the following benefits data science and business leaders can use to help seize competitive advantage in the age of AI:

Scale

• Reduce operational workload and costs by automating data science and data engineering tasks
• Train, test and deploy models seamlessly across multiple enterprise applications
• Extend common data science capabilities across hybrid, multicloud environments

Speed

• Accelerate development by harnessing pre-built applications and pre-trained models
• Deliver value faster by helping data science and business teams collaborate
• Streamline model building by combining state-of-the-art IBM and open source software

Simplicity

• Take advantage of a central platform to manage the entire data science lifecycle
• Standardize development and deployment processes
• Create a single framework data governance and security across the organization

Watson Studio helps businesses

focus on solving problems and

identifying opportunities.

Watson Machine Learning empowers

businesses to deploy and manage

models to give the results they need fast.

Learn more Learn more

Glossary

Algorithms are sets of rules that define a sequence of operations that can be applied to data to solve a particular problem. In a data science context, the term encompasses a huge range of techniques, including the following:

• Decision trees and regression models
• Autoregressive Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA) and exponential smoothing
• Transfer functions with predictors and outlier detection
• Ensemble and hierarchical models
• Vector machine and temporal causal modeling
• Time series and spatial AR for spatiotemporal prediction
• Generative adversarial networks (GANs) and reinforcement Your data science platform should give you easy access to all these powerful algorithms. Artificial intelligence (AI) is the ability of computer systems to interpret and learn from data. The term is most commonly used to describe systems built using machine learning or deep learning models. AI techniques can be used to enable computers to solve a wide range of problems that were previously considered intractable. Bias is a common issue when designing, training and testing models that can lead to inaccurate predictions. Mitigating bias by monitoring and auditing models during runtime is an increasingly important topic as businesses seek to adopt AI more widely. Classification models aim to put data points into categories by comparing them with a set of data points that have already been categorized. The result is a discrete value, meaning one of a limited list of options, rather than a score. For example, a classification model can give a yes or no answer on whether customers are likely to make a purchase or if they are a bad credit risk. Classification models can be built using various techniques, including decision trees and logistic regression. Content analytics is the analysis of unstructured data in documents of various formats, including text, images, audio and video files. Machine learning techniques can greatly accelerate analyzing large repositories of content that would previously have required workers hundreds or thousands of hours to review and classify. Data science is a wide-ranging discipline that unifies aspects of statistics, data analysis and machine learning to harness data to solve business problems. Deep learning is a branch of machine learning that uses neural networks with large numbers of hidden layers. These highly sophisticated networks are used in cutting-edge fields of deep learning such as computer vision, machine translation and speech recognition. Training a deep neural network is extremely computationally intensive, typically requiring clusters of machines with high- performance processors. A hybrid cloud platform such as IBM Watson Studio or IBM Cloud Private for Data can make this kind of infrastructure more accessible and affordable for companies of all sizes.

Deployment is the process of integrating a model into your business applications and running that model against real-world data. Making and moving the model through test, staging and production environments requires collaboration between your data science, application developers and IT operations teams. It can be challenging to integrate open source data science tools with the organization’s existing continuous integration and deployment pipeline. To avoid manual deployments with multiple handovers between teams, a coherent data science platform with automated deployment capabilities can be a major advantage. Development of predictive models involves the use of traditional statistical techniques or machine learning algorithms to create and refine models by training and testing them against your data sets. The development process is highly iterative; you may need to train dozens or even hundreds of models to achieve the level of accuracy you require. That’s why automating the workflows around model development and training can deliver huge value. Explainability is an important attribute of any system that uses predictive models to make recommendations and assist business decision-making. A predictive model seen as complex and mysterious can be difficult to convince business stakeholders, regulators and customers to trust its output. The advanced runtime monitoring and logging capabilities of Watson OpenScale provide context around each decision, making AI models transparent and auditable. Exploration of data is an important part of the model building process. This activity aims to reveal interesting features in a given data set, uncover hidden relationships and highlight use cases where predictive modeling could deliver business value. During the exploration phase, it’s critical to exercise data science skills and business knowledge to define questions you want to answer and outcomes you want to predict. This may result in an iterative cycle of preparation and exploration until you have fully explored the domain and have the data in the right shape to proceed. Geospatial analytics is the analysis of geographic data such as latitude and longitude, postal codes and addresses. This analysis is extremely useful for solving many kinds of practical data science problems. A modern data science platform should make it easy to detect, parse and calculate geospatial information, and offer easy integration with mapping tools to visualize the results. Inference in artificial intelligence applies logical rules to the knowledge base to draw conclusions in the presence of uncertainty. With inference, users get a prediction that is simplified, compressed and optimized for runtime performance. Linear regression is a statistical process using one independent variable to explain or predict a value or score. Examples include the number of SKUs of a product sold in a given week or the percentage risk of a customer closing their account. Logistic regression is a statistical process used in predicting outcomes. The process differs from linear regression in that the one independent variable has only a limited number of possible values rather than infinite possibilities. Users employ logistic regression when the response falls into categories such as numeric orders like first, second, third and so on. Machine learning uses statistical techniques to derive sophisticated predictive models and algorithms from large data sets, without requiring explicit programming.