The fact is that every company in the world today is a data company. As the Economist magazine rightly pointed out in 2017, data is the world's most valuable resource and unless you are leveraging your data as a strategic differentiator, you are likely missing out on opportunities.

Simply put, data is the fuel, the cloud is the vehicle, and AI is the destination. The intersection of these three pillars of IT is the driving force behind digital transformation disrupting every company and industry. To be successful, companies need to quickly modernize their portfolio and embrace an intentional strategy to re-tool their data, AI, and application workloads by leveraging a cloud-native architecture. So, cloud platforms act as a great enabler by infusing agility, while AI is the ultimate destination, the so-called nirvana that every enterprise seeks to master.

While the benefits of the cloud are becoming obvious by the day, there are still several enterprises that are reluctant to embrace the public cloud right away. These enterprises are, in some cases, constrained by regulatory concerns, which make it a challenge to operate on public clouds. However, this doesn't mean that they don't see the value of the cloud and the benefits derived from embracing the cloud architecture. Everyone understands that the cloud is the ultimate destination, and taking the necessary steps to prepare and modernize their workloads is not an option, but a survival necessity:

Figure 1.1 – What's reshaping how businesses operate? The driving forces behind digital transformation

IBM enjoys a strong Data and AI portfolio, with 100+ products being developed and acquired over the past 40 years, including some marquee offerings such as Db2, Informix, DataStage, Cognos Analytics, SPSS Modeler, Planning Analytics, and more. The depth and breadth of IBM's portfolio is what makes it stand out in the market. With Cloud Pak for Data, IBM is doubling down on this differentiation, further simplifying and modernizing its portfolio as customers look to a hybrid, multi-cloud future.

We all know data is the foundation for businesses to drive smarter decisions. Data is what fuels digital transformation. But it is 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. You can find the relevant source here: https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html.

However, the adoption of AI has been slower than anticipated. This is because many enterprises do not make a conscious effort to lay the necessary data foundation and invest in nurturing talent and business processes that are critical for success. 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 in terms of how you collect and organize data. Business leaders not only need to understand the power of AI but also how they can fully unleash its potential and operate in a hybrid, multi-cloud world.

This section aims to demystify AI, common AI challenges and failures, and 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.

As companies look to harness the potential of AI and identify the best ways to leverage data for business insights, they need to ensure that they start with a clearly defined business problem. In addition, you need to use data from diverse sources, support best-in-class tools and frameworks, and run models across a variety of environments.

According to a study by MIT Sloan Management Review, 81% of business leaders (http://marketing.mitsmr.com/offers/AI2017/59181-MITSMR-BCG-Report-2017.pdf) do not understand the data and infrastructure required for AI and "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).

IBM recognizes this challenge our clients are facing. As a result, IBM built a prescriptive approach (known as the AI ladder) to help clients with the aforementioned 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 AI ladder, enterprises can build a governed, efficient, agile, and future-proof approach to AI. Furthermore, it is also an organizing construct that underpins the Data and AI product portfolio of IBM.

It is critical to remember that AI is not magic and requires a thoughtful and well-architected approach. Every step of the ladder is critical to being successful with AI.

The rungs of the AI ladder

The following diagram illustrates IBM's prescriptive approach, also known as the AI ladder:


Figure 1.2 – The AI ladder – a prescriptive approach to the journey of AI

The AI ladder has four steps (often referred to as the rungs of the ladder). They are as follows:

1. 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.
2. Organize: Create a business-ready analytics foundation. Organize all the client's data into a trusted, business-ready foundation with built-in governance, quality, protection, and compliance.
3. 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.
4. Infuse: Operationalize AI throughout the business. You should do this across multiple departments and within various processes by drawing on predictions, automation, and optimization. Craft an effective AI strategy to realize your AI business objectives. Apply AI to automate and optimize existing workflows in your business, allowing your employees to focus on higher-value work.

Spanning the four steps of the AI ladder is the concept of Modernize from IBM, which allows clients to simplify and automate how they turn data into insights. It unifies collecting, organizing, and analyzing data within a multi-cloud data platform known as Cloud Pak for Data.

IBM's approach starts with a simple idea: run anywhere. This is because the platform can be deployed on the customer's infrastructure of choice. IBM supports Cloud Pak for Data deployments on every major cloud platform, including Google, Azure, AWS, and IBM Cloud. You can also deploy Cloud Pak for Data platforms on-premises in your data center, which is extremely relevant for customers who are focused on a hybrid cloud strategy.

The way IBM supports Cloud Pak for Data on all these infrastructures is by layering Red Hat OpenShift at its core. This is one of the key reasons behind IBM's acquisition of Red Hat in 2019. The intention is to offer customers the flexibility to scale across any infrastructure using the world's leading open source steward: Red Hat. OpenShift is a Kubernetes-based platform that also allows IBM to deploy all our products through a modern container-based model. In essence, all the capabilities are rearchitected as microservices so that they can be provisioned as needed based on your enterprise needs.

Now that we have introduced the concept of the AI ladder and IBM's Cloud Pak for Data platform, let's spend some time focusing on the individual rungs of the AI ladder and IBM's capabilities that make it stand out.

The Collect layer is about putting your data in the appropriate persistence store to efficiently collect and access all your data assets. A well-architected "Collect" rung allows an organization to leverage the appropriate data store based on the use case and user persona; whether it's Hadoop for data exploration with data scientists, OLAP for delivering operational reports leveraging business intelligence or other enterprise visualization tools, NoSQL databases such as MongoDB for rapid application development, or some mixture of them all, you have the flexibility to deliver this in a single, integrated manner with the Common SQL Engine.

IBM offers some of the best database technology in the world for addressing every type of data workload, from Online Transactional Processing (OLTP) to Online Analytical Processing (OLAP) to Hadoop to fast data. This allows customers to quickly change as their business and application needs change. Furthermore, IBM layers a Common SQL Engine across all its persistence stores to be able to write SQL once, and leverage your persistence store of choice, regardless of whether it is IBM Db2 or open source persistence stores such as MongoDB or Hadoop. This allows for portable applications and saves enterprises significant time and money that would typically be spent on rewriting queries for different flavors of persistence. Also, this enables a better experience for end users and a faster time to value.

IBM's Db2 technology is enabled for natural language queries, which allows non-SQL users to search through their OLTP store using natural language. Also, Db2 supports Augmented Data Exploration (ADE), which allows users to access the database and visualize their datasets through automation (as opposed to querying data using SQL).

To summarize, Collect is all about collecting data to capture newly created data of all types, and then bringing it together across various silos and locations to make it accessible for further use (up the AI ladder). In IBM, the Collect rung of the AI ladder is characterized by three key attributes:

• Empower: IT architects and developers in enterprises are empowered as they are offered a complete set of fit-for-purpose data capabilities that can handle all types of workloads in a self-service manner. This covers all workloads and data types, be it structured or unstructured, open source or proprietary, on-premises or in the cloud. It's a single portfolio that covers all your data needs.
• Simplify: One of the key tenets of simplicity is enabling self-service, and this is realized rather quickly in a containerized platform built using cloud-native principles. For one, provisioning new data stores involves a simple click of a button. In-place upgrades equate to zero downtime, and scaling up and down is a breeze, ensuring that enterprises can quickly react to business needs in a matter of minutes as opposed to waiting for weeks or months. Last but not least, IBM is infusing AI into its data stores to enable augmented data exploration and other automation processes.
• Integrate: Focuses on the need to make data accessible and integrate well with the other rungs of the AI ladder. Data virtualization, in conjunction with data governance, enables customers to access a multitude of datasets in a single view, with a consistent glossary of business terms and associated lineage, all at your fingertips. This enables the democratization of enterprise data accelerating AI initiatives and driving automation to your business. The following diagram summarizes the key facets of the Collect rung of the AI ladder:

Figure 1.3 – Collect – making data simple and accessible

Our portfolio of capabilities, all of which support the Collect rung, can be categorized into four workload domains in the market:

1. First, there's the traditional operational database. This is your system of records, your point of sales, and your transactional database.
2. Analytics databases are in high demand as the amount of data is exploding. Everyone is looking for new ways to analyze data at scale quickly, all the way from traditional reporting to preparing data for training and scoring AI models.
3. Big data. The history of having a data lake using Hadoop at petabyte scale is now slowly transforming into the separation of storage and compute, with Cloud Object Storage and Spark playing key roles. The market demand for data lakes is clearly on an upward trajectory.
4. Finally, IoT is quickly transforming several industries, and the fast data area is becoming an area of interest. This is the market of the future, and IBM is addressing requirements in this space through real-time data analysis.

Next, we will explore the importance of organizing data and what it entails.