Data and AI Engineering capabilities on a public cloud make it easy to collect, store, process, and analyze unimaginable volumes of data. Still, one of the major challenges for business and IT leaders today is deriving meaningful insights from all this data and making it available across the organization.
Data and AI democratization refer to the process of making data and artificial intelligence (AI) tools and technologies available to a broad range of users rather than being limited to a small group of technical experts or a specific organization. The aim of data and AI democratization is to empower individuals and organizations to leverage the power of data and AI to improve decision-making, drive innovation, and create new business opportunities.
There are four important dimensions of Data & AI democratization:
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Empowerment: Enabling users to take control of their data and AI processes rather than being dependent on a small group of engineers.
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Responsiveness: Encouraging data and AI systems to be responsive to users’ needs and feedback, and adaptable to different user groups and use cases.
Enablement
Democratization can be enabled by technologies such as open-source software, cloud computing, and low-code/no-code platforms. It allows for more and more people to have access to data, AI models, and platforms, even if they are not experts on it, thus contributing to a more informed organization, better decision-making, and more efficient services.
Democratizing data & AI engineering involves making data and AI tools accessible to a wider range of people within an organization rather than just a select few engineers or analysts. This can be achieved through a combination of technical and organizational changes. The four dimensions can further be split into more granular realization views.
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Self-service tools: Provide self-service tools for data & AI. This allows non-technical users to access and work with data without needing to rely on data engineers.
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Data-driven decision making: Implement data driven decision-making. Encourage all employees to use data to inform their decisions and encourage leaders to make data-driven decisions.
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Technology view of Enablement
Self Service Data Tools
Having self-service data tools is one of the most important requirements to enable data & AI democratization. These data tools abstract complex engineering tasks for average users. One can take a structured approach towards creating these self-service tools. You need to make sure that these tools are addressing basic needs of the data lifecycle.
Google Cloud provides proven service for managing full data lifecycle.
Storage:
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BigQuery:
BigQuery is a fully-managed, cloud-native data warehouse that enables super-fast SQL queries using the processing power of Google’s infrastructure. It allows you to analyze large and complex datasets using a SQL-like syntax and integrates with other Google Cloud Platform (GCP) services for data storage, management, and analysis. BigQuery also provides a web UI and a command-line tool for managing and running queries and has a robust security model to protect data. Additionally, it offers features like real-time streaming, data integration, and machine learning capabilities.
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Cloud Storage:
Google Cloud Storage is a fully-managed, object-based storage service provided by Google Cloud Platform (GCP). It enables users to store and retrieve data in the cloud through a variety of interfaces, including a web UI, command-line tools, and APIs. Cloud Storage offers a range of storage classes, including multi-regional, regional, and nearline storage, that cater to different performance and cost requirements. It also provides built-in encryption, access controls, and data management features, such as versioning and lifecycle management.
Processing
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Dataflow:
Google Cloud Dataflow is a fully-managed service that allows developers to build data pipelines and process data in real-time or batch mode. It provides automatic scaling, fault-tolerance, and built-in support for common data processing tasks and integrates with other Google Cloud services. It also supports Apache Beam, an open-source programming model for data processing that allows code to run on multiple execution engines.
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Pub Sub:
Google Cloud Pub/Sub is a messaging service that allows for the sending and receiving of messages between independent applications. It is designed to handle high-throughput, low-latency communication and can be used to build real-time streaming data pipelines and applications. Pub/Sub allows developers to send messages to one or many “topics,” and subscribers can then receive and process those messages. The service is fully managed and scales automatically, allowing easy integration with other Google Cloud services.
Orchestration
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Composer:
Google Cloud Composer is a fully managed workflow orchestration service that runs on the Apache Airflow open-source project. It allows for the creation, management, and execution of complex multi-step workflows using Python code. With Cloud Composer, users can easily schedule, manage, and monitor their workflows. It also allows them to easily integrate with other Google Cloud services such as BigQuery, Cloud Storage, and Cloud Dataflow.
Analysis
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BigQuery ML:
BigQuery ML allows users to create and train machine learning models using SQL, which makes it accessible to data analysts and other users who are familiar with SQL but not necessarily with other machine learning platforms. This allows for more efficient and cost-effective machine learning, as well as easier collaboration between data analysts and data scientists.
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Looker:
It allows users to create and share interactive dashboards, reports, and visualizations and allows for data exploration, discovery, and collaboration. It uses Look ML to define the structure of the data, which enables users to perform complex calculations and transform data, without the need to write code.
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Vertex AI:
Google Cloud Vertex AI is a platform that allows developers to easily build and deploy machine learning models. It provides a set of tools and services for data preparation, model building, deployment, and management. With Vertex AI, users can leverage pre-built models and machine learning frameworks like TensorFlow and PyTorch or use AutoML to automatically train models using their own data. The platform also provides a suite of tools for monitoring and optimizing the performance of deployed models. It provides a secure and compliant environment for storing and managing data and ensures the privacy and security of the data and models at all times. Vertex AI is designed to make it easier for developers to build and deploy machine learning models, regardless of their level of experience with machine learning.
When you combine all of the above services together, you are essentially moving towards creating a self-service data platform. You are moving towards creating standardized, automated frameworks for all common tasks. Once these are in place, you can achieve very high efficiency and adoption of data-driven decision-making in the organization.
Cross-functional teams
Enabling cross-functional teams can help organizations to better collaborate, problem-solve, and innovate. The success of cross-functional teams highly depends on collaboration and sharing. In the context of data & AI democratization, it is very important to provide platforms where data can be shared and used in a secure and easy way. Here, I would like to introduce two more services from Google Cloud Data Catalog and Analytics Hub which can help in collaboration and sharing.
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Data Catalog:
It is a fully managed, scalable metadata management service within Dataplex. Data Catalog can catalog asset metadata from different Google Cloud systems. Data Catalog provides a centralized place that lets organizations achieve the following: (a) Gain a unified view to reducing the pain of searching for the right data. (b) Enriching data with technical and business metadata. (c) Take ownership over the data to improve trust and confidence in it.
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Analytics Hub:
Analytics Hub is a data exchange that allows you to efficiently and securely exchange data assets across organizations to address challenges of data reliability and cost. Analytics Hub makes the administration of sharing assets across any boundary even easier and more scalable while retaining access to key capabilities of BigQuery, like its built-in ML, real-time, and geospatial analytics.
Data Literacy
Data literacy in an organization refers to the ability of employees to understand, work with, and make decisions based on data. It generally includes:
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Data Concepts: Employees need to have a basic understanding of data concepts such as data types, data structures, SQL, and data modeling.
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Data Tools: Employees should be able to work with tools like spreadsheets, databases, and visualization software.
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Data Analysis: This includes the ability to find answers to regular business metrics, identify patterns, trends, and testing hypotheses, basics of machine learning algorithms, and understand and validate ML results.
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Data Ethics: Employees need to use data in a responsible manner, including understanding and respecting data privacy and security. They need to be made aware of compliance requirements and the ethical use of ML algorithms.
With Google Cloud services like BigQuery, BigQuery ML, Google Sheets, Looker Studio, Data Catalog & Analytics Hub, an organization can enable Data Literacy among its employees. Data-literate employees in an organization can make more informed decisions and identify new growth opportunities.
Data Governance Framework
A data governance framework is a set of guidelines, policies, and procedures that organizations use to manage and protect their data. Dataplex on Google Cloud is one of the services that can be used to implement a governance framework on data stored in Cloud Storage and BigQuery.
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Dataplex:
Organizations would have their data distributed across Cloud Storage and BigQuery. Dataplex enables you to discover, curate, and unify this data without any data movement, organize it based on your business needs, and centrally manage, monitor, and govern this data. Dataplex enables standardization and unification of metadata, security policies, governance, classification, and data lifecycle management across this distributed data.
Automation
Automation is one the very important factors in increasing efficiency and reducing errors. Automation in the context of Data & AI Engineering means automated deployment of data and ML pipelines, automated data quality checks to identify and correct errors in data, automated restart-ability of pipelines, and automated provisioning of infrastructure and services required for the data platform. The automation ambit needs to cover DevOps, DevSecOps, Data Quality, Data Pipeline resilience and performance, and scalability. Open-source tools like Jenkins, Terraform, Ansible, GitHub, etc., along with Google Cloud services like Cloud Build, Deployment Manager, Scheduler, and Cloud Composer, help build strong automation strategies and execution.
Data-driven Decision-making
Data-driven decision-making relies on facts based on data rather than intuition. To make data-driven decision-making a de facto way of decision-making, organizations need efficient ways of collecting, storing, processing, and analyzing data. In previous sections, we saw how different services could be enabled on Google Cloud to support a complete data lifecycle. Along with Data Literacy and Data Engineering self-service frameworks, an organization is set to adopt data-driven decision-making.
Conclusion
Moving data workloads, Enterprise Data warehouses, Data Lakes, and Analytics to the cloud could be an organization’s important strategic initiative. But the real value of moving to the cloud would realize when people in the organization are able to utilize and bring value to the business with the help of this data. Hence it’s important to keep the goal of democratizing data & AI as part of the Cloud Adoption Strategy.