According to Forrester, the automation of data management can be useful for organizations looking to maintain that competitive edge because of the many benefits that it offers. New approaches, such as data virtualisation, data mesh, artificial intelligence-enabled data integration, and data fabric, use advanced automation, connected data intelligence, etc to make data and analytics even more effective. It’s critical for enterprise architecture, technical architecture and delivery leaders to support the new generation of data and analytics requirements, including support for real-time customer 360, data intelligence and modern edge applications.

Data architecture deserves a high level of democratization and scalability that can be provided by Artificial Intelligence. New and innovative AI capabilities, drive the next level of DataOps solutions, helping enterprises to automate data functions, including data ingestion, classification, processing, security and transformation.

Enterprises face increasing data challenges, primarily due to the growing volume of data, compliance pressure, need for real-time information, increased data complexity, and data distribution across hybrid and multiple clouds. Business users want quick access to reliable, real-time information to help them make better business decisions.

Unified DataOps and MLOps pipelines

Robust back-end development, testing, and operationalization ensure that analytics are always accurate, relevant, and fit for purpose. DataOps pipeline processes continuously integrate, transform, and prepare data for deployment into analytics applications. MLOps pipelines handle the continuous building, training, serving, and optimization of machine learning, deep learning, natural language processing, and other statistical models.

Unified DataOps and MLOps pipelines improve how advanced analytics, artificial intelligence, and other intelligent applications are developed, tested, deployed, and optimized.

Data as a service

Data as a service (DaaS) is a concept that is only now beginning to see widespread adoption. However, Forrester predicts that it will keep growing in the coming years mainly because of the high demand for real-time trusted data and the self-service capabilities DaaS provides. The business benefits that come from low-cost cloud storage and bandwidth, combined with cloud-based platforms designed specifically for fast, large-scale data management and processing, have already win many industries. A common view of business and customer data using industry-standard protocols is a big asset that DaaS offers. It delivers a common data access layer through application programming interfaces (APIs), SQL, ODBC/JDBC and other protocols to support querying, reporting, data virtualisation, data mesh, integrated and custom-built applications and other advanced data integration technology.

Data mesh

Data mesh leverages a domain-oriented, flexible, self-serve design that embraces the ubiquity of data in the enterprise as it views “data-as-a-product,” with each domain handling their own data pipelines. Unlike traditional monolithic data infrastructures that lead to disconnected data producers and backlogged data teams, a data mesh offers a centralized database with domains responsible for handling their own pipelines. With data mesh you can match processing engines and data flows with the right use cases, achieve optimized mixed workloads and enable support for edge use cases. Developers, data engineers and architects become more productive and accelerate various business use cases as data mesh offers a communications plane between applications, machines and people in order to keep both human and machine party in sync. It supports a cloud-native architecture with federated computational governance.

Synthetic Data Generation

Synthetic data is data artificially generated by an AI algorithm that has been trained on a real data set. Synthetic data contains all the characteristics of production minus the sensitive content. As a result, the synthetic data set has the same predictive power as the real data, but none of the privacy concerns that impose user restrictions.

What makes it special is that data scientists, developers and data engineers have the complete control, and they don’t need to put faith in unreliable, incomplete data, or struggle to find enough data at the scale they need.

Data Observability

Inaccurate data and reporting lead key stakeholders to wrong decisions. Data Observability is the process of diagnosing the health of the entire data value chain by viewing its outputs in order to proactively find and mitigate the issues. It is able to detect inaccuracies in your data but also to enable users in identifying the cause of the problems and suggest proactive measures to make your system more efficient and reliable.

This means that data consumers can rely on excellent data to carry out their business and enterprises are more confident in their data when making data-driven decisions.

Data Governance

Data scientists that are extremely busy may often overlook data governance. Data governance is a collection of processes, roles, policies, standards, and metrics that ensure the effective and efficient use of information, it establishes the processes and responsibilities that ensure the quality and security of the data used across an organization.

An effective data governance strategy provides many benefits to an organization, such as a common understanding of data, ensuring data accuracy, completeness, and consistency. It provides the means of meeting the demands of government regulations while establishing codes of conduct and best practices in data management.

Especially in the case where organizations are moving to the cloud, then data governance finds its place. Cloud adoption is all about delegating certain tasks to third parties, such as infrastructure management, application development, security, etc, adding a layer of complexity regarding security and access.