Analytics and business intelligence platforms — enabled by IT and augmented by AI — empower users to model, analyze and share data. Analytics and business intelligence (ABI) platforms enable organizations to understand their data. For example, what are the dimensions of their data — such as product, customer, time, and geography? People need to be able to ask questions about their data (e.g., which customers are likely to churn? Which salespeople are not reaching their quotas?). They need to be able to create measures from their data, such as on-time delivery, accidents in the workplace and customer or employee satisfaction. Organizations need to blend modeled and nonmodeled data to create new data pipelines that can be explored to find anomalies and other insights. ABI platforms make all of this possible.
Data masking is based on the premise that sensitive data can be transformed into less sensitive but still useful data. This is necessary to satisfy application testing use cases that require representative and coherent data, as well as analytics that involve the use of aggregate data for scoring, model building and statistical reporting. The market for data protection, DM included, continues to evolve with technologies designed to redact, anonymize, pseudonymize, or in some way deidentify data in order to protect it against confidentiality or privacy risk.
Elevating Test Data Management for DevOps is the process of providing DevOps teams with test data to evaluate the performance, and functionality of applications. This process typically includes copying production data, anonymization or masking, and, sometimes, virtualization. In some cases, specialized techniques, such as synthetic data generation, are appropriate. With that, it applies data masking techniques to protect sensitive data, including PII, PHI, PCI, and other corporate confidential information, from fraud and unauthorized access while preserving contextual meaning.
Enterprise information archiving (EIA) solutions are designed for archiving data sources to a centralized platform to satisfy information governance requirements, including regulatory and/or corporate governance and privacy; improve data accessibility; surface new data insights; and gain operational efficiencies. There are several core capabilities of this market. They include archiving digital communication content, such as email, workstream collaboration, instant messaging (IM) and SMS; classifying data and enabling retention management of archive content; creating a searchable index of content; and providing basic tools for e-discovery and supervision.
The structured data archiving and application retirement market is identified by an array of technology solutions that manage the life cycle of application-generated data and accommodate corporate and regulatory compliance requirements. Application-generated data is inclusive of databases and related unstructured data. SDA solutions focus on improving the storage efficiency of data generated by on-premises and cloud-based applications and orchestrating the retirement of legacy application data and their infrastructure. The SDA market includes solutions that can be deployed on-premises, and on private and public infrastructure, and includes managed services offerings such as SaaS or PaaS.