The Forrester Wave™: Augmented BI Platforms, Q3 2021

 "BI Platforms Get A Shot Of AI, While Holding True To Their Core"

What can AI do to further democratize access to data and keep improving insights-driven maturity? With this in mind, business insights pros now seek augmented BI features that go beyond reporting, querying, data visualizations, and basic descriptive and diagnostic analytics. Industry leaders already realize the mission-critical nature of these platforms — deploying BI platforms infused with the power of AI can be a key competitive differentiator.

Microsoft (Power BI), Oracle, Sisense, Tibco (Spotfire), Salesforce (Tableau) and DOMO are the leaders in 'The Forrester Wave™: Augmented BI Platforms, Q3 2021'

Forrester Wave™: Data Governance Solutions, Q3 2021

Data protection and privacy regulations such as the EU’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and Singapore’s Personal Data Protection Act (PDPA) have been major drivers for data governance initiatives and the emergence of data governance solutions.They have expanded the scope of data governance from an initial technical focus (master data management, data catalogs, data quality, etc.) to include data privacy, protection, and sovereignty. But organizations have an ever-increasing appetite to leverage their data for business advantage, either through internal collaboration, data sharing across ecosystems, direct commercialization, or as the basis for AI-driven business decision-making. While doing so, organizations must take care to maintain employee, partner, and customer trust in their approach of leveraging data (and technology fueled by data). This requires data governance and data governance solutions to step up once again and enable data-driven businesses to leverage their data responsibly, ethically, compliantly, and accountably.

Alation, Infogix, Collibra, Ataccama are the leaders in "Forrester Wave™: Data Governance Solutions, Q3 2021"

Forrester Wave™: Multimodel Data Platforms, Q3 2021

 Multimodel data platforms represent the intersection of multiple data models such as document, graph, and relational in a single data platform, offering speed, scale, performance, integration, and security over the polyglot persistence model. Combining multimodel data types and models on large-scale memory helps deliver real-time, consistent, and trusted data to support new business requirements. Most organizations leverage multimodel data platforms to support microservices-based applications and a common data platform for customer 360, fraud detection, internet-of-things (IoT) analytics, and highly interactive edge applications.

As a result of these trends, multimodel data platforms customers should look for providers that:

• A platform that delivers built-in speed, scale, and security to meet your requirements.
• A platform that can support multimodel apps and insights quickly through automation.
• A roadmap that is as bold as your multimodel ambitions.

SAP (HANA, HANA Cloud), Oracle, MongoDB (Enterprise & Atlas), Marklogic, InterSystems, Microsoft (Azure Cosmos DB), Couchbase are the leaders in "Forrester Wave™: Multimodel Data Platforms, Q3 2021"

The Forrester Wave™: Dynamic Data Masking Solutions, Q3 2021

 Tech investment and customer needs for dynamic data masking are increasingly driven by privacy compliance requirements like GDPR and CPRA, in addition to requirements like HIPAA, PCI, and more.

Today there is a range of approaches to architecture and deployment options for dynamic data masking solutions; there is no right or wrong approach, only what is best suited for your environment and needs. Some dynamic data masking solutions are embedded in the database, while others integrate at the middle and application tier. Also, some solutions are available on-premises, while others leverage native cloud capabilities.

Check out the 'The Forrester Wave™: Dynamic Data Masking Solutions, Q3 2021'

The Forrester Wave™: Streaming Analytics, Q2 2021

 treaming analytics allows enterprises to understand what is happening now, in real time, by analyzing streaming data from multiple real-time data sources. Enterprise customers have great options for choosing a streaming analytics platform from tried-and-true vendors, public cloud service providers, open source, and startups. The common theme in all of these vendors is a focus on development tools, scalability, and adding ever-richer analytics capabilities. 

Google (Google Cloud Dataflow), Microsoft (Azure Stream Analytics), SAS (SAS Event Stream Processing), Oracle (GoldenGate Stream Analytics (GGSA))and Tibco Software (TIBCO Streams) are the leaders in "The Forrester Wave™: Streaming Analytics, Q2 2021"

Magic Quadrant for Multichannel Marketing Hubs 2021

 Digital marketing leaders use multichannel marketing hubs to deliver contextually relevant experiences on complex customer journeys. Unified customer profiles backed by predictive insights help orchestrate personalized, multichannel engagement. Use this research to identify suitable MMH solutions.

Gartner defines the multichannel marketing hub (MMH) as a technology that orchestrates a company’s communications and offers to customer segments across multiple channels. These include websites, mobile, social, direct mail, call centers, paid media and email. MMH capabilities also may extend to integrating marketing offers/leads with sales for execution in both B2B and B2C environments.

Salesforce, Adobe, Oracle, SAS, Acoustic & SAP are the leaders in "Magic Quadrant for Multichannel Marketing Hubs 2021"

The Forrester Wave™: Cloud Data Warehouse, Q1 2021

 Today, cloud data warehouse (CDW) solutions are changing the way we deliver modern analytics. They can provision any size data warehouse in minutes, auto-tune queries, scale resources such as compute and storage based on demand, and automatically upgrade to the latest version. To address the growing need for more integrated, real-time, and self-service analytics, CDW vendors continue to focus on native integration with data lakes and object stores; self-service to simplify access and administration for larger and more complex warehouses; and advanced capabilities on parallel processing, compression, partitioning, indexing, query optimization, and dynamic resource provisioning. The most common CDW use cases include customer analytics, AI/machine learning (ML)-based analytics, vertical-specific analytics, and real-time analytics.

Google (BigQuery), Microsoft (Azure Synapse Analytics ), Amazon Web Services (Redshift), Teradata (Teradata Vantage in the Cloud ), Oracle (Oracle Autonomous Data Warehouse) & Snowflake (Snowflake Data Cloud ) are the leaders in “The Forrester Wave™: Cloud Data Warehouse, Q1 2021”.