The Forrester Wave™ Enterprise Data Virtualization, Q1 2015

The data virtualization market is growing as enterprise architects adopt the technology to support requirements for secure, self-service access to real-time data. These solutions provide a virtualized data services layer that integrates data from heterogeneous data sources and content in real-time, near-real time, or batch as needed to support a wide range of applications and processes. Data provided through the data services layer can be updated, transformed, and/or cleansed when (or before) applications access it. Data virtualization can do more than just basic federation; it can do transactions that write back to the original data source.

Informatica, IBM, SAP, Oracle, Denodo Technologies, Cisco Systems are the leaders in The Forrester Wave™-Enterprise Data Virtualization, Q1 2015

For more please refer: https://www.informatica.com/forrester-enterprise-data-virtualization.html 

Gartner Magic Quadrant for Business Intelligence and Analytics Platforms : 2015

The BI and analytics platform market is undergoing a fundamental shift. During the past ten years, BI platform investments have largely been in IT-led consolidation and standardization projects for large-scale systems-of-record reporting. These have tended to be highly governed and centralized, where IT-authored production reports were pushed out to inform a broad array of information consumers and analysts. Now, a wider range of business users are demanding access to interactive styles of analysis and insights from advanced analytics, without requiring them to have IT or data science skills. As demand from business users for pervasive access to data discovery capabilities grows, IT wants to deliver on this requirement without sacrificing governance.

Tableau, Information Builders (WebFOCUS BI), MicroStrategy, QlikView , Oracle, IBM, SAP, MicroSoft & SAS are in the leader's quadrant for "Magic Quadrant for Business Intelligence and Analytics Platforms : 2015".

As a result of the market dynamics discussed above, for this Magic Quadrant, Gartner defines BI and analytics as a software platform that delivers 13 critical capabilities across three categories — enable, produce and consume — in support of four use cases for BI and analytics.

·         Enable

o   Business User Data Mashup and Modeling

o   Internal Platform Integration

o   BI Platform Administration

o   Metadata Management

o   Cloud Deployment

o   Development and Integration

·         Produce

o   Free-Form Interactive Exploration

o   Analytic Dashboards and Content

o   IT-Developed Reporting and Dashboards

o   Traditional Styles of Analysis

·         Consume

o   Mobile

o   Collaboration and Social Integration

o   Embedded BI

 Note: Content is from Gartner . Refer: http://www.tableau.com/gartner-magic-quadrant-2015 

Gartner Magic Quadrant for Advanced Analytics Platforms: 2015

Gartner defines advanced analytics as the analysis of all kinds of data using sophisticated quantitative methods (for example, statistics, descriptive and predictive data mining, simulation and optimization) to produce insights that traditional approaches to business intelligence (BI) — such as query and reporting — are unlikely to discover.

An advanced analytics platform provides a full suite of tools for use by knowledgeable users, traditionally data scientists. However, they are increasingly being directed at business analysts and "citizen data scientists," to enable them to perform a variety of analyses on different types of data. In today's market, much analysis is predictive in nature, although descriptive analysis is often needed as well, especially for data exploration. While these analytical capabilities remain important, additional analytic techniques such as forecasting, optimization and simulation will grow in importance.

SAS, IBM (SPSS), Knime & RapidMiner are in the leaders quadrant for "Magic Quadrant for Advanced Analytics Platforms: 2015".

Gartner Evaluated considering following Criteria:

• Product or Service: A use-case-weighted average of the scores in the accompanying "Critical Capabilities for Advanced Analytic Platforms"
• Customer Experience: A combination of feedback from users about their overall satisfaction with the company and its product, and the product's integration.
• Overall Viability: An evaluation of the viability of best-of-breed vendors and the importance of this product line to larger vendors.
• Market Responsiveness and Track Record: An evaluation based on the size of the active customer base and new sales traction since last year.
• Marketing Execution: An evaluation based on how well the product has achieved market awareness and the market's understanding of its value proposition.

Note: Content from Gartner

Magic Quadrant for Data Warehouse and Data Management Solutions for Analytics 2015

Magic Quadrant's definition: a data management solution for analytics is a complete software system that supports and manages data in one or many disparate file management systems (most commonly a database or multiple databases) that can perform relational processing (even if the data is not stored in a relational structure) and support access and data availability from independent analytic tools and interfaces.

Teradata (TD, AsterData, UDA suite), IBM (PureData.. Netezza), Oracle (Exadata ..Oracle Big Data Appliance), SAP (IQ & Hana), Microsoft (SQL Server Parallel Data Warehouse and HDInsight) & HP (Vertica, Autonomy) are in the leader's quadrant of "Magic Quadrant for Data Warehouse and Data Management Solutions for Analytics 2015" considering following criteria by Gartner:

• Relational data management.
• Non-relational data management.
• No specific rating advantage is given regarding the type of data store used (for example, RDBMS, HDFS, key-value, document; row, column and so on).
• Multiple solutions in combination to form a DMSA are considered valid (although one approach is adequate for inclusion), but each solution must demonstrate maturity and customer adoption.
• Cloud solutions (such as PaaS) are considered viable alternatives to on-premises warehouses; and ability to manage hybrids between premises and the cloud are considered advantageous.
• DMSAs are expected to coordinate data virtualization strategies for accessing data outside of the DBMS, as well as distributed file and/or processing approaches.

The Forrester Wave™: B2C Commerce Suites, Q1 2015

The market for commerce suite technology is mature, yet it is set to almost double from a $1.2 billion market in 2014 to $2.1 billion in the US alone by 2019. Although adoption of commerce technology in B2C verticals such as retail and consumer products is already largely saturated, much of the anticipated growth over the next five years will be driven by replatforming activities as established online retailers look to fortify the scalability of their technology and branded manufacturers increase their focus on direct-to-consumer (DTC) digital channels.

IBM, Oracle Commerce, Hybis, Demandware & eBay Enterrpise are the leaders in The Forrester Wave™: B2C Commerce
Suites, Q1 2015.

Gartner Magic Quadrant for Data Quality Tools : 2014

Informatica, IBM, Trillium Software, SAS & SAP are in the leaders quadrant for “Gartner Magic Quadrant for Data Quality Tools : 2014”

Market Definition/Description

Data quality assurance is a discipline that focuses on ensuring data is fit for use in business processes. These processes range from those used in core operations to those required by analytics and for decision making, regulatory compliance, and engagement and interaction with external entities.

As a discipline, data quality assurance covers much more than technology. It also includes roles and organizational structures; processes for monitoring, measuring, reporting and remediating data quality issues; and links to broader information governance activities via data-quality-specific policies.

Given the scale and complexity of the data landscape, across organizations of all sizes and in all industries, tools to help automate key elements of this discipline continue to attract more interest and to grow in value. As such, the data quality tools market continues to show substantial growth, while also exhibiting innovation and change.

This market includes vendors that offer stand-alone software products to address the core functional requirements of the discipline, which are:

Data profiling and data quality measurement: The analysis of data to capture statistics (metadata) that provide insight into the quality of data and help to identify data quality issues.

Parsing and standardization: The decomposition of text fields into component parts and the formatting of values into consistent layouts, based on industry standards, local standards (for example, postal authority standards for address data), user-defined business rules, and knowledge bases of values and patterns.

Generalized "cleansing": The modification of data values to meet domain restrictions, integrity constraints or other business rules that define when the quality of data is sufficient for an organization.

Matching: The identifying, linking or merging of related entries within or across sets of data.

Monitoring: The deployment of controls to ensure that data continues to conform to business rules that define data quality for an organization.

Issue resolution and workflow: The identification, quarantining, escalation and resolution of data quality issues through processes and interfaces that enable collaboration with key roles, such as data steward.

Enrichment: The enhancement of the value of internally held data by appending related attributes from external sources (for example, consumer demographic attributes and geographic descriptors).

Gartner Magic Quadrant for Data Masking Technology - 2014

Gartner estimated that the overall SDM (static data masking) revenue of vendors in this Magic Quadrant will be approximately $300 million in 2014 — up from approximately $190 million in 2013 — thus making revenue growth close to 60% over the period of one year. More than 75% of this 2014 revenue has been earned by the three Leaders, while less than 25% has been earned by the other 11 vendors. This disproportion is worrisome to smaller IT vendors because it demonstrates their lack of ability to reach their target audiences, even though some of them have strong technical features with which to challenge Leaders.

IBM, Informatica & Oracle are leading the pack of “Gartner Magic Quadrant for Data Masking Technology – 2014”.

Market Definition/Description

Data masking (DM) is a technology aimed at preventing the abuse of sensitive data by giving users fictitious (yet realistic) data instead of real sensitive data. It aims to deter the misuse of data at rest, typically in nonproduction databases (static data masking [SDM]), and data in transit, typically in production databases (dynamic data masking [DDM]).

SDM for relational databases remains the most demanded technology, and, in this research, we highly value vendors' ability to execute in the SDM space (that is, to demonstrate maturity, quality and scalability of SDM technology, as well as the high revenue from and broad adoption of it). From a visionary's viewpoint, we highly value vendors' ability to offer DDM, the masking of the big data platform and suites with multiple data security technologies.