Market Landscape Reports

Does information security analytics qualify as big data? Considering the challenges involved in capturing, processing, storing, searching, sharing, analyzing, and visualizing all of the data that an organization collects from log files, external intelligence feeds, and other sources, this question is clearly answered as many organizations say that security data collection and analysis would be considered big data within their organizations today. ESG defines the term “big data” as follows: In information technology, big data is defined as a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.

Social media creates new opportunities for companies that want to engage better with customers. Real engagement, which binds customers to companies, can drive revenue gains and reduce the costs associated with customer churn. However, engagement through social media requires new ways to manage and understand customer interactions.

For decades, relational databases (RDBMS) have dominated the enterprise landscape. Strictly on a market share basis, longstanding RDBMS products from providers such as Oracle, IBM, Microsoft, and Sybase (SAP), herein referred to as “classic RDBMS,” still reign over the enterprise database scene. The classic RDBMS offerings represent commercial technologies designed around E. F. Codd’s relational model. However, a large number of new databases have come to market during the 2000s, with a particularly strong incursion over the past five years, to challenge classic RDBMS.

Connecting People Across the Enterprise

Social collaboration seems like a redundant term. Collaboration happens when groups of people come together to work on a problem. On the surface, all collaboration is inherently social. That may be true for how humans act, but not for software. Until recently, most software was designed to facilitate transactions or movement of data, not human interaction.

The need for greater business agility and responsiveness has spawned a desire for software solutions that help teams with diverse skills, which are often geographically dispersed, to work better together. This need has been met with social collaboration solutions that encompass a host of tools which allow groups to interact and self-organize better.

Social ERP, CRM, Project Management, and PLM Solutions

Whether the connection is between employees, customers, or partners, social enterprise applications exist to facilitate collaboration and communication leading to more meaningful business interactions. These interactions, in turn, lead to improved and faster decision making, and a more agile organization.

The Case for, Types of, and Purchasing Considerations

It's almost impossible to conduct a conversation with a storage vendor nowadays without talking about solid-state storage (SSS). Along with virtualization and cloud, it's one of those de rigueur phrases. And, like those other two, SSS is suffering from an overexposure that can assume familiarity and understanding where it does not exist. While there is tremendous user value to be gained from employing solid-state storage, there is also, at times, a dramatic lack of clarity and of semantic accuracy in the market. This report sets out to address this and to outline both the generic value of solid-state storage and the specific landscape of options that are available.

To assess how endpoint security technologies will align with rapidly evolving market requirements, ESG interviewed a number of leading security vendors.  This Market Landscape Report examines the trends in the endpoint security market that affect IT professionals, provides guidelines for purchasing considerations, and categorizes and reviews the various vendors' solutions.

In order to improve upon current analytics capabilities, organizations must have data-a huge amount of detailed data that allows them to identify patterns, pick up on trends, and predict outcomes with greater accuracy. This unquenched thirst for data is driving IT organizations to work in collaboration with their data analytics teams to evaluate new platforms that can meet the demanding business requirements without cracking under the pressure of massive volumes of data. After much examination of the various options available on the market today, many organizations have narrowed their focus to Hadoop as an alternative big data analytics platform. Hadoop-an emerging open-source distributed computing platform designed to address the need for performing data analytics on extremely large data sets-seems to offer exceptional capability at an affordable price point. But with so many options now available in the market, how can organizations begin to structure their requirements to better identify the distribution that best meets its needs?