Make more informed decisions with real-time reporting on opinions and attitudes.
Big Data Analysis
EXALEAD customers are using sentiment analysis of Big Data in domains as diverse as product development and public policy, bringing unprecedented scope, accuracy and timeliness to efforts such as:
Monitoring and managing public perception of an issue, brand, organization, etc. (called "reputation monitoring")
Analyzing reception of a new or revamped service or product
Anticipating and responding to potential quality, pricing or compliance issues
Identifying nascent market growth opportunities and trends in customer demand
Democratize access to existing intelligence assets
Harness entirely new information channels for smarter, more contextual decision-making
Associate (and combine) structured and non-structured data
Unlike conventional analytics (referred to as "online analytical processing", with which one seeks to retrieve answers to precise, pre-formulated questions from an orderly, well-known universe of data), exploratory analytics are about making discoveries and uncovering possibilities as you follow your curiosity from one intriguing fact to another within largely unknown data collections (thus exploratory analytics are also called "iterative analytics"). EXALEAD CloudView is ideally suited to empowering non-specialist users to perform exploratory analytics on Big Data in both "pull" and "push" modes.
In the "pull" mode, EXALEAD's semantic mining tools are used to identify the embedded relationships, patterns and meanings in data, with visualization tools, facets (dynamic clusters and categories) and natural language queries used to explore these connections in an ad hoc manner.
In the "push" method, users can sequentially ask the data for answers to specific questions, or instruct it to perform certain operations (like sorting), to see what turns up.
It's an approach that, unlike other Big Data technologies, allows ordinary users to tap into the potential of exploratory analytics: sampling it all in, seeing what shows up, and, depending on the situation, either acting on their discoveries or relaying the information to specialists for investigation or validation.
While exploratory analytics are suitable for planning, operational analytics are ideal for action. The goal of such analytics is to deliver actionable intelligence on meaningful operational metrics in real or near-real time.
This is not easy, as many such metrics are embedded in massive streams of small-packet data produced by networked devices like "smart" utility meters, RFID readers, barcode scanners, website activity monitors and GPS tracking units. It is machine data designed for use by other machines, not humans.
Performance barriers can be overcome if such data is housed in a non-relational (NoSQL) database, but this essential usability barrier remains. In addition, batch update processes associated with most such systems introduce data latency that is incompatible with operational reporting.
Data latency and usability issues are also roadblocks for conventional data warehouses. And certain conventional relationship databases scale only with significant cost and complexity.
Now, however, organizations are leveraging CloudView to overcome technical and financial hurdles to deliver operational reporting and analytics solutions that are simple to use and massively scalable at a very low hardware cost. These solutions are also natively engineered to deliver the global, cross-silo visibility that is critical to smart operational decision-making.