Getting Started with Watson Explorer Engine
Delivering and Building Big Data Value
Watson Explorer Engine is a robust application layer upon which a company may build an infrastructure of easily accessible information and an inter-network of shared knowledge. As even modest corporations gather a foundation of data and big data, the threat of the data becoming overwhelming and therefore under-utilized is great. It is the goal of Watson Explorer Engine to optimize the use of this information to drive value for the organization.
A business's information is valuable to many units within the organization itself, each of which may have differing requirements for content, presentation, and security. Without having an infrastructure managing that information, the task of putting it to use may be insurmountable. With a reliable big data delivery solution like Watson Explorer Engine in place, digital assets of all kinds can be found at a moment's notice and used and re-used for any purpose.
This is the value of big data. Big data value only results when companies make economically optimized use of all the structured and unstructured data already residing in existing systems across the enterprise to become better at discovering opportunities, selling, innovating, delighting customers, improving productivity, cutting costs, mitigating risk, and more.
Watson Explorer Engine enables you to tap the value of big data. With the right big data solution, companies can quickly deliver an entirely new class of actionable, high-value business insight to decision-makers and front-line employees across the organization. Equipped with this insight, companies can substantially improve business performance no matter what the business climate. Companies with differentiated big data strategies will be in a better position than their competitors to take full advantage of market expansion, emerging market opportunities, and potential market opportutnies revealed, all enabled and supported by Watson Explorer Engine. Let's get started.
Benefits of the Watson Explorer Engine Platform
To use big data effectively, people must be able to access it and derive intelligence. So any successful big data optimization solution must include a robust set of big data access mechanisms. Search is a core element of this essential data access. Users must be able to find relevant data regardless of where it resides and what form it is in. And they must be able to this on an ad hoc basis using common language, navigate to information to promote serendipitous learning and utilize advance query logic that are associated with traditional search engines. Traditional search solutions, however, are typically insufficient to provide a strong data access foundation for big data discovery and optimization as they lack broad connectivity, contextual insight, and strong organization capabilities. Other key aspects of big data access that help contribute to effective big data optimization include:
By delivering information to the right people at the right time, IBM Watson Explorer ensures that organizations gain the full value of their data, enabling improved operations, real-time decisions, better understanding of customers, increased innovation and actionable insight.
- Query routed navigation and discovery - across a broad range of applications, data sources and file types. Includes access to enterprise applications such as content management systems, customer relationship management systems, supply chain management, e-mail systems, relational database management systems and more, as well as web pages and networked file systems
- Access to non-indexed systems - such as premium information services, supplier and partner portals and catalogs, and legacy applications. Results from these non-indexed sources can be merged with results from the Watson Explorer Engine index or kept separate
- Highly relevant navigation results - for precise discovery against both large and small structured and unstructured data sets. Relevance model accommodates diverse document sizes and formats while delivering consistent results
- Sophisticated security model - including cross-domain and field-level security to ensure that users only see content they would be able to view if logged into the source application
- Rich analytics - including clustering, categorization, entity and metadata extraction, faceted navigation, conceptual search, name matching and document de-duplication
- Collaboration capabilities - to enable users to tag, comment, organize and rate content to enhance navigation and discovery results for other users
- Highly elastic, fully distributed architecture - offering fault-tolerance, vertical and horizontal scalability, master-master replication and shared deployment
In addition to providing users with ready access to any and all non-confidential data relevant to their needs, effective use of big data requires an applied understanding of the business context of each data inquiry.
Context may be related to an information taxonomy - for example, that a given model is associated with a particular product line and that the product line is associated with a particular line of business. Context can also be associated with a specific set of data sources, such as "all email attachments sent out by the sales department in the last two weeks of the quarter."
Context adds substantial insight and business value to data. For example, a transactional system will indicate that sales in a given territory spiked suddenly last month. With access to the right data properly contextualized, however, a sales manager might quickly realize that a major competitor faced a major legal problem in that same territory - and that this may have been a contributing factor to the spike in sales volume.
Your big data solution is therefore not just a matter of indexing and searching data. It is also about placing that data in the context of the business problems and objectives that users are trying to address on a daily basis.