Enterprises are innovating for the digital economy and data has become the fulcrum of innovation. Data centric innovations can deliver a wide range of business benefits - customer satisfaction, productivity, cost control, dynamic and personalized products and services and discovery of business insights. When such a fundamental shift is happening with data, there seems to be a contrast in the way information solutions are built within the enterprise and on the internet which is the nervous system of the digital economy.
The technologies of internet search engines and enterprise knowledge systems are creating breakthrough applications and thus driving innovations for dynamic search within the enterprise. The needs of an enterprise for the digital economy is to provide the right information on demand to people playing varied roles in the enterprise and answer questions with speed and agility. The technologies and architectures would need to break the data silos, understand the context of the business domain and the business processes that generate the data and deliver this through thin line interfaces like the internet search engines. One can map this need to four key principles: discovery, find-ability, understanding, and dialogues. The intersection of these would give the enterprise areas to innovate for the digital economy.
Let’s look at these 4 principles in detail and see how they aid to creating a scalable and an intuitive information management system.
- Discovery: Data virtualization technologies such as Apache TEIID create a single fabric for diverse data sources, offering applications and users access to organizational data, while providing for data security. In this virtual environment, data repositories and warehouses can be added dynamically and information accessed on demand from multiple data sources, enabling information discovery.
- Find-ability: This covers the extent to which an information management system supports data navigation and retrieval, while enabling intelligence augmentation through knowledge models associated with the data.
- Understanding: Understanding is about the contextual meaning, reasoning and making inferences to provide unambiguous results. Semantic technology offers a meaning-centric approach. Semantic search improves accuracy and relevance by considering the intent behind a query and the contextual significance of the words in the search.
- Dialogues: Most users have questions that need precise answers. The need perhaps is for an answer engine rather than a search engine. Here, Natural Language Processing (NLP) can improve the quality of an information system’s response by offering a more complete understanding of the user’s question.
The enterprise knowledge systems designed around these four key principles will be driven by tools for data virtualization, semantic search, ontologies, and natural language processing. These systems will provide an abstraction layer, above existing data management technologies, enabling enterprises to bridge the gap that separates data, content, and processes. This offers a more intelligent, relevant, and responsive interaction than the deployment of standalone information technologies in isolation.
I believe this new-age digital information architecture will fundamentally disrupt how enterprises deal with information, democratizing data, augmenting intelligence and providing understanding and insights. What are your views? Do write in.