Data Ingestion & Knowledge Management

Data ingestion and organization are like water: Basic building blocks of any contemporary data-driven enterprise. Data ingestion is the vital integration point where information collected from a wide range of sources — ranging from databases, IoT sensors, to social media feeds, cloud apps and more — is brought into one place (a data lake or warehouse). Streaming (or real-time) and batch are two dimensionalities under which this process takes place. The focus is on reliable data capture and processing to analysis-ready information. Siloscoop Without efficient ingestion, an organization’s data remains siloed, inconsistent and in short inaccessible for decision making.

With successful ingestion of data being a fact, knowledge management takes the lead in all these raw resources into something useful. Knowledge management (KM) is the formal process of capturing, storing, and using a company’s collective expertise. It adds context to data beyond simple storage. With metadata, taxonomies and advanced search indexing, organizations can help to make whatever is required available to the right person at exactly the time they need it. This will help promote an environment where employees are continuously learning, as well as reduce the risk of corporate amnesia after people leave.

As a pair, these two fields serve as the link between “having data” and “knowing what to do with it.” If the ingestion flows smoothly and the knowledge management is strong, we get an ecosystem that facilitates advanced analytics, machine learning, and strategic agility. Enterprises may finally transition from being responsive to the market to becoming proactive – spotting market trends and operational flaws long before they become hazards. At the end of the day, combining said processes allows an organization’s greatest asset — its data — to be used to great effect for innovation and competitive advantage.