Businesses generate around 7 septillion megabytes of data per day, but more than half of it is never monetized in any way (Splunk). Unseen connections in these data points could potentially identify what makes a company run smoothly, but finding a needle in a haystack is rarely the kind of time investment that businesses can make.
Disorganization of the Enterprise
“Using predictive AI and knowledge graphs is not about finding a needle in the haystack; it’s learning there’s a needle in the haystack. Graph and AI can be predictive in not only discovering something but quantifying it, with precision and accuracy.” - John Rueter, Head of Marketing, Katana Graph (KMWorld, 2021)
Conventional searching of a database relies on understanding its schema and knowing identifiers for the information being sought, but attempts to make databases more searchable frequently result in a spider web data model or excessive denormalization. Similarly, attempts to provide for querying unstructured data often result in strict filing rules and convolution while the data remains impenetrable.
With intelligent search incorporated into the operation, users need not be familiar with a system’s schema or storage system to be able to find what they need.
In Search of Intelligent Search
Traditional search requires thinking like a database and forces the user to restrict lines of inquiry to meet a system’s parameters, whether that be relational database processes or questions applicable to simple text indexing. With intelligent search incorporated into the operation, users need not be familiar with a system’s schema or storage system to be able to find what they need.
Intelligent search is context-aware; it uncovers insights specific to the needs of the organization. It is also capable of being conversational, digesting natural human language and learning via the user’s speech. Through machine learning techniques, intelligent search can improve the relevance of results over time, even understanding document structures to differentiate header, body, references, and footnotes, and process the content accordingly.
“Conversational [search] means the user either types in a natural language query or speaks to the computer to get answers. Cognitive [search] uses knowledge about the user and the word to return better results.” Keshav Pingali, CEO, and co-founder of Katana Graph (KMWorld, 2021)
Intelligent search eliminates data discovery challenges created by unruly data systems and processes massive amounts of unstructured data. Tapping into the value of this pre-existing but messy data enables those searching for information to quickly and easily find answers and insights that generate ideas, facilitate growth, and keep businesses running smoothly.
Whether you’re using structured data, unstructured data, or both, Katana Graph can greatly improve the insights and opportunities uncovered from your organization’s data. Click to schedule a meeting.
