Best Practices for Intelligent Search
Sinequa provides an intelligent search platform that enables organizations to become information-driven, which means having actionable information presented in context to surface insights, inform decisions, and elevate productivity, consistently and reliably. Our platform consists of packaged technology that allows this to happen quickly and without sacrificing context or quality as typically happens with “lossy” approaches involving data migration.
Let’s explore some of the best practices for becoming information-driven using intelligent search by leveraging the experience Sinequa has gained working with large customers within knowledge-intensive industries.
As we all know, digital assets have emerged as a driving force for many types of organizations. This represents a huge opportunity for these organizations, as they come to understand the value of extracting relevant information out of their digital assets for the purposes of better decision making, superior customer service, accelerated innovation, more effective management, and significantly improved performance overall.
For many of these organizations, especially in knowledge-intensive industries, the number and volume of digital assets that they regularly depend on are increasing quickly, and there are many case studies on so-called “big data” that prove the benefit. For example, consider the following IDC prediction:
“By 2020, organizations able to analyze all relevant data and deliver actionable information will achieve an extra $430 billion in productivity benefits over their less analytically oriented peers.”1
The prediction describes a stark gap, less than two years from now, quantified in financial terms between the organizations that will have chosen explicitly to leverage their enterprise data as a core part of their business versus everyone else. At the same time, as the enterprise digital landscape gets bigger, more diverse and more prominent, executives start to expect line-of-business (non-technical) staff to be able to understand and act on insights extracted from complex, distinct, and often hybrid (structured/unstructured) digital sources.
In the worst cases, the expectation for these folks to understand and execute tasks that were traditionally given to business analysts is prompting a decline in the quality of decisions due to the skills gap between experienced business analysts and regular line-of-business employees. This dynamic is compounded by the often-erroneous assumption that these line-of-business staff have clear visibility of what data/information/knowledge is available that is relevant to the issue at hand.
Traditionally, humans had to be computer literate to adapt to working in the “computer space” to gain all the advantages that computing technology had to offer. Increasingly, and especially in recent years, the advent of intelligent “AI-powered” search has started to reverse this by providing a means for computing power to be leveraged in the “human space.” Think here of natural language processing, which enables computers to interpret human language and helps to decode meaning, or think of machine learning algorithms that can give computers the ability to “learn” with data, without being explicitly programmed.
In the best cases, these technologies are quickly becoming a critical means for knowledge workers to obtain actionable information to:
♦ Find patterns and relationships among disparate data from different silos
♦ Surface experts within the organization based on evidence within content
♦ Present topically relevant information from disparate sources in a unified view
♦ Discover what information and insights exist within your enterprise data
Invest in Proven Technology
Organizations seeking to become information-driven with intelligent search need the right technology from the right vendor to help their people. They need proven technology that can handle a critical project while continually accommodating additional projects as the organization grows and/or begins to realize the deep benefits of being information-driven.
To avoid the pains and inefficiency that accompany a trial and error approach, such a solution should have been forged by experience with other organizations with similar goals and complex projects in heterogeneous environments full of large and diverse content and data volumes. Specifically, the solution should provide hardened capabilities such as enterprise grade security, extensive linguistic capabilities, integrated machine learning, and a well-designed user experience.
Relatedly, the vendor providing the solution should employ an agile development approach to evolving their product. This allows them to learn from and capitalize on real-world experience to iteratively and empirically build an ever-stronger platform.
Adopt a Complete Solution
Not all proven solutions are complete. Organizations seeking to become information-driven with intelligent search want to realize value quickly and are looking for comprehensive, packaged technology that helps them move quickly without sacrificing context or quality.
By adopting a unified platform, these organizations shield themselves from the cost and complexity of IT integration. They also benefit from a development paradigm that enables implementation and administration through configuration, scalable to even the largest and most heterogeneous environments.
Adopting a complete solution means these organizations don’t have to purchase a point solution whenever new business requirements arise. Instead, they can leverage a unified, end-to-end platform to configure applications quickly that are specifically aligned with business goals.
These are some of the best practices for becoming information-driven using intelligent search, which Sinequa has observed working with large customers within knowledge-intensive industries. At Sinequa, we believe these practices, intelligently applied, serve as the primary enablers for organizations seeking to become information-driven.
1. IDC FutureScape: Worldwide Analytics, Cognitive/AI, and Big Data 2017 Predictions