This article was originally published on Information Management.
Over the last several years, data analytics has become a driving force for organizations wanting to make informed decisions about their businesses and their customers.
With further advancements in open source analytic tools, faster storage and database performance and the advent of sensors and IoT, IDC predicts the big data analytics market is on track to become a $200 billion industry by the end of this decade.
Many organizations now understand the value of extracting relevant information from their enterprise data and using it for better decision-making, superior customer service and more efficient management. But to realize their highest potential in this space, organizations will have to evolve from being “data-driven” to being “information-driven.” While these two categories might sound similar, they’re actually quite different.
In order to make a data-driven decision, a user must somehow find the data relevant to a query and then interpret it to resolve that query. The problem with this approach is there is no way to know the completeness and accuracy of the data found in any reliable way.
Being information-driven means having all of the relevant content and data from across the enterprise intelligently and securely processed into information that is contextual to the task at hand and aligned with the user’s goals.
An information-driven approach is ideal for organizations in knowledge-intensive industries such as life sciences and finance where the number and volume of data sets are increasing and arriving from diverse sources. The approach has repeatedly proven to help research and development organizations within large pharmaceutical companies connect experts with others experts and knowledge across the organization to accelerate research, lab tests and clinical trials to be first to market with new drugs.
Or think of maintenance engineers working at an airline manufacturer trying to address questions over an unexpected test procedure result. For this, they need to know immediately the particular equipment configuration, the relevant maintenance procedures for that aircraft and whether other cases with the same anomaly are known and how they were treated. They don’t have time to “go hunting” for information. The information-driven approach draws data from multiple locations, formats and languages for a complete picture of the issue at hand.
In the recent report, “Insights-Driven Businesses Set the Pace for Global Growth,” Forrester Research notes organizations that use better data to gain business insights will create a competitive advantage for future success. They are expected to grow at an average of more than 30 percent each year, and by 2020 are predicted to take $1.8 trillion annually from their less-informed peers.
To achieve this level of insight, here are several ways to evolve into an information-driven organization.
Understand the meaning of multi-sourced data
To be information-driven, organizations must have a comprehensive view of information and understand its meaning. If it were only about fielding queries and matching on keywords, a simple indexing approach would suffice.
The best results are obtained when multiple indexes are combined, each contributing a different perspective or emphasis. Indexes are designed to work in concert to provide the best results such as a full-text index for key terms and descriptions, a structured index for metadata and a semantic index that focuses on the meaning of the information.
Maintain strong security controls and develop contextual abilities
Being information-driven also requires a tool that is enterprise-grade with strong security controls to support the complexities and multiple security layers, and contextual enrichment to learn an organization’s vernacular and language.
Capture and leverage relevant feedback from searches
As queries are performed, information is captured about the system that interacts with the end user and leveraged in all subsequent searches. This approach ensures the quality of information improves as the system learns what documents are most used and valued the most.
Connect information along topical lines
Connecting information along topical lines across all repositories allows information-driven organizations to expose and leverage their collective expertise. This is especially valuable in large organizations that are geographically distributed.
As more people are connected, the overall organization becomes more responsive in including research and development, service and support and marketing and sales as needed. Everyone has the potential to be proficient in less time as new and existing employees learn new skills and have access to the expertise to take their work to the next level.
By connecting related information across dispersed applications and repositories, employees can leverage 360-degree views and have more confidence they are getting holistic information about the topic they are interested in, whether it be a specific customer, a service that is provided, a sales opportunity or any other business entity critical to driving the business.
Leverage natural language processing
A key to connecting information is natural language processing (NLP), which performs essential functions, including automated language detection and lexical analysis for speech tagging and compound word detection.
NLP also provides the ability to automatically extract dozens of entity types, including concepts and named entities such as people, places and companies. It also enables text-mining agents integrated into the indexing engine that detects regular expressions and complex “shapes” that describe the likely meaning of specific terms and phrases and then normalizes them for use across the enterprise.
Put machine learning to work
Machine learning (ML) is becoming increasingly critical to enhancing and improving search results and relevancy. This is done during ingestion but also constantly in the background as humans interact with the system. The reason ML has become essential in recent years is that it can handle complexity beyond what’s possible with rules.
ML helps organizations become information-driven by analyzing and structuring content to both enrich and extract concepts such as entities and relationships. It can modify results through usage, incorporating human behavior into the calculation of relevance. And it can provide recommendations based what is in the content (content-based) and by examining users’ interactions (collaborative filtering).
Taking these steps will help organizations become information-driven by connecting people with the relevant information, knowledge, expertise and insights necessary to ensure positive business outcomes.