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According to a recent Aberdeen survey, 44% of business users feel that the search and natural language processing (NLP) capabilities they have access to are “inadequate.” At the same time, many organizations are exploring NLP as a way to connect non-technical users with the data they need to support critical decisions.

The effective use of search and NLP doesn’t come about in a vacuum. Our research reveals a few supporting capabilities that help enable these technologies:

Data sharing and collaboration. One of the most visible and pressing challenges that companies face is a lack of accessibility to data across functional areas. For example, customer data residing in a CRM system may only be accessible to marketing managers. Supply chain data needed for financial analysis may only be available to operational employees.

Companies using search and NLP are more likely to have a process in place to share information across business silos.

Data preparation. As more users start to take ownership of and interest in their data, many are asking for capabilities to help them blend information from multiple sources and ensure its cleanliness and usability.

Companies using NLP recognize the need to build their search activities on a strong foundation of data through widely accessible data preparation capabilities.

Automated, predictive modeling. One of the most powerful aspects of a good search environment is the ability for the system to understand and adapt to the needs of specific users. With predictive modeling and machine learning capabilities, users are in a much better position to interact with data and make better decisions.

Figure 1: A Comprehensive Approach to Data Across the Enterprise

data_driven_approach

The impact of this more comprehensive strategy reveals itself in two key ways.

First, these companies are able to connect line-of-business users with data in a way that other others can’t. The research shows that employees at companies using search are more likely to be satisfied with the relevance of analytics to their job role, the accessibility of data, and the usability of their analytical systems.

Second, with a greater degree of user adoption, these companies are able to deliver superior business results. Companies using search and NLP are more likely to see an improvement in decision speed. They also experience a greater year-over-year improvement in organic revenue.

Conclusion

As the need for better analytics grows, so does the need for a more comprehensive approach to data.

Where it once may have been adequate to deploy pre-built, static dashboards created by IT and consumed by the line-of-business, users now need more flexibility and robust search capabilities. Along these lines, search-driven analytics and NLP can be a powerful way to support executives and other non-technical users.

However, success ultimately depends more on a broader ecosystem of capabilities than on an individual technology deployment. Those having success with NLP today focus on maintaining a clean and accessible foundation of data, while also taking advantage of emerging capabilities for data exploration.

By doing so, these companies not only empower business users with more efficient decision capabilities, they also help produce tangible business results.

Image Source (Creative Commons): Sebastian Sikora.

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