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SIX REASONS WHY ORGANIZATIONS SHOULD CONSIDER INVESTING IN SEARCH AND AI-DRIVEN
ANALYTICS

November 2, 2021

Shobhit AgrawalNovember 2, 2021

Natural Language Processing (NLP) helps computers understand informal human
language as it is typed or spoken out. The latest innovations in NLP technology
are revolutionising human-machine interactions. For decades, all data and
analytics users have been looking for easier ways to interact with data and
present insights in a simpler manner. As computers get better at understanding
natural human language, analytics applications can leverage this capability to
instantly connect decision makers with the right business data. Search and
AI-driven analytics provides a google-like experience on top of data, thus
making the use of analytics tools as easy as having a conversation with a
virtual assistant or a modern search interface.

Gartner’s* data and analytics trends signifies the importance of Natural
Language Processing (NLP) by including it amongst the top 10 data and analytics
technology trends.

Below are the top six motivations for organizations to adopt Search and
AI-driven Analytics along with traditional Business Intelligence (BI) dashboards
and reports. These motivations are based on experiences from multiple customer
success stories across industries and observations from numerous use cases at
various stages of data maturity.

 1. Improve data and analytics adoption: As global organizations focus their
    efforts on democratizing analytics for everyone, it is observed that most of
    the data initiatives are unsuccessful due to low analytics adoption and
    usage rates. High complexity of analytics tools and platforms used by most
    business teams is the primary factor for organizations failing in their
    efforts to be data driven. To overcome this, organizations are required to
    spend significant efforts on training and change management to enable users
    with the right skills.
    
    NLP features offer simplicity of use and significantly reduces or eliminates
    training efforts. With the increasing use on NLP, most of the analytics
    queries will be generated by NLP search/voice or will be automatically
    generated by the analytics tool. This boost’s analytics and BI adoption from
    35% to over 50% for all employees and business users. It also enables
    businesses to deliver analytics anywhere and to everyone in the
    organization, with less needed skills, and reduced interpretation bias than
    current manual processes.

 2. Reduce time to actionable insights: Swift decision-making in uncertain
    business scenario is another challenge where NLP techniques are helping in
    providing automated vital insights and predictions on-the-fly. Businesses
    can be better equipped to predict, prepare, and respond in a proactive
    accelerated manner.
 3. Extract value from untapped data sources: Around 80% of enterprise
    information is either unstructured or ignored by business teams. NLP
    combined with AI helps to derive value out of untapped and unstructured
    information assets like text, voice, video and images for enhanced insight
    discovery, reduced costs, and inefficiencies inherent to manual data
    collection and data entry.
 4. Enable frontline users with embedded operational insights: NLP provides
    users with dynamic data stories with more automated, intelligent, and
    customized experiences, compared to dashboards which provide point-and-click
    authoring and exploration. Implementation of NLP allows streaming of
    in-context ad-hoc analysis which provides the most relevant insights to each
    user based on their context, role, or use.
 5. Empower executive users with innovative features: Due to time constraints,
    business leaders and executives normally do not get the opportunity to
    deep-dive into dashboards. NLP allows users to analyze data and provide
    important summaries which can be delivered in the form of a newsletter, or a
    short voice note (quick narrative). Conversational features can also help to
    answer further questions by the users and provide a 360-degree view on
    request.
 6. Rationalize BI workload: There is a constant increase in total cost of
    ownership for running and maintaining Data and Analytics systems. With
    adoption of NLP, typically 50-60% of current BI workload (comprising of
    static reports and canned dashboards) can be consolidated and redirected to
    NLP features and voice guided applications. This can help significantly
    reduce several operational, infrastructure and staff costs, and accelerate
    democratized analysis of complex data.

Are you wondering what NLP use cases can add value to your organization?

InfoCepts specializes in identifying the right use cases for search-driven
analytics and implementing them using industry leading tools.

Get in touch to know more!

References

* Gartner Article: ‘Gartner Top 10 Trends in Data and Analytics for 2020’, by
Laurence Goasduff, 19 October 2020 –
https://www.gartner.com/smarterwithgartner/gartner-top-10-trends-in-data-and-analytics-for-2020

November 2, 2021/

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405 930 Shobhit Agrawal
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Agrawal2021-11-02 06:19:232021-12-31 10:29:33Six Reasons Why Organizations
Should Consider Investing in Search and AI-Driven Analytics



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