Tag: Natural Language Processing

White Paper – Conversational AI and NLP Analytics Reduces the Dependence and Usage of Traditional BI Tools, and Improves User Adoption and Data Democratization

author_img
by |
January 7, 2026
White Paper – Conversational AI and NLP Analytics Reduces the Dependence and Usage of Traditional BI Tools, and Improves User Adoption and Data Democratization!

Conversational AI and NLP Analytics
Reduces the Dependence and Usage of Traditional BI Tools, and Improves User Adoption and Data Democratization

The incorporation of Artificial Intelligence (AI) and Natural Language processing (NLP) in existing business intelligence and self-serve analytics tools has had (and will continue to have) a profound influence on ease-of-use, on user adoption and on the democratization of data across the enterprise, and the use of Conversational AI and NLP is rapidly changing the face of BI tools and business user and organizational expectations.

Download White Paper

What Are Citizen Data Scientists Doing Today?

author_img
by |
October 29, 2025

How Has the Citizen Data Scientist Role Evolved?

Ten years ago, the term ‘Citizen Data Scientist’ was coined by the world-renowned technology research firm, Gartner. The term refers to business team members whose expertise and role are not focused on analytics as a primary job function. Using self-serve analytics solutions, these team members can leverage analytics to create models, reports and analysis to collaborate, share and make decisions. Gartner predicted the emergence of this role within businesses as part of the growing importance of data analytics and data-driven decisions within the business environment.

A decade later, it is worth reviewing the status of this role in the business enterprise and within the average organization. Is the Citizen Data Scientist role a standard role within most businesses today? Does a Citizen Data Scientist replace or work independently from a Data Scientist or Business Analyst? Has the Gartner prediction come to fruition?

While there are no current statistics regarding the number of companies currently using a Citizen Data Scientist approach, the trend toward data-driven planning and forecasting is clear. As with many other business trends, the larger organizations usually take the lead. They have the budget and the depth of resources to plan for and deploy changes across the enterprise and to test theories and enforce cultural changes.

Here are some statistics that reflect the growth of the Citizen Data Scientist movement and the supporting technologies that engender this approach:

After Ten Years, Is the Promise of Citizen Data Scientists Fulfilled?
  • Studies reveal that the number of Citizen Data Scientists is growing five times faster than the number of Data Scientists.
  • Automation technologies support the growth of the Citizen Data Scientist approach with over 40% of data science tasks automated through augmented analytics and/or machine learning.
  • The Machine Learning (ML) market is growing at a compounded annual rate of more than 15%, reflecting the need for data analytics capabilities within self-serve solutions.
  • By some estimates, interest in the Citizen Data Scientist role has tripled in the past decade, as medium and small enterprises embrace new, intuitive, more affordable technologies to support the Citizen Data Scientist concept within their organization.

As this concept became mainstream, the industries saw a trend toward increasing data-driven insight while reducing dependence on Data Scientists.

While the Citizen Data Scientist role began as a basic initiative to gather data and create simple reports, today’s Citizen Data Scientists are now using business intelligence (BI) tools and augmented analytics with Natural Language Processing (NLP), machine learning, low-code and no-code platforms and other technologies to leverage limited technical skills and create sophisticated analytics with clear results. Reports, dashboards and data sharing allow team members to create and use data models and to increase data literacy and data democratization.

Team members can use smart data visualization and assisted predictive modeling to gain insight and solve day-to-day problems, advise management and collaborate with other team members to understand trends, patterns, challenges, and opportunities and leverage metrics to make fact-based decisions.

This evolution of the Citizen Data Scientist role within the organization can free Data Scientists to perform more strategic activities without the daily distraction of simple report requests. If and when a particular data model or analytical approach must be refined to be more strategic, the Citizen Data Scientist can work with the Data Scientist to achieve that goal.

Using this approach, the enterprise can empower team members with the tools to analyze data and to use their knowledge of the industry, market, customers and business environment to make decisions and improve results.

When we consider the last decade of Citizen Data Scientist evolution, we see that businesses across all industries are working toward a more data-driven approach to decision-making, and embracing data democracy as a means to improve productivity and the quality of decisions and to reduce re-work and missteps.

Contact Us to discuss your analytical needs and to find out more about Citizen Data Scientists, and the process of choosing the right Analytics Solution for your business. Explore our free White Papers: ‘The Potential Of The Citizen Data Scientist Approach And Augmented Analytics,’ ‘Enabling Business Optimization And Expense Reduction Through The Use Of Augmented Analytics,’ and ‘A Roadmap To ROI And User Adoption Of Augmented Analytics And BI Tools.’

Original Post : What Are Citizen Data Scientists Doing Today?

Citizen Data Scientists Are Important to Business Transformation

author_img
by |
October 7, 2025

Transform Your Business with Citizen Data Scientists

The business environment today is competitive. Whether your business is global or local, you are challenged to do more with less, to set and achieve goals more quickly and to stay ahead of your competitors by gaining a comprehensive understanding of what your customers want, what they WILL want, and how to best attract their attention and retain them.

To meet these challenges, every team member and employee must have a thorough understanding of how their roles and responsibilities fit into the grand scheme of things and how the projects, tasks and activities they pursue on a day-to-day basis will affect revenue, outcomes and results.

When a business (large or small) makes the decision to transition business users to Citizen Data Scientists, it supports the alignment of goals and objectives with fact-based decision-making and improved data literacy, encouraging its users to embrace and understand data and use that data to collaborate, present information to management and gain insight into results to identify opportunities and address issues.

‘The Citizen Data Scientist approach transforms the organization by improving time to market, reducing rework and mitigating market missteps and improving productivity and the alignment of workflow and tasks with the goals and objectives of the enterprise.’

World renowned technology research firm Gartner first coined the term ‘Citizen Data Scientist’ in 2016 and defined the role as ‘a person who creates or generates models that leverage predictive or prescriptive analytics, but whose primary job function is outside of the field of statistics and analytics.’

Nearly a decade later, the role has been refined and structured within many organizations and recent Gartner research reports that, ‘Citizen Data Scientists can be leveraged to perform repetitive and redundant tasks in the analytics workflow, and therefore create value to the organization, while allowing expert data scientists to focus on more complex tasks.’

The increased pace and tenor of competition has forced businesses to accommodate rapid change in markets and customer buying behavior by using analytics, data scientists and business analysts to work with IT and create reports and presentations to be used for decisions. But there just aren’t enough professional resources of funds to support this approach. Hence, the evolution of Citizen Data Scientists.

Transform Business Users Into Citizen Data Scientists AND Transform Your Business

The evolution of self-serve augmented analytics tools and technologies like natural language processing (NLP) and NLP search, machine learning, flexible data visualization, and artificial intelligence (AI) provide support for business users without technical skills to gather and analyze data and produce reports, collaborate with other users and make recommendations using insight derived from advanced analytics. And this approach supports data democratization and data literacy.

The Citizen Data Scientist approach also transforms the organization by allowing business users to interact with and collaborate with IT and data scientists to take day-to-day data analytics and translate them into strategic initiatives with measurable results, accurate predictions and rapid flexible processes.

The Citizen Data Scientist approach transforms the organization by improving time to market, reducing rework and mitigating market missteps and improving productivity and the alignment of workflow and tasks with the goals and objectives of the enterprise. It provides a career path for business users and advances their knowledge and skills, allowing them to understand how their role directly influences results and to create and innovate.

‘When a business (large or small) makes the decision to transition business users to Citizen Data Scientists, it supports the alignment of goals and objectives with fact-based decision-making and improved data literacy, encouraging its users to embrace and understand data.’

To plan for and execute a Citizen Data Scientist initiative, the organization must engage an expert in augmented analytics and develop a comprehensive understanding of technology and cultural changes in order to advance this new idea within the ranks of IT, data scientists, business users, managers and executives.

Learn more about how the transformation of business users to Citizen Data Scientists can benefit your business, and how technology and appropriate Self-Serve Analytics Tools can support Citizen Data Scientists in their new role, and provide fact-based decision-making and advantages to the organization. Explore our free white papers and articles: ‘The Potential Of The Citizen Data Scientist Approach And Augmented Analytics,’ and ‘Leverage Citizen Data Scientists For Business And Business Users.’

Original Post : Citizen Data Scientists Are Important to Business Transformation!

Data Insights Assure Quality Data and Confident Decisions!

author_img
by |
November 26, 2024

Why is Data Insight So Important?

Every business (large or small) creates and depends upon data. One hundred years ago, businesses looked to leaders and experts to strategize and to create operational goals. Decisions were based on opinion, guesswork and a complicated mixture of notes and records reflecting historical results that might or might not be relevant to the future.

Today, organizations look to data and to technology to help them understand historical results, and predict the future needs of the enterprise to manage everything from suppliers and supplies to new locations, new products and services, hiring, training and investments. But too much data can also create issues. If the data is not easily gathered, managed and analyzed, it can overwhelm and complicate decision-makers.

‘Data insight techniques provide a comprehensive set of tools, data analysis and quality assurance features to allow users to identify errors, enhance data quality, and boost productivity.’

By some estimates, bad data costs global organizations more than five trillion USD annually.

Use Data Insight Techniques and Data Quality Management and Analytics to Achieve Better Results

Data insight takes data to the next level by providing comprehensive data analysis and quality assurance features that empower business analysts and users to quickly and easily identify errors, enhance data quality, and boost productivity. The business can harness the power of statistics and machine learning to uncover those crucial nuggets of information that drive effective decision, and to improve the overall quality of data.

By incorporating machine learning, natural language processing and automation within advanced analytics solutions, the enterprise can improve results and support its team with augmented analytics that are designed as self-serve solutions for business users, so the team can gather and analyze information with ensured, sustained data quality and results that are clear and concise. When an analytics solution is built upon this foundation, with advanced tools and techniques to support users, the enterprise can ensure user adoption and positive outcomes. Users do not have to learn complex systems or look to data scientists or business analysts for answers.

Tools that support data insight include numerous data quality management techniques. These tools allow users to see and work with datasets in a way that is targeted and provides clear, actionable information for decisions and strategies.

Overview – Reveals the data quality index in percentage representing the quality level of data. It shows the quality of the dataset and number of columns with listing down the missing values, duplicates, and measure and dimension columns.

Observations – Highlights all detected inconsistencies and anomalies within your dataset, along with the corresponding column names. By clicking on a column name, you can access detailed information about the observation for that particular column and view recommendations for fixing the issue.

Column Analysis – Shows the details related to all the columns in the dataset. It categorizes the columns by their types and shows Sample values, Missing Values, Most frequent values, least frequent values, Unique values and Quality index of that column.

Column Associations – Shows the pairs associations between all columns which helps you to understand the relationship with each other. The degree of association can be determined by the index value, and higher the index indicating a stronger relationship between columns.

Feature Importance – Automatically identifies and displays the target variable along with its key predictors from your dataset. It also shows the influence of each predictor on the target. This helps you select the predictors that have the greatest impact, making it easier to create an effective predictive model.

Missing Value Analysis – Shows the analysis of the missing values across all the columns of the dataset at a glance. The graph visually represents both non-missing (non-null) values and missing (null) values, allowing you to quickly identify which columns have incomplete data.

Column Metadata – Provides information on the dataset’s recency, such as the last update and publication dates. It will also talk about the details like Datatype, Column Type and respective Sample Value of the columns in dataset.

Settings – Customize the data insights computing process for datasets to lower the load and processing time.

Data insight and Data Quality Management tools and techniques provide a comprehensive set of tools, data analysis and quality assurance features to allow users to identify errors, enhance data quality, and boost productivity. Users can uncover hidden insights and improve the overall quality of data with actionable recommendations to take prompt action.

‘By some estimates, bad data costs global organizations more than five trillion USD annually.’

To find out more about Natural Language Processing (NLP), Machine Learning And NLP Search Analytics, and comprehensive data quality management and Data Insight Tools, Contact Us. Discover the power of Augmented Analytics, machine learning, and Natural Language Processing (NLP). Read our free article, ‘Why Is Natural Language Processing Important To Enterprise Analytics?’.

Original Post: Data Insights Assure Quality Data and Confident Decisions!

Give Business Users NLP Search Analytics and Get Results!

author_img
by |
October 24, 2024

NLP Search Analytics Ensures User Adoption

These days, most people understand the term Natural Language Processing (NLP). It has been around a while, and represents perhaps the most significant information tool in the past century.

Machine Learning and Natural Language Processing (NLP) have unlocked a vast library of knowledge, making it accessible to the average person, requiring no significant technical skills, and leveling the playing field for millions of people, seeking to learn and understand the world.

‘NLP search technology significantly simplifies the user experience and encourages team members to learn and incorporate augmented analytics into their daily activities.’

Just a few years ago, Gartner predicted that, ‘50% of analytical queries will be generated via search, NLP or voice, or will be automatically generated.’ Today, this prediction is a reality.

When an enterprise wishes to implement augmented analytics and business intelligence, and make these tools available to its business user community, it must select a solution that uses natural language processing (NLP) search capabilities to allow business users with average technical skills to gather and analyze data and achieve results. Without these simple tools, the enterprise cannot ensure user adoption of the solution.

Natural Language Processing Search Analytics (NLP) is crucial component to search analytics in that it allows business users to perform complex searches without endless clicks, coded queries, or complex navigation and commands. Users can access and view clear, concise answers and analysis quickly and easily, leveraging a familiar Google-type interface to compose and enter a question using common language.

Natural Language Processing and NLP Search Analytics Give Business Users True Access to Analytics

When you choose Augmented Analytics with machine learning and natural language processing (NLP), your users can enjoy a self-serve environment that is easy and intuitive, and will increase user adoption, data democratization, and return on investment (ROI).

NLP search technology significantly simplifies the user experience and encourages team members to learn and incorporate augmented analytics into their daily activities. Finding information is easy! Let’s suppose a team member wants to understand the trends in regional bakery sales. With NLP, the user can simply ask, ‘how many bakery products were sold in the Southwest and Southeast regions in 2023?’

Natural Language Processing (NLP) and search capability allows users to avoid scrolling through menus and navigation. The user only has to enter a simply worded search query, and the system will translate the query, and return the results in natural language using an appropriate form, e.g., visualization, tables, numbers or descriptions. There is no advanced training required. Users can analyze data and receive results in a way that is meaningful to them.

The benefits of augmented analytics using natural language processing (NLP) enable swift, easy searching and allows business users to create context-rich searches that provide in-depth information and concise results and can be used to solve problems, identify opportunities, spot trends and patterns and present data and recommendations. There is no need to request reports or information from IT, business analysts or data scientists. The business user has the tools and the capability to get results when and how they need the information.

‘Just a few years ago, Gartner predicted that, ‘50% of analytical queries will be generated via search, NLP or voice, or will be automatically generated.’ Today, this prediction is a reality.’

To find out more about Natural Language Processing (NLP), Machine Learning and NLP Search Analytics, Contact Us. Discover the power of Augmented Analytics, Machine Learning, and Natural Language Processing (NLP). Read our free article, ‘Why is Natural Language Processing Important to Enterprise Analytics?’

Original Post: Give Business Users NLP Search Analytics and Get Results!

Choose Augmented Analytics Designed for Business Users!

author_img
by |
September 11, 2024

Avoid Complex Analytics Solutions (Your Users Will Hate)

When a business is considering a business intelligence or analytics solution, it is important to recognize that today’s solutions are very different than the solutions of the past. Not only do they include more analytical techniques and features, but they have come a long way in providing access to sophisticated analytics for the average enterprise team member.

Harvard Business Review Analytics Service reports that

a) businesses can substantially improve business performance by giving frontline workers modern self-service analytics tools to enable fast intelligent action and,

b) not all self-service analytics provide this effective approach.

Choose Augmented Analytics Designed for Business Users and Get the Most From Your Solution

The Harvard Business Review Analytics Service surveyed nearly 500 executives and found that they reported significant performance improvement when they empowered frontline workers with augmented analytics. More than one-third of those surveyed noted improvement in customer and employee engagement and in product and service quality.

While some businesses may still be using business intelligence and analytics that are designed for data scientists and IT professionals, most of those are actively working to upgrade and/or migrate to augmented analytics and solutions that are designed for self-serve business user access.

Here’s why:

  • Search-based, self-serve analytics provides swift access to data and familiar natural language processing (NLP) search capability so business users can ask a question, get an answer and drill down to discover the root cause of issues. There is no need for the user to wait for IT or a data scientist to produce a report. They can continue to work on a task or a problem with full insight into results, challenges and possibilities.
  • The enterprise can enable data democratization and data literacy across the business landscape, thereby ensuring that there is a rapid response to market and competitive changes and to changing customer buying behavior.
  • Business users can leverage their industry knowledge and functional skillset and combine data insight with experience to produce the best results.
  • Intuitive, easy-to-use solutions help to combat user resistance and ensure user adoption. While there are always cultural issues surrounding this type of adoption and the perceived changes in responsibilities, when business users see the value of having crucial information at their fingertips, the enterprise can ease the transition and ensure user adoption.
  • No matter the role of the user, the team can enjoy the benefits of augmented analytics and make the transition to Citizen Data Scientists to improve collaboration, data sharing and fact-based decision-making.
  • The business can understand quality and maintenance issues, refine customer targeting and marketing optimization, and make appropriate financial investments, and they can analyze trends and patterns and make forecasts and predictions.
  • When the enterprise adopts these tools and techniques, they allow Citizen Data Scientists to perform analytics on a day-to-day basis and, where appropriate to effectively interact with and collaborate with the IT team and data scientists to refine data and prepare it for more strategic initiatives, so there is a seamless handoff from the business user to the analytical community, when and as necessary.

When the business is ready to acquire augmented analytics or to upgrade from existing, more restrictive solutions designed for professional analytical resources, it is important to choose the right solution – one with sophisticated tools that are presented in an intuitive user interface with auto-suggestions and recommendations to assist business users, and ample personalization of dashboards and reports.

With the right IT consulting partner, you can select and implement an Augmented Analytics Solution with business intelligence (BI) and advanced capabilities, and ensure that every user can leverage these tools, no matter their skillset or technical capabilities. Explore our free white paper, ‘A Roadmap To ROI And User Adoption Of Augmented Analytics And BI Tools.’

Original Post: Choose Augmented Analytics Designed for Business Users!

Natural Language Processing Analytics for Business Users!

author_img
by |
June 4, 2024

Clickless Analytics in Augmented Analytics Solution Supports Users with Simple Searches and Results!

Every consumer and business user loves the new world of search and query. Google-type searches offer the ability to ask a question in simple form, and receive an answer you can understand. You don’t have to be a data scientist, a rocket scientist, a statistician or a data guru to perform the search or to understand the results!

Continue reading “Natural Language Processing Analytics for Business Users!”

White Paper – Enabling Business Optimization and Expense Reduction Through the Use of Augmented Analytics

author_img
by |
May 9, 2024
White Paper – Remote and Hybrid Technology Outsourcing Models Are Now Mainstream and the Future is Bright!

white-paper-enabling-business-optimization-and-expense-reduction-through-the-use-of-augmented-analytics

No matter the reason or the goal, when an enterprise chooses the right Augmented Analytics solution and carefully plans for and executes its implementation, it can optimize business results, reduce expenses and improve its market position, customer satisfaction and user adoption, and it is key to transforming business users to Citizen Data Scientists to improve results and team skills. Here, we examine the benefits of Augmented Analytics and how to plan and successfully execute an Augmented Analytics initiative.

Download White Paper

Augmented Analytics Must Provide Data Quality and Insight!

author_img
by |
April 25, 2024

How Can I Ensure Data Quality and Gain Data Insight Using Augmented Analytics?

There are many business issues surrounding the use of data to make decisions. One such issue is the inability of an organization to gather and analyze data. These enterprises will typically focus on building a team of data scientists or business analysts to help with this task OR they might take on an augmented analytics initiative to provide access to data and analytics for their business users. This is where businesses will often face a second issue; namely that the analytics solution they choose is not designed to easily and quickly provide insight into data and to ensure data quality.

Continue reading “Augmented Analytics Must Provide Data Quality and Insight!”

AI In Analytics: Today and Tomorrow!

author_img
by |
April 19, 2024

Nothing…and I DO mean NOTHING…is more prominent in technology buzz today than Artificial Intelligence (AI). The use of Generative AI, LLM and products such as ChatGPT capabilities has been applied to all kinds of industries, from publishing and research to targeted marketing and healthcare. Gartner recently estimated that the market for AI software will be nearly $134.8 billion, with the market growing by 31.1% in next several years. In a recent survey of C-suite executives, 80% of said they believe AI will transform their organizations, and 64% said it is the most transformational technology in a generation.

(more…) "AI In Analytics: Today and Tomorrow!"