Tag: NLP Search
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:
- 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?
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.
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.’
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.
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!
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.
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!
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!
Why Should My Enterprise Provide Mobile Business Intelligence (BI) Tools to Business Users?
Many businesses are beginning to see and leverage the value of business intelligence and augmented analytics within the organization to engender data democratization, improve data literacy and collaboration and improve results. In a competitive global and local business environment, this move toward analytics for all is a positive thing.
If your business is considering BI tools or augmented analytics, or if you have already implemented these tools within the enterprise, there is one more question to answer.
Does your organization provide Mobile BI to its business users? If it does not, you are missing out on a crucial opportunity and a critical business advantage.
‘Mobile augmented analytics can benefit your business users and help to transform them into Citizen Data Scientists.’
When considering a Mobile BI app, it is important to verify ease of access and availability of data on the road and anywhere your team may be working, and to ensure that your team has tools that are easy-to-use and will enable user adoption.
If you select the right mobile business intelligence (BI) app, you can enjoy the following benefits:
- A native app, with seamless user interface for a great user experience (Ux). Available for iOS and Android
- Extends the office environment. Allows swift analysis and decisions from anywhere
- Encourages user adoption and provides support for BI investments and data democratization
- Access rights are defined on the server so security and privacy is ensured at all levels
- Supported by a server that is hosted anywhere within IT infrastructure –on premises, public or private cloud
- Business Users will have access to dashboards, reports, Clickless Analytics – Google-type Natural Language Processing (NLP) Search functionality
- Start-up is easy, within a few minutes and support is readily available
The right Mobile BI App should include the following components and features:
- Provide access to Dashboards, Graphs, KPIs, PDF reports, and Clickless Analytics – NLP Search – all from within the mobile app.
- Users login with an enterprise login, browse and search objects, and access objects for interactive analysis with access to ‘my favorites’ for quick navigation, i.e., objects marked as favorites, and recently used objects.
- Download Smarten objects via PDF, XLS, and other file formats and share via WhatsApp, email or other sharing app loaded on their device.
- Natural Language Processing (NLP) search, where users can query a dataset with a Google-type interface where a user can compose a question using natural English language and receive answers using visualization that provides the best-fit for the user inquiry. With natural language-processing-based search capability, users do not need to scroll through menus and navigation. Enter a search query in natural language and the system will translate the query, and return the results in natural language in an appropriate form, such as visualization, tables, numbers or descriptions.
- No requirement to redesign or customize dashboards and reports to accommodate a mobile device. Dashboards and Reports are displayed and optimized for the best user experience on the mobile device.
- Licensing fees are affordable and implementation is easy and fast.
‘When considering a Mobile BI app, it is important to verify ease of access and availability of data on the road and anywhere your team may be working, and to ensure that your team has tools that are easy-to-use and will enable user adoption.’
Find out how Mobile BI and mobile augmented analytics can benefit your business users and help to transform them into Citizen Data Scientists, and how it can provide an advantage to your data scientists, business analysts and IT team members.
Original Post: Give Your Business Users Mobile BI to Ensure Success!
Select the Right BI Tools and Succeed with Data Democratization!
Do you know what Data Democratization is? It’s simple, really. Data Democratization is the purposeful approach to cascading and integrating data into the daily workflow of business users to provide access to crucial information and the tools to analyze and understand that data and use it to make confident decisions. Instead of holding data in silos that are only accessible to IT, business analysts, data scientists and management, the enterprise recognizes the value of providing team members with the right information to do their job and contribute to the bottom line.
‘To succeed in data democratization, you need BI tools that provide data analytics access for all business users.’
Gartner predicts that, ‘75% of organizations will…deploy…multiple data hubs to drive mission-critical data and analytics sharing and governance.’ The key here is the ‘analytics sharing’ piece of the statement!
In order to fulfill the promise of this approach, your enterprise must employ business intelligence solutions that are easy-to-use and designed for business users, without advanced technical skills or advanced analytical skills. These tools allow your team members to engage in analytics and enjoy data democratization without the frustration of leveraging solutions designed for data scientists or IT staff.
Here are a few considerations to give you an idea of the kinds of things you will need to support your data democratization initiative. These factors are crucial to success, as they ensure that your users can and will adopt the BI tools you select to enjoy the new data access you have given them. Without these, you run the risk of spending the time and money to provide access and achieving poor return on investment (ROI) because of poor user adoption.
Embedded BI – By embedding business intelligence into the enterprise apps your users love, you can encourage data democratization and analytics in a single sign-on environment. Users do not have to sign in to multiple systems or move data around. They can start with the data within the ERP, HR, Finance or other system and perform analysis from within that system. Make it as easy as you can, and users will be happy!
Mobile BI – Don’t make your users sit at their desk in an office to use the BI tools. Make these tools accessible from the office and on the road, at home and in a client location or hotel. If you want your users to see the value in data democratization and you want to achieve your goals for this initiative, you must give your users the tools they need WHEN THEY NEED those tools.
Business Intelligence with Seamless User Access and Security – Data democratization does NOT mean throwing caution to the wind. Data must still be secured and accessible to users for the things they need to see, but not for the things they are not eligible to see and not in an environment where data security and privacy are at risk. To democratize your data, you must also ensure data governance, security and access standards and requirements are met.
Natural Language Processing – Make the augmented analytics and BI tools intuitive. Democratized data is no good if the users need an advanced degree to access the data. Natural Language Processing (NLP) allows your users to access data in a familiar way, with a Google-type search interface where they can ask questions using regular language and receive answers in a way that is easy to understand. If they can search, query and find information easily, they are more likely to a) use the system and b) understand the information they produce and make the right decisions.
Tools Designed Specifically for Business Users – The solution you select should be designed for business users, not for data scientists, business analysts, IT or statisticians. While you want the data democratization initiative to expand the skills and knowledge of your team, you do not want them to need advanced skills or training. Select a system that can be adopted and used within minutes – not months. Users want sophisticated functionality in an easy-to-use environment. That is important!
‘If you want your data democratization initiative to succeed, select tools that allow your team members to engage in analytics without the frustration of leveraging solutions designed for data scientists or IT staff.’
There are other considerations but, if you address the ones we have highlighted in this article, you will be well on your way to achieving your data democratization goals and ensuring that your users adopt the solution you select.
BI Tools should provide data analytics access for all business users. Simple, Self-Serve BI Tools can provide your business with the foundation to achieve your data democratization and user adoption goals. Let us help you achieve your vision and improve productivity and insight across the organization.
Original Post : Data Democratization Can Succeed with the Right BI Tools!
NLP Search Capability Assures User Adoption of Augmented Analytics!
Gartner predicts that, ‘50% of analytical queries will be generated via search, NLP or voice, or will be automatically generated.’ If your business is considering democratizing data and rolling out analytical capabilities to team members within the organization, you will want to plan for an augmented analytics solution that is designed for, and can be used by, all team members – even those with limited technology skills.
‘The benefits of augmented analytics using NLP enable swift, easy searching and allows business users to create context-rich searches that provide in-depth information and concise results.’
By incorporating natural language processing (NLP) and NLP search capabilities within its features and functionality, business users can gather data, integrated from disparate systems and solutions and use that data to prepare and analyze and to achieve swift, accurate results for decision-making.
Meet User Expectations
Business team members use technology outside the walls of the business. Every business user is accustomed to simple search techniques, ala Google. Ask a question, get a list of results with worry-free dependability. Augmented Analytics solutions using NLP search capabilities ensure that users can gather and analyze data and get results quickly, all by using familiar search techniques. Natural Language Processing Search Analytics (NLP) is a crucial component of search analytics and smart data discovery today. NLP search allows business users to create complex searches without endless clicks and complex navigation and commands. Using this type of search analytics, users can access and view clear, concise answers and analysis quickly and easily. Advanced analytics with Natural Language Processing (NLP) provides a familiar Google-type interface where a user can compose and enter a question using common human language. Augmented Analytics that leverages machine learning and natural language processing (NLP), is designed as a self-serve environment that is easy enough for every business user resulting in increased user adoption, improved data democratization, and return on investment (ROI).
Simplify Search and Analysis
NLP search technology simplifies the user experience and the process of building analytics and achieving results. A business user might ask, ‘who sold the most bakery products in 2017 in the Southwest region?’ It’s that simple! With natural language-processing-based search capability, users can avoid scrolling through menus and navigation and simply enter a search query in natural language. The system will translate the query, and return the results in natural language in an appropriate form, such as visualization, tables, numbers or descriptions. Clickless Analytics democratizes advanced analytics so business users can enjoy the benefits of analytics and data democratization and can improve data literacy and fact-based decision-making using natural language searches. No advanced training is required.
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 – all without extensive training or skills.
‘Incorporate NLP search capabilities within augmented analytics and gather data, integrated from disparate systems and solutions. Use that data to prepare and analyze and to achieve swift, accurate results for decision-making.’
Explore the advantages of Natural Language Processing and NLP Search Analytics. Let us help you implement a solution that will be suitable for your team members and your business results.
Original Post : Make Analytics Easy with NLP Search Capability!
If you are an avid reader of technical research or industry journals, you probably know about Natural Language Processing or NLP. If you don’t know about it, you certainly use it every day – whether you know it or not! When you search using Google, you are using natural language and that makes it easier for you to develop a question and get an answer. Ask a question and get an answer. It’s that simple!
But, when it comes to analytics, NLP is typically much more restrictive. Talk all you want about machine learning and natural language processing but the boundaries and restrictions placed on these concepts in a typical analytical solution do not make it as easy as business users might like.
Remember that your business team members are also consumers outside the walls of the office and they use and appreciate the ease of Google searches. Here, a consumer might ask, ‘how many ounces in a pound’, or ‘what is the tallest building in the world’, and they get an immediate answer. THAT is what they want in analytics as well and if you don’t give it to them, they are unlikely to adopt the analytical tools you invested in or to achieve the results you wanted for optimizing resources, improving productivity and, most importantly, engaging in fact-based decision making that will improve the business bottom line.
So, what, if anything can one do about the disconnect between the ease of use of analytics and the typical NLP solution? To answer this question, we first need to understand the difference between standard natural language processing in analytics (AKA Dumb NLP) and context-driven searching using natural language processing (AKA Intuitive NLP).
Context-driven natural language processing allows people to think and communicate like people – not like machines! It is intuitive and ‘smart’ and goes far beyond ‘dumb’ NLP.
Dumb NLP: The use of NLP in analytics provides the basic foundation to get a user into the details of the data and allow them to choose and filter using columns and filters. It recognizes the data in the column or field but not the context. Much like a text to speech solution, it can read, translate and present the data but it has no real understanding of what the data means. Users often get frustrated when they try to use these tools because they have to wade through the choice of columns, click on menus and sift through scripts and when the NLP query results are presented, they may discover that they did not get what they wanted because they omitted or included some inappropriate data.
Intuitive NLP: Context-driven natural language processing allows the user to think of a question and ask that question in a way that supports human thought, and natural communication. Context-driven NLP ‘understands’ and interprets the question and the user intention so, for example, if a user wants to find sales results for a product sold during the ‘Thanksgiving’ season, they do not have to know the date for a particular year or years. They can simply ask the question. ‘How many donuts were sold in Scottsdale, Arizona during Thanksgiving 2019 and 2018? The system will understand the question and interpret it to provide the right information.
Much like your best friend can understand your intent and respond to a question without your being concise or detailed, context-driven NLP can handle the subtleties and the context without the need for excruciating detail and restrictive programming or scripting.
Ask, ‘What was the best day of bakery sales in Tucson Az last year?’, and the system will know that, a) the ‘best day’ means best day of sales for you, that b) bakery sales means all items that fall within the bakery category in your product portfolio, such as cake, brownies, croissants, cookies, bread etc., c) that Az means Arizona and that d) last year was 2019. Just ask the question…and you will get an answer. It’s that simple.
Context-driven, intuitive NLP provides many opportunities to ask questions and handles many concepts, including,
Synonyms, Phonetics and Abbreviations – Enter question and the system will recognize and process information correcting for spelling errors, abbreviations and related words.
Geography, Places and Persons – Enter a question and allow the system to identify a person or place automatically, and give the answer in context of person or geo location.
Time Series – Enter a question and receive results based on absolute time, or on a range or relative time period.
Rank and Polarity – Enter a question and receive results based on a determination of ‘higher’ or ‘lower’ results.
Aggregation – Enter a question to understand results for averages, minimum, maximum, first, last, sum, counts, etc.
Comparison – Explore how sales or other factors compare from one region, year or variable to another.
Context-driven natural language processing allows people to think and communicate like people – not like machines! It is intuitive and ‘smart’ and goes far beyond ‘dumb’ NLP by offering tools that users will want to leverage and interacting with users in a way that is meaningful to them. Your business users don’t need have to use or understand sophisticated skills to create a query or ask a question. They don’t have to wade through five or ten steps to create a query. With context-driven NLP, users just have to think of a question and type that question and they will get the answers they need.
Make your business more productive, optimize resources, empower your team members and allow them to make confident, fact-based decisions and to solve problems and get the information they need to do their job – without stress or time-consuming training or procedures! THAT is the difference between using ‘dumb’ NLP and using intuitive (context-driven) NLP in analytics.
