Tag: Natural Language Processing

Data Insights Assure Quality Data and Confident Decisions!

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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!

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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!

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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!

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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!

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

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May 9, 2024

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.

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Augmented Analytics Must Provide Data Quality and Insight!

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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.

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AI In Analytics: Today and Tomorrow!

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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!"

Data Democratization is Important. So Are the Right BI Tools!

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March 16, 2023

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.

Data Democratization Can Succeed with the Right BI Tools

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!

Smarten Augmented Analytics Now Available on Mobile App!

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July 26, 2022

Smarten is pleased to announce the launch of its Mobile Application for Smarten Augmented Analytics. This native app has a seamless user interface for a great user experience (UX). Smarten Mobile app is available for iOS and Android. Installation is easy.

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Embedded BI Can Encourage Data Literacy!

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June 22, 2022

Technology research and consulting firm, Gartner, predicts that, ‘By 2023, data literacy will become an explicit and necessary driver of business value, demonstrated by its formal inclusion in over 80% of data and analytics strategies and change management programs.’

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