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Is the Citizen Data Scientist Approach Right For My Business?

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March 20, 2025

Find Out the How of the Citizen Data Scientist Approach

In 2016, the technology research firm, Gartner, coined the term ‘Citizen Data Scientist,’ and defined it 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.’

‘When business users make the transition to Citizen Data Scientists with access to augmented analytics solutions, they can provide additional value to the team, to managers and executives and allow IT and data scientists to focus on strategic goals.’

In the ensuing years, the Citizen Data Scientist role has become more refined, and those businesses that embrace this approach have seen real benefits. But just who are Citizens Data Scientists, and how does a business recognize candidates and benefit from enabling this role?

What Does the Citizen Data Scientist Concept Entail, and Can My Business Capitalize On Its Potential?

How Do I Find Citizen Data Scientist Candidates Within My Business? You will find your Citizen Data Scientist candidates among your business users and team members. They are curious and eager to learn new skills to contribute to the organization and to hone their skills for career advancement. Team members who make great Citizen Data Scientists are often power users, and are acknowledged as leaders within their own team. They are NOT IT professionals, analysts or data scientists but they share a common characteristic for precision and wanting to get it right the first time.

What Does a Citizen Data Scientist Do? Within the context of their roles and responsibilities, every business user needs clear, meaningful information to make fact-based decisions and recommendations. Citizen Data Scientists use data to create reports on a daily basis. As the Citizen Data Scientist role evolved, team members have leveraged the advantages of this data to share reports, to create and format presentations for recommendations and suggested changes to support pricing decisions, hiring, production, new products and services, financial investments, marketing and advertising campaigns, and many other decisions. As the movement grows within your organizations, you can enable data democratization and improve data literacy. Citizen Data Scientists can also work with IT, data scientists and business analysts to share their research and analytics when the business feels it is necessary to take the analytics to another level to ensure credibility for strategic decisions.

What Tools and Training Does a Citizen Data Scientist Need? One of the primary reasons the Gartner predictions have come to fruition is he evolution of the business intelligence (BI) and augmented analytics market to support the concept of Citizen Data Scientists. Today’s analytics solutions are easy-to-use, self-serve tools driven by Natural Language Processing (NLP), and machine learning, as well as Artificial Intelligence (AI). All of these technologies come together to support the business user and provide tools that are sophisticated in their functionality, yet intuitive and easy for a business user. These tools do not require IT skills or data science knowledge. When the team uses these tools, they can adopt a common language and techniques to work with IT and data scientists to create use cases and refine and share reports, formats and outcomes. The more complete, and intuitive the solution, the less training and onboard time the user will require. There are simple, Free Training Courses available that can help your business and your team understand the uses and benefits of this approach and enable user adoption.

When business users make the transition to Citizen Data Scientists with access to augmented analytics solutions, they can provide additional value to the team, to managers and executives and allow IT and data scientists to focus on strategic goals. Using Augmented Analytics Tools like self-serve data preparation to gather and prepare data, and smart data visualization to receive suggestions and recommendations on how to best view data, users can combine predictive analytics to forecast and model, and sophisticated tools like anomaly monitoring, key influencers, and sentiment analysis to gain crucial insight into changes in customer buying behavior, supplier issues, product time-to-market, trends, patterns and opportunities with dependable metrics to make data-driven decisions.

‘The Citizen Data Scientist role has become more refined, and those businesses that embrace this approach have seen real benefits.’

If your business wishes to capitalize on the potential of the Citizen Data Scientist approach, it important to work with an IT Partner who can help you define your requirements and strategize for optimal success, providing the augmented analytics tools and knowledge of the industry that is required to position you for success.

Original Post : Is the Citizen Data Scientist Approach Right For My Business?

Understanding GenAI and Agentic AI: What’s the Difference?

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March 19, 2025

Choose or Combine GenAI and/or Agentic AI for Apps

The only way to avoid news of Artificial Intelligence (AI) is to move to the top of a mountain and leave all your devices behind. Talk of AI is everywhere. So, it is no surprise that most businesses are considering how to incorporate artificial intelligence (AI) into their consumer apps, business applications, websites and mobile applications.

Gartner predicts that within the next few months, ‘…40% of enterprise applications will have embedded conversational AI.’

As you discuss AI opportunities with your team and your IT consultant, be sure you understand the terminology. There is a distinct difference among AI technology, products and solutions and the industry often uses the terms interchangeably.

In this article, we will discuss the difference between two types of Artificial Intelligence (AI) development your business may be considering, namely, Generative AI (GenAI) vs. Agentic AI.

Generative AI (GenAI)

This technology is form of AI designed to understand and respond to prompts and to generate text, images (including video) and other media. To function, GenAI models must be trained, using large datasets. By analyzing these datasets, the system can learn to spot repetitive results, trends and patterns. Generative AI utilizes neural networks to recognize and identify these patterns in ‘training’ data, and use that data to generate content.

Here are some of the models in use today:

Multimodal Models

These models can process and integrate information in the form of text, audio, images and video, gestures and facial expressions, etc. Tools like DALL-E, Stable Diffusion, and ChatGPT are based on multimodal models.

Large Language Models (LLM)

LLM is used to understand and generate language. It uses a large volume of data and parameters to train the model.

Variational Autoencoder (VAE)

This model provides probabilistic graphical models and variational methods.

Generative Adversarial Network (GAN)

This machine learning framework consists of two neural networks competing for a ‘win’ or for the best result.

Use Case Examples

Marketing – A business might use Generative AI (GenAI) to create customized, targeted marketing content and social media posts to attract a certain demographic or customer without the need for professional knowledge or human intervention, so the team can focus on critical operations and strategic goals. Using training data, the GenAI model will produce contextual content specifically designed to target customers in a particular market niche.

Reporting and Visualization – When an analytical solution incorporates GenAI within its software or app, it can improve the clarity and precision of the data presented. Using visualization, graphs, images and combining those with summaries and details can provide reports and presentations that are clear and suitable for all audiences, including management and executives, as well as teams and staff members.

Technology – Combine GenAI with search optimization, rules-based systems for natural language generation and chatbots, with simulation, with non-generative ML to classify and segment data, or with graphs. Combining techniques can reduce costs, while delivering appropriate performance, efficiency and accuracy.

For more information about Generative AI (GenAI) benefits and uses, see our free white paper, ‘Generative AI (GenAI): The Benefits And Application Of AI In Analytics.’

Click Here to download the whitepaper.

Agentic AI

This artificial intelligence (AI) approach goes well beyond the ubiquitous platforms such as ChatGPT and other popular AI tools with sophisticated reasoning and iterative planning features to autonomously solve complex, multi-step problems.
  • Flexibility and precision
  • Extended reach and scalability
  • Autonomous capabilities
  • Intuitive capacity
Agentic AI independently and autonomously performs tasks and augments other systems to complete workflow and tasks using tools and processes within a solution or system. It is capable of solving complex problems and taking action and can perform interactive tasks, operating outside the typical machine learning (ML) environment of a classic AI trained model to achieve true process automation.

Use Case Examples

Marketing – Your business might use Agentic AI to automate tasks and schedules, track performance and monitor spending. These AI agents can be categorized to handle specific tasks like creating copy and content, choosing a target audience and monitoring and reporting on marketing campaigns.

Research – Use multi-agent AI models to scan and analyze research, articles and databases and suggest improvements, identify new solutions or products using existing technologies, materials, etc.

Manufacturing – Agentic AI uses sensors attached to machines, components, and other physical assets to predict wear-and-tear and production outages, and sending alerts or initiating processes to mitigate probable issues, avoiding unscheduled downtime and associated costs to manufacturers.

Gartner has predicted that ‘Agentic AI will introduce a goal-driven digital workforce that autonomously makes plans and takes actions — an extension of the workforce that doesn’t need vacations or other benefits.’

Is GenAI OR Agentic AI Right for My Business or Consumer App, or Should I Choose Both?

When GenAI and Agentic AI are combined, the business can build a technology that creates contextual content and is capable of taking autonomous action and making routine decisions, so the enterprise can optimize human and technology resources to scale operations and provide targeted, personalized customer service to enhance customer satisfaction and ensure efficiency and productivity within the organization.

By employing cutting-edge Artificial Intelligence (AI) Technology and expert predictive and data science services, the enterprise can gather, produce and analyze information from disparate data sources, and use that data to create products, enhance services, improve productivity and improve market position, all with the support of a team that is skilled in AI, Data Science, Data Engineering and Predictive Analytics. Contact Us to find out how Generative AI (GenAI), Agentic AI and other AI technologies and services can support your software applications, mobile application, or software product ideas, and advance Digital Transformation (Dx).

Original Post : Understanding GenAI and Agentic AI: What’s the Difference?

Case Study : Augmented Analytics for Global Telecommunications Infrastructure Solutions Provider!

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March 11, 2025

Augmented Analytics for Global Telecommunications Infrastructure Solutions Provider

This Client is a global telecommunications infrastructure provider, offering comprehensive end-to-end solutions for wireless network planning, optimization, and performance management. It offers services to over thirty (30) Tier 1 and Tier 2 telecom operators and Original Equipment Manufacturers (OEMs) worldwide, and excels in advanced 5G NR and LTE-A technologies as well as legacy networks. Business services include RF Planning, Optimization, Quality of Service (QoS) Benchmarking, In-Building Solutions, and skilled Manpower Deployment to provide seamless execution across diverse environments. The Client operates in more than fifteen (15) countries and spans five (5) continents.

Continue reading “Case Study : Augmented Analytics for Global Telecommunications Infrastructure Solutions Provider!”

Why Choose Augmented Analytics with Low-Code, No-Code Development

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February 26, 2025

Low Code No Code Development Supports Analytics Performance

Within the very near future, it is estimated that 70% of all software and application design will include a component of low-code or no-code development. So, it is no surprise that analytics software and tools are also affected by this trend. While advanced analytics and augmented analytics solutions provide a sophisticated, complicated underpinning of algorithms and analytical techniques, the average enterprise expects (and should look for) tools that are easy to use, so they can improve data literacy and data democratization and leverage analytics within the organization at the business user level, to improve results and efficiency.

It may be difficult to understand how such complex systems can benefit from the no code, low code approach, since the very concept of this approach seems at odds with the complexity of an analytical solution, but nothing could be further from the truth. When applied appropriately, these techniques can benefit the foundation of the augmented analytical solution and the users of those solutions.

  • Time and Expense – In a world where new features and functionality must keep pace with market demand, the emergence of no-code and low-code allows developers to add analytical functionality quickly, while controlling costs and time to market.
  • Business and Market Requirements – As organizations and business users embrace analytics, the need for new types of visualization, reporting and features changes quickly. In order to stay abreast of these changes and offer businesses the products they need, analytical vendors can quickly leverage, modify and develop new approaches to satisfy user requirements. Vendors can accommodate business-specific needs and data visualization requirements without time-consuming, expensive customization.
  • Integration of Third-Party Apps – Low-Code, No-Code capabilities support the easy integration of other enterprise applications and solutions and allow data analysis across the organization.
  • Performance and Scalability – Low-Code and No-Code solutions and platforms enable high-performance, scalable solutions and ensure that businesses can accommodate an expanding user base and data volume.
  • Compliance, Data Security and Industry Standards – No Code, Low-Code development includes data encryption features and user access security controls to mitigate risk, and protect data integrity and privacy.
Augmented Analytics with Low-Code, No-Code Development Provides Performance and Adaptability

If you are still wondering whether low-code and no-code approaches are appropriate for software and applications, consider these predictions and statistics from technology research organizations:

  • Gartner predicts that 75% of new software solutions will incorporate a low-code approach to development.
  • By some estimates, the use of low-code, no-code and artificial intelligence in analytics solutions has increased user access to analytics by as much as 56%.
  • Gartner predicts that organizations that lack a sustainable plan to operationalize and manage data and analytics will face a two-year setback in their data and technology efforts.

Choosing the right self-serve, augmented analytics solution can help the enterprise build a crucial foundation for analytics, for transition of business users into a Citizen Data Scientist role and for improved time-to-market, decision-making and collaboration. The use of new and cutting edge technologies and the seamless incorporation of these technologies is critical to the success of the analytical application implementation and to return on investment (ROI) and total cost of ownership (TCO) metrics.

Select and implement an Augmented Analytics Solution with business intelligence (BI) and advanced capabilities and enjoy the benefits of advanced technologies like Artificial Intelligence (AI) And Low-Code, No-Code (LCNC) techniques to ensure affordable, flexible solutions that every user can leverage, no matter their skillset or technical capabilities. Read our free article, ‘The Benefits Of Low-Code No-Code in Augmented Analytics.’

Original Post : Why Choose Augmented Analytics with Low-Code, No-Code Development!

Predictive Analytics Supports Citizen Data Scientists!

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February 20, 2025

Use Predictive Analytics for Fact-Based Decisions

Like every other business, your organization must plan for success. In order to do this, the team must have a dependable plan and be able to forecast results and create reasonable objectives, goals and competitive strategies. These plans and forecasts will support investment in technology, appropriate resources and hiring strategies, additional locations, products, services and marketing strategies, partnerships and other components of business management to ensure success.

Forecasting and planning cannot be based on opinions or guesswork. It must be based on historical data, facts and clear insight into trends and patterns in the market, the competition and customer buying behavior. To accomplish these goals, businesses are using predictive modeling and predictive analytics software and solutions to ensure dependable, confident decisions by leveraging data within and outside the walls of the organization and analyzing that data to predict outcomes in the future.

‘Every industry, business function and business users can benefit from predictive analytics.’

According to CIO publications, the predictive analytics market was estimated at $12.5 billion USD in 2022 and is expected to reach $38 billion USD by 2028.

Predictive Analytics is Beneficial for Every Industry and Business Function

Predictive analytics encompasses techniques like data mining, machine learning (ML) and predictive modeling techniques like time series forecasting, classification, association, correlation, clustering, hypothesis testing and descriptive statistics to analyze current and historical data and predict future events, results and business direction.

When a business selects predictive analytics tools that are suitable for business users and team members, it can leverage sophisticated algorithms and analytical techniques in an easy-to-use environment to enable every team member to contribute to the bottom line by allowing them to gain insight into data and use that insight to make confident, fact-based decisions.

With these tools, users can explore patterns in data and receive suggestions to help them gain insight on their own without dependence on IT or data scientists. The enterprise can provide the tools needed at every level of the organization with tools and data science for business users that are sophisticated in functionality and easy-to-use for users at every skill level.

The benefits of augmented analytics and self-serve predictive modeling include:

  • No complex algorithms or data manipulation
  • Auto-recommendations for algorithms to explore underlying data
  • No advanced data science skills required
  • Analyze, share, collaborate and optimize business potential
  • Business users can prototype and hypothesize without professional assistance
  • Recommend optimal actions to achieve specific goals

Every industry, business function and business users can benefit from predictive analytics. Here are some examples of the use of predictive modeling:

Retail – Predictive Analytics tools can be used to understand customer buying behavior and to suggest products and product bundling based on previous purchases, buying patterns, and demographics. This creates a more personalized and targeted shopping experience that is unique to each customer.

Supply Chain – The organization can forecast demand and manage the supply chain to optimize inventory using machine learning to predict customer demand, seasonality, product trends etc., to that the enterprise can mitigate stock shortages and avoid warehouse and inventory overstock.

Healthcare – By using historical data regarding specific diseases, conditions and treatment plans, providers can forecast treatment outcomes, limit risk and improve overall care, thereby reducing complications, readmission and provider resource, medication and hospital bed shortages.

Energy Infrastructure – Using predictive analytics allows these businesses to monitor and analyze data and performance and to detect patterns and trends that may indicate downtime, breakdowns and maintenance issues.

Financial Services, Banks and Loan Businesses – Predictive analytics provides support for credit risk and fraud mitigation and allows businesses to create scoring models for loan approval, etc. based on credit history, and other financial considerations. Predictive modeling allows the organization to identify transactions that are outside the norm, and alert the business and its customers of hacks, fraud, etc.

‘When a business selects predictive analytics tools that are suitable for business users and team members, it can leverage sophisticated algorithms and analytical techniques in an easy-to-use environment to enable every team member to contribute to the bottom line by allowing them to gain insight into data and use that insight to make confident, fact-based decisions.’

These are just some of the benefits and use cases your business can consider to decide on how best to implement predictive analytics and integrate the use of these tools into day-to-day use for business users to improve data-driven decisions and results.

To find out more about AI And Predictive Analytics, Contact Us. Keep pace with changing enterprise needs and support business agility. Let us help you realize your business goals and objectives with fact-based information, and flexible, scalable technology solutions that will support Citizen Data Scientist initiatives, and improved data literacy and data democratization.

Original Post : Predictive Analytics Supports Citizen Data Scientists!

Choosing a Software Development Partner? Choose Wisely!

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February 14, 2025

The Secret Sauce of Software and IT Consulting Partners!

If a business wishes to remain competitive or to achieve a competitive advantage, it must consider both its internal business applications, and its outreach to consumers, customers, suppliers and partners. Technology, software solutions, mobile apps, web services – all of these offerings can be important to customer satisfaction and internal applications for team members can mean the difference between a productive, collaborative team and a frustrated business user.

(more…) "Choosing a Software Development Partner? Choose Wisely!"

White Paper – The Practical Use of GenAI in Business Intelligence and Analytics Tools

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February 13, 2025

White Paper – The Practical Use of GenAI in Business Intelligence and Analytics Tools

In this white paper, we focus on the practical use of Generative Artificial Intelligence (GenAI) in Business Intelligence (BI) and Analytics tools and present several business use cases to illustrate how this approach to analytics can support business goals and help the enterprise to achieve goals. We also discuss some of the challenges a business may wish to consider in order to ensure that its analytics solution effectively incorporates GenAI so the enterprise can confidently depend on results and improve user adoption and ROI.

(more…) "White Paper – The Practical Use of GenAI in Business Intelligence and Analytics Tools"

Mobile BI Tools Solve Many Enterprise Issues!

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February 11, 2025

Incorporate Data Into User Roles with Mobile BI

If you are a business executive or an IT professional, you have probably seen a number of articles about the importance of Business Intelligence in industry publications and trade journals. If you are still unconvinced or uncertain about the critical importance of business intelligence (BI) and analytics for businesses like yours, here are some sobering, surprising and impressive statistics to ponder.

  • The global business intelligence adoption rate is 26%.
  • Organizations use 4 or more different business intelligence tools on average.
  • Organizations leave 97% of gathered data unused.
  • 74% of employees feel unhappy or overwhelmed when working with data.
  • Businesses using business intelligence are 5 times more likely to reach faster decisions than those that do not.
  • Bad data costs the US economy $3.1 trillion each year.

When you review these statistics, you will notice that some of the issues described can be resolved by the careful selection of the right software and solutions to satisfy your business, your team needs and your data infrastructure.

Why Mobile BI? Because it Solves Common Business Analytics Complaints!

If you are planning to acquire or upgrade a business intelligence or analytics app or solution to provide self-serve augmented analytics for your business users and improve productivity, access and data sharing, you will want to include the review of mobile business intelligence solutions in your requirements.

‘Mobile business intelligence (BI) solutions improve user adoption, ensure access to data from anywhere and encourage the use of data-driven information across the enterprise.’

The right mobile BI tools can address the issues and statistics listed above in the following ways:

  • The global business intelligence adoption rate is 26% – Mobile BI tools will increase the user adoption rate by providing intuitive data analytics that users can access from anywhere at any time, making these tools an important part of each team member’s tool box.
  • Organizations use 4 or more different business intelligence tools on average – The right mobile BI solution can integrate data from disparate data sources, and allow users to visualize in a way that is meaningful to each team member. It can optimize technology infrastructure and mitigate the issue of data silos and complex data mining and data gathering.
  • Organizations leave 97% of gathered data unused – Data is often misunderstood, misused or unused simply because team members and the enterprise do not know how to leverage, gather and analyze the data, or because the IT and/or data science team is short on resources and does not have the time to produce reports. Mobile BI tools are designed to be used by business professionals on a daily basis and to encourage data literacy and data democratization across the enterprise.
  • 74% of employees feel unhappy or overwhelmed when working with data – Understand and address your business user concerns and make data more accessible, easier to understand and use, concise and clear, so that every user can optimize data, make confident decisions and share and collaborate without data science skills or sophisticated data analytical experience.
  • Businesses using business intelligence are 5 times more likely to reach faster decisions than those that do not – Improve productivity, time to market and return on investment (ROI) and total cost of ownership (TCO) to gain a competitive advantage with mobile tools and easy-to-use techniques that will enable fact-based decisions.
  • Bad data costs the US economy $3.1 trillion each year – Banish bad data by integrating your data sources and analyzing and presenting data in a mobile environment to gain insight into trends, opportunities, issues and problems and reveal clear, concise soluti

‘If you are planning to acquire or upgrade a business intelligence or analytics app or solution to provide self-serve augmented analytics for your business users and improve productivity, access and data sharing, you will want to include the review of mobile business intelligence solutions in your requirements.’

Mobile business intelligence (BI) solutions improve user adoption, ensure access to data from anywhere and encourage the use of data-driven information across the enterprise.

If you want to support your business user team and provide a foundation for BI tools that will better serve your team and your customers, explore Smarten Mobile BI benefits and features, with powerful functionality and access for your business users including out-of-the-box Mobile BI and advanced analytics for every team member. For more information on Mobile BI and Augmented Analytics, read our article, ‘Mobile BI Business Use Provides Real Advantages.’

Original Post : Mobile BI Tools Solve Many Enterprise Issues!

DeepSeek Unseats U.S. AI, and Reveals India Potential

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February 6, 2025

DeepSeek Reveals the Potential for India’s AI Market

The shock waves from the DeepSeek announcement in the artificial intelligence (AI) space are still being felt. As AI technology businesses, software developers and consumers digest the new reality, there is no doubt the market will shift, adapt and change in a drastic way.

At the heart of this change is the Chinese company, DeepSeek, a new venture that has achieved a significant breakthrough in inference speed, leapfrogging over previous, world-renowned AI models like OpenAI, and causing a massive derailment of chip-maker Nvidia’s stock by proving that lower-end chips can be used to achieve high-performance results in the AI space. This open-source market entry has radically changed the U.S. and global market and, in so doing, it has also changed the mindset of AI developers and investors in other countries, including India.

Prior to the DeepSeek announcement, the prevailing AI strategy was a scenario in which a business required a huge capital investment to build Large Language Models (LLMs) for training and to fund massive processing capabilities and a huge talent pool for development. That meant government support, and an industry and environment to achieve these multi-billion dollar investments and goals.

DeepSeek built an AI product for less than ten million USD, with only 200 engineers.

That reality has shifted the India AI landscape by proving that the India software industry can move beyond an AI app focus to create base models and fully functional concepts. In fact, India has done it before! In defiance of all common expectations, India’s ISRO space program reached the moon at a fractional cost of the U.S. NASA program.

So, why not now? India (and other countries) have often watched from a distance as their own technology talent developed the best and brightest applications in the U.S. Today, the India government has the opportunity expand the capabilities of AI within the country, going beyond chatbots and AI contextual applications to leverage the technology talent within its borders and Create Solutions For Government, Agriculture And Other Industries.

The media and industry publications like to use the word ‘gamechanger,’ but in this case, the revelation of DeepSeek is a gamechanger in the true sense of the word, especially for India. The government is now supporting the new concept of AI evolution and development with the acquisition of 10k GPUs to support development and building the infrastructure to roll out AI models in India within 4-6 months. Yes, it’s possible!

Is the DeepSeek Moment an Opportunity for India and Global AI Potential?

Union IT Minister Ashwini Vaishnaw recently announced that, ‘the planned compute facility (cloud-based servers that are geared to run and handle AI inference), Vaishnaw stated that it has exceeded initial expectations with the country securing more than 18,600 GPUs. Among these are 12,896 Nvidia H100 GPUs, 1,480 Nvidia H200 GPUs, and 742 AMD MI325 and MI325X GPUs. Originally, the target was to procure 10,000 high-end AI chipsets. “DeepSeek AI was trained on 2,000 GPUs, ChatGPT was trained on 25,000 GPUs, and we now have 18,000 high-end GPUs available,” the IT Minister said.

As the technology market evolves, we can expect to see more changes, and those changes will come faster and more frequently. DeepSeek is just one example of the need for India to establish a nimble, adaptable approach that will respond quickly to new developments and identify the opportunities for growth and application of these developments in the global and local market.

The game is changing, and India no longer needs to defer to the U.S. or other countries in the AI market or in the use of its own development talent and investment infrastructure. It can build an ecosystem that will stand on its own without deference to other market leaders.

As IT Minister Vaishnaw said, ‘India will offer the cheapest compute in the world at less than $1 per hour for high-end chips that power generative artificial intelligence (GenAI) as the government’s ₹10,000 crore IndiaAI Mission comes into play….’

There is much to research, investigate and understand about the most recent DeepSeek development. But it does prove one thing. We do not need complacency or acceptance in technology. We need innovation, and India is up to the challenge!

Select an AI, partner for Artificial Intelligence Application Development, or a partner for  predictive analytics and technology partner and a solution Augmented Analytics Solution With Artificial Intelligence (AI) components to ensure affordable, flexible solutions that every user can leverage, no matter their skillset or technical capabilities. Read our White Papers, ‘Generative AI (GenAI): The Benefits And Applications Of AI In Analytics,’ and ‘The Practical Use Of GenAI In Business Intelligence And Analytics Tools’ and explore the benefits of AI in analytics and the full spectrum of benefits and advantages of current artificial intelligence (AI) technologies.

Original Post : DeepSeek Unseats U.S. AI, and Reveals India Potential!

Smarten Support Portal Updates – January – 2025!

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February 3, 2025

We invite you to explore our latest knowledgebase articles and to join the Smarten user community on Smarten Support Portal.

If you have not registered yet, Click Here to obtain your login credentials.

Knowledgebase Articles

  1. Schedulers : Delivery and Publishing : Working with E-mail Delivery and Publishing Task
  2. Data Sources : Improving performance for fetching data from the database
  3. Datasets & Cubes : Handling multiple JOINs through Step by Step Procedure to create a dataset

Predictive Use cases

  1. Assisted predictive modelling : Classification : Customer Churn model using Smarten Assisted Predictive Modelling
  2. Assisted predictive modelling : Regression : Handling Outliers Using Smarten Assisted Predictive Modelling

Business Use Cases

  1. How to derive the recently purchased city of each customer from daily customer transactions from various cities
  2. Derive Week of year considering the week starts from Saturday

Manuals and Technical Guides

  1. Smarten-Concept-Manual-BI
  2. Smarten-Working with Reports
  3. Smarten-Load-Balancing-Clustering-Architecture

If you don’t find what you need in the Support Portal, we hope you will contact us with your questions, comments or suggestions. We are here to help and to support your business goals and success!

URL: Smarten Customer and Partner Community
Email: support@Smarten.com

Original Post : Smarten Support Portal Updates – January – 2025!