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HGS Named as a “Leader” in NelsonHall’s 2025 NEAT Evaluation for Transforming Business Operations with GenAI


The recognition validates the company’s ability to deliver immediate value and serve as a future‑ready partner

Mumbai, India, — April 13, 2026: HGS (listed on BSE & NSE), a global provider of technology, AI, and business services, today announced that it has been positioned as a “Leader” in NelsonHall’s 2025 NEAT Evaluation for Transforming Business Operations with GenAI in the “Developing custom GenAI solutions” segment. This notable recognition reflects HGS’s strong standing on both evaluation criteria: the ability to deliver immediate value by transforming business operations with GenAI and the ability to meet future client requirements, a reflection of HGS’s broader Intelligent Experience strategy focused on measurable, repeatable business outcomes.

HGS’ modular agentic AI enterprise platform, HGS Agent X®, integrates a broad set of GenAI capabilities, including Agent Assist, Anomaly Detection, Automated QA & Insights, Conversational Bots, and Agentic Process Automation (APA), that can be used to create custom GenAI solutions tailored to each client’s unique high‑value use cases, with clear outcomes and ROI.

Mike Smart, Senior Analyst – Digital Transformation Technologies and Services at NelsonHall, said: “HGS is increasingly focused on delivering agentic solutions through HGS Agent X®, a cloud-based, AI-powered cognitive accelerator, within broader business operations. Rather than simply layering GenAI onto existing workflows, the company focuses on identifying high-impact opportunities and re-engineering processes to avoid automating existing inefficiencies. HGS’s approach emphasizes rapid prototyping and co-creation, supported by its AI Lab, Customer Advisory Board, and data science team. It also maintains flexibility in model selection, evaluating different models and choosing those that best fit each use case.”

Venkatesh Korla, Global CEO, HGS, said: “Amid the rapid acceleration of AI, businesses need clarity more than ever. This recognition highlights our focus on applying real, practical GenAI innovation within robust guardrails, so clients can move forward with confidence on their transformation roadmap. We stand in our clients’ shoes and co‑create solutions with a disciplined Realized AI methodology, while de‑risking their investments with clear ROI. Advising clients through this transformation journey is a responsibility we approach with rigor and commitment.”

HGS has deployed one of its GenAI-powered solutions Interaction Intelligence for a major US-based telecom provider to automate quality assurance and quality monitoring processes with human-in-the-loop. The automation has enabled processing of up to 100 % of customer interactions across channels, and offers real-time insights, consistent evaluations, and enterprise-scale visibility across customer conversations. The quality assurance and quality monitoring processes have been transformed with clear, measurable outcomes:

  • 100% omnichannel interaction assessments now vs 1% before
  • <48 hours turnaround time, down from 7+ days
  • >90% automated solution accuracy

About HGS:

HGS is a global provider of technology, AI, and business services that helps organizations transform with confidence. Rooted in decades of operational excellence, HGS combines automation, analytics, artificial intelligence, and deep domain expertise to deliver Intelligent Experiences across the customer lifecycle, from digital customer care to back-office operations, human resources outsourcing, and advanced contact center solutions.

Building on our strong foundation in CX and business process management, we are expanding into new related areas by integrating technology, data, and operational insight to deliver real business results. Our Realized AI methodology brings discipline, speed, and certainty to transformation, delivering right-sized, practical solutions that work here and now, including a 90‑day Proof of Value commitment that validates measurable impact in live environments before scaling.

HGS also operates NXTDIGITAL (www.nxtdigital.in), India’s premier digital media distribution company, providing satellite, digital cable, and broadband services to over 4.8 million customers across 1,500 cities and towns.

Part of the multi-billion-dollar Hinduja Group, HGS takes a “globally local” approach with ~18,000 employees across 10 countries and 30 delivery centers, supporting some of the world’s most recognized brands. For the year ended March 31, 2025, HGS reported total income of Rs. 4,958.8 crore (US$586.1 million).

HGS. Experience Intelligence.

Learn more at https://hgs.com.

About NelsonHall

NelsonHall is the leading global analyst firm dedicated to helping organizations understand the ‘art of the possible’ in digital operations transformation. With analysts in the U.S., Europe, and India, NelsonHall provides buy-side organizations with detailed, critical information on markets and vendors (including NEAT assessments) that helps them make fast and highly informed sourcing decisions. And for vendors, NelsonHall provides deep knowledge of market dynamics and user requirements to help them hone their go-to-market strategies. NelsonHall’s analysis is based on rigorous, primary research, and is widely respected for the quality and depth of its insight.





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Power BI Datasets | Complete Guide on Power BI Datasets


Power BI Datasets – Table of Content

What is Power BI?

Power BI is a set of software services, apps, and connectors that work together to turn disparate data sources into coherent, visually immersive, and interactive insights. Your data could be in the form of an Excel spreadsheet or a hybrid data warehouse that is both on-premises and cloud-based. Power BI makes it simple to connect to your data sources, visualize and uncover what matters, and share your findings with whomever you choose.

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What are Datasets in Power BI?

A dataset is a data collection that you can connect to or import. Power BI allows you to connect to and import all kinds of datasets, allowing you to put everything together in one place. Dataflows can also be used for sourcing the data for Datasets. Workspaces are associated with datasets, and a single dataset can be used in multiple workspaces.
We have selected “My workspace” and then the “Datasets + dataflows” tab in the example below

Power BI workspace

Let us now look into the different types of Datasets in Power BI.

Types of Datasets

Datasets in Power BI are ready to report and visualize the source of data. There are five different types of datasets, each of which can be constructed in one of the following ways:

  • An existing data model will be connected that is not hosted in a Power BI capability.
  • Power BI Desktop file needs to be uploaded which includes a model.
  • Uploading a CSV (comma-separated values) file, or uploading an Excel workbook (Includes one or more Excel tables and/or a workbook data model).
  • Creating a push dataset using the Power BI service.
  • Creating streaming or dataset with hybrid streaming using the Power BI service.

Let us now explore different types of Datasets.

1) External-hosted models

Azure Analysis Services and SQL Server Analysis Services are the two types of externally hosted models. Installing the on-premises data gateway, whether on-premises or VM-hosted infrastructure-as-a-service (IaaS), is required to connect to a SQL Server Analysis Services model. A gateway isn’t required for Azure Analysis Services.

When there are existing model investments, such as those that form part of an enterprise data warehouse(EDW), connecting to Analysis Services makes sense. By utilizing the identity of the Power BI report user, Power BI can establish a live connection to Analysis Services, enforcing data permissions. Both tabular models and multidimensional (cubes) are supported by SQL Server Analysis Services. A live connection dataset sends queries to externally hosted models, as demonstrated in the accompanying 

External-hosted models

2) Power BI Desktop-developed models

A model can be created using Power BI Desktop, a client application for Power BI development. The model is essentially a tabular Analysis Services model. Models can be created by importing data from dataflows and blending it with data from external sources. While the characteristics of how modeling can be accomplished are outside the subject of this article, it’s crucial to note that Power BI Desktop supports three different types, or modes, of models. We are going to discuss the datasets in the coming sections.

Row-Level Security (RLS) can be used in externally hosted models and Power BI desktop models to restrict the amount of data that can be obtained for a certain user. Users in the Salespeople security group, for instance, can only see report data for the sales region(s) to which they’ve been assigned. Roles in RLS can be either static or dynamic. Static roles apply the same filters to all users allocated to the position, whereas dynamic roles filter by the report user.

3) Excel workbook models

The creation of a model is automatic when datasets are created from Excel workbooks or CSV files. To construct model tables, Excel tables, and CSV data are imported, and an Excel workbook data model is translated to produce a Power BI model. In every scenario, data from a file is imported into a model.

4) Push Dataset

A Power BI dataset that can only be created and populated using the Power BI API is known as a push dataset. However, the lack of a good user interface for creating a push dataset restricted its adoption to scenarios where a single table was inhabited with real-time data streaming.

5) Hybrid Streaming Dataset

Real-time streaming in Power BI allows you to stream data and update dashboards in real-time. Real-time data and visuals can be displayed and updated in any Power BI visual or dashboard. Factory sensors, social media sources, service usage metrics, and a variety of other time-sensitive data collectors or transmitters can all be used to collect and transmit streaming data.

Hybrid Streaming Dataset

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How to Create a Power BI Dataset?

Before discussing the steps of creation. It is necessary to know that there are three basic ways to retrieve data in Power BI Desktop that you will use to create your visualizations:

1) Live:

Here you will be connecting to a server that carries all the data. Although no data is sent, the model’s metadata is imported into Power BI Desktop. A query is transmitted to the server when you build visualizations, and it is then executed. The outcomes are then visualized and returned to Desktop. With SQL Server Analysis Services (SSAS) models, whether multidimensional or Tabular, live connections are commonly employed. Power BI Desktop behaves like any other thin client in this scenario, like Excel or Reporting Services (SSRS). It is not possible to make major modifications to the model, but you can add new measurements that will be available in that  .pbix file.

2) DirectQuery:

You can make more modifications to the model here than you can with a Live connection. The data is kept on the server, and queries are run on the server, just like in Live. The Power BI Desktop model, for instance, allows for the creation of relationships.

3) Import:

Power Query queries are used to import the data into a Power BI Desktop file (.pbix). The data is compressed highly so it’s feasible to load records in millions into a file on your system. A model, comparable to an SSAS Tabular model, is built behind the scenes. This is the most versatile mode, as it allows you to blend data from any source. However, all data must be loaded into your model, which can take a long time to refresh.

Now, let’s move to create the dataset. Below are the steps which make you comprehend the creation of the Power BI Dataset.

1) A dataset is connected to the .pbix file where it was created one by one. When you first launch PBI Desktop, click “Get Data” to create a new dataset.

Get Data

Alternatively, you can choose a source from the dropdown menu as shown below:

dropdown menu

2) Let’s assume we imported a few tables from the WideWorldImporters SQL Server sample database (The .pbix file can be downloaded here). The tables and their relationships are visible in the Model view:

.pbix file downloaded

3) You can view the actual data of one table at a time in the “Data view”.

Data view

4) You can create, view, and interact with visualizations built on top of the data and model in the “Report view”. 

Report view

 The dataset is made up of the data as well as the model view. Now, let’s move to the different modes of Dataset available in Power BI.  

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Dataset modes in the Power BI

These modes of Dataset in Power BI ascertain whether or not data is imported into the model or retained in the data source. The following are the three Dataset modes in Power BI:

  1. Import
  2. DirectQuery
  3. Composite
1) Import

The most popular mode for developing datasets is the import mode. Because of in-memory querying, this mode provides incredibly quick performance. Modelers can also benefit from design flexibility and support for certain Power BI service capabilities (Quick Insights, Q&A, etc.). It’s the default mode when developing a new Power BI Desktop solution because of these advantages.

It’s crucial to realize that all imported data is saved on disk. When the data is refreshed or queried, it should be fully loaded into the memory of Power BI. Import models can yield very rapid query results once they are in memory. It’s also crucial to note that there’s no such thing as a partially loaded Import model in memory. An Import model can also integrate data from any number of supported data source types. The following image illustrates it. 

Import model

2) DirectQuery

Import mode can be replaced by DirectQuery mode. Data is not imported into models created in DirectQuery mode. Instead, they are made up entirely of metadata that defines the model’s structure. If the model is queried, data is retrieved by using the native queries from the underlying data source.

DirectQuery Model

3) Composite

The composite mode can blend DirectQuery and Import modes, or integrate multiple data sources for DirectQuery. The storage mode for every model table can be configured for models created in Composite mode. Calculated tables (defined with DAX) can also be used in this mode.

Composite Model

Import and DirectQuery modes are used in composite models to give you the best of both modes. They can blend the high query performance of in-memory models with the capacity to access near real-time data from data sources when set properly.

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 Conclusion:
We have successfully learned that Power BI lets you connect various datasets for importing and bringing them all together in one place. In this blog, we explored the topics of Datasets in Power BI in a systematic flow by understanding Power BI, then Datasets in Power BI, different types of Datasets and models used for reporting and visualizing data, creating a Dataset for connecting files, and various modes of Datasets in Power BI.

Related Article:

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  5. Power BI Architecture
  6. Power BI Components
  7. Power BI Dashboard
  8. Power BI Data Modeling
  9. Power BI Documentation



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