Author: Milan Dhanushka

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IFS Cloud Analysis Models

IFS Cloud Analysis Models
IFS Cloud Analysis Models

Wouldn’t it be amazing to effortlessly grasp the pulse of your business within seconds? Imagine having a clear understanding of your sales department’s performance even before stepping into your next meeting with them. Indeed, this isn’t just desirable; it’s essential to comprehend your company’s core business processes, assess their efficacy, identify past shortcomings, and discern future opportunities. Yet, the staggering volume of data generated daily by various systems can feel overwhelming. This is precisely where Business Intelligence (BI) emerges as a game-changer.
Business Intelligence entails the meticulous analysis of business information, translating it into actionable insights through a set of strategies and technologies. These insights empower stakeholders to make informed strategic and tactical decisions crucial for business growth. BI tools possess the capability to dissect datasets, presenting analytical findings regarding the business’s current state in a myriad of formats—reports, summaries, dashboards, graphs, charts, and maps, to name a few.
While Enterprise Resource Planning (ERP) systems excel in collating and streamlining business data, BI tools elevate this process by converting raw data into actionable insights. When these two software systems synergize effectively, they become invaluable resources, enhancing decision-making and driving business success.

IFS Cloud Analysis Models

The IFS Analysis Models framework, primarily constructed upon the IFS Cloud ERP system, facilitates the seamless transfer of data from IFS Cloud information sources to a Data Warehouse hosted in Microsoft SQL Server, along with a suite of Tabular models linking to the warehouse.
Comprising 12 pre-constructed Tabular models, tailored to diverse requirements across various business domains, the framework eliminates the necessity for users to devise data models from scratch. Instead, users retain the flexibility to customize these models through custom configurations within a layered application architecture.
With an emphasis on user convenience, the framework offers effortless installation, guided setup, and configuration processes. Additionally, troubleshooting capabilities are readily accessible through the familiar Aurena client interface, ensuring a seamless user experience.

Information Sources

OLTP (Online Transaction Processing) systems, designed primarily to enhance transactional performance, often lack optimization for reporting and analytical purposes. To address this limitation, IFS has developed an information source framework based on the star schema, effectively concealing the intricacies of data structures and logic. Serving as the interface for reporting and analysis, this framework encompasses a diverse array of Information Sources spanning various product areas.
In this framework, each fact is linked to one or more dimensions, facilitating comprehensive analysis. Moreover, IFS extends flexibility to users by enabling customization of information sources through custom attributes or the addition of entirely new information sources. This empowers businesses to swiftly adapt to evolving requirements, leveraging quick information sources and dimensions to cater to specific needs.

Power BI Integration

Now, Microsoft Power BI seamlessly integrates with IFS applications, offering users the ability to access reports published on Power BI Service or Power BI Report Server directly within the IFS Cloud web interface. This integration also supports adding parameters to filter Power BI reports integrated into IFS, enhancing data filtering capabilities.
Furthermore, users can embed Power BI reports, visuals, and dashboards into tiles within IFS Lobbies. This feature streamlines access to Power BI functionality within the IFS Cloud web platform, enabling users to conveniently edit Power BI reports without leaving the IFS environment. Elevate your Power BI experience within IFS with enhanced convenience and user-centric design.

Key Features

User Experience and Ease of Navigation

In IFS Cloud, configuring Analysis models has become significantly easier compared to IFS Applications 9 and 10. All the functionalities, from setting up the connection to SQL Server to loading the data into Tabular Models, are now seamlessly integrated into the Aurena interface. With guided setup and wizards available in the IFS Cloud web platform, users can effortlessly navigate through the process of configuring Analysis models, simplifying the overall setup and configuration process.

Customizability

Utilizing the layered application architecture enables seamless customization of existing tabular models to meet user requirements, enhancing adaptability. Additionally, users have the flexibility to create new Tabular models from the ground up, tailored to their unique business needs and industry standards. This empowers users to incorporate entirely new information sources and tabular models to suit personalized requirements.

Security

Security for the analysis models is managed across different levels, employing techniques such as row-level security and specific user roles within SQL Server. During data extraction from the Oracle database to SQL Server, the built-in IFS Cloud Service User, IFSINFO, which possesses its own schema in the Oracle database containing all required views, is utilized. Complete access to the Information Source views is granted to the IFSINFO user.

Visualization and Reporting

When utilizing IFS Cloud analysis models, users have access to various industry-level reporting and visualization tools such as Excel, Power BI, or Report Builder. Additionally, Analysis models can serve as a Lobby Data source, enabling users to create different lobbies to meet diverse requirements.
Thank you for taking the time to explore the power of IFS Cloud Analysis models. I hope this article has provided valuable insights into how Analysis models can revolutionize data analysis for your business. For deeper technical insights into IFS Cloud Analysis Models, consider delving into Analysis Models.
We’d love to hear your thoughts! Feel free to share your insights, experiences, or questions.

Conclusion

IFS Cloud Analysis Models empower businesses to transform raw data into actionable insights with ease, enhancing decision-making and driving growth. With seamless integration, customization, and advanced visualization tools like Power BI, this framework simplifies the complexities of data analysis. By leveraging pre-built models and flexible configurations, businesses can efficiently adapt to changing needs while maintaining robust security. IFS Cloud Analysis Models are a powerful solution for any organization looking to optimize its business intelligence capabilities.

Written By: Nisansala Liyanage

Categories
Blog

The Power of AI in Microsoft’s Power Platform

The way businesses function in today’s fast-paced digital environment is changing due to the introduction of artificial intelligence (AI) into business tools. At the heart of this revolution is Microsoft’s Power Platform suite, which enables users to develop intelligent chatbots with little code, automate operations, analyze data, and create complex apps. This article explores AI’s components, capabilities, advantages, and future directions as it delves into the platform’s major role in artificial intelligence.

Overview of Microsoft Power Platform

Microsoft Power Platform consists of four main components as listed below:

  1. Power BI: BI tool for visualizing data and gaining insights.
  2. Power Apps: A platform for developing custom apps (canvas, Model Driven) using no/low code.
  3. Power Automate: A service for automating workflows between apps and services.
  4. Power Pages: For creating customer facing web sites.
  5. Copilots (Formerly Power Virtual Agents): A tool for building intelligent chatbots.

These components are seamlessly integrated, allowing users to leverage AI capabilities to enhance productivity and decision-making.

AI Builder:

The AI Builder is probably one of the earliest approaches to utilize AI in the Power Platform. With the help of this functionality, customers can easily add AI capabilities to their Power Apps and Power Automate processes. For a variety of AI/ML activities, AI Builder offers highly accurate pre-built and configurable models.

  • Form Processing: Extracts data from forms and documents, streamlining data entry and processing..
  • Object Detection: Identifies and labels objects within images, useful for inventory management and quality control.
  • Prediction: Analyzes historical data to forecast future outcomes, aiding in decision-making processes.
  • Text Classification: Automatically categorizes text into predefined categories, enhancing data organization.
  • Entity Extraction: Recognizes and extracts specific information from text, such as names and dates.

These models are designed to be user-friendly, allowing even those without technical expertise to harness the power of AI.

AI in Power BI: Enhanced Data Analytics

Power BI integrates AI to provide advanced data analytics and visualization capabilities, helping turn your data into engaging reports in seconds. With Copilot in Power BI, you can save time and gain more insights. Ask a question about your data or describe what you need, including reports, narrative summaries, and calculations, and Copilot will pull, analyze, and visualize the relevant data for you. Using conversational language you use every day, you are able to create customized reports with unique designs, inquire about your data, compute and modify Data Analysis Expressions (DAX) formulas, make story summaries, and alter the summary’s tone, format, and reach.

Some key AI features are,

  • AI Visualizations: Features like Key Influencers and Decomposition Tree help users understand the key drivers behind their data trends.
  • Natural Language Processing (NLP): Users can ask questions in natural language and receive visual answers, making data interaction more intuitive.
  • Cognitive Services Integration: Power BI can utilize Azure Cognitive Services for tasks such as sentiment analysis and image recognition, providing deeper insights.

These AI-driven features enable businesses to make more informed decisions by uncovering hidden patterns and trends within their data.

AI in Power Apps: Intelligent Application Development

Power Apps empowers users to build custom applications with integrated AI capabilities

Now with the help of in-built copilot you can create new app UIs, Data structures and also ‘PowerFx’ formulas within a short time to increase the productivity and the development time.

By embedding AI into Power Apps, users can develop smarter applications that automate tasks and improve user experiences.

  • AI Models: Users can incorporate AI models created with AI Builder directly into their apps, enhancing functionality without complex coding.
  • AI-Powered Controls: Controls like Business Card Reader, Object Detector, and Text Recognizer simplify the addition of AI features into applications.

AI in Power Automate: Streamlined Workflows

Power Automate leverages AI to optimize and automate workflows

Building flows has never been easy, ‘To design it, just describe it’, using your own words. Workflows can be quickly and simply streamlined by describing what you want to automate using natural language expressions. The AI-powered tool will quickly create a flow that is tailored to your requirements after understanding your intent. No speculating or assuming where to start. Increase the efficiency of your processes by utilizing the new process mining copilot features.

Like Power Apps you can use AI features like mentioned below for achieving more complex tasks.

  • AI in Workflows: Users can integrate AI models from AI Builder into their automated workflows. For example, categorizing emails or extracting information from documents can be automated, reducing manual effort.
  • Intelligent Automation: AI-driven recommendations suggest next steps in workflows, enhancing efficiency and productivity. This intelligent automation ensures that business processes are more efficient, accurate, and responsive.

AI in Power Virtual Agents (formerly), Now Copilot:

Copilot/Power Virtual Agents uses AI to create intelligent chatbots that handle complex interactions:

You can use any data source you choose any public or internal websites (i.e. SharePoint) or your documents to use with copilot which gives you can talk to your copilot and get generative answers. This is a great use if you have a large knowledge hub and want specific answers quickly.

  • Conversational AI: These chatbots understand and respond to customer inquiries using natural language processing, providing a more human-like interaction.
  • Integration with AI Builder: Custom AI models enhance chatbot capabilities, allowing them to perform specific tasks and provide precise responses.

This integration allows businesses to offer superior customer service (and also for the employees) by automating routine queries and providing instant support.

Benefits of AI in Power Platform

The integration of AI within the Power Platform offers numerous benefits:

  • Enhanced Productivity: AI automates routine tasks, freeing up time for more strategic activities.
  • Improved Decision Making: AI-driven insights and predictions help businesses make better decisions.
  • User Empowerment: Non-technical users can leverage AI without needing deep technical expertise, democratizing access to advanced technology.
  • Cost Efficiency: By automating tasks and improving efficiency, AI helps reduce operational costs.

These benefits highlight how AI within the Power Platform can drive business growth and innovation.

Real-World Applications

Numerous industries are already being transformed by AI in the Power Platform:

  • Customer service: Businesses can reduce the workload of human agents and speed up response times by using Power Virtual Agents/Copilot to build chatbots that handle frequently asked customer questions.
  • Document Processing: Businesses use AI Builder to automate data extraction from forms, receipts, and invoices, improving processing speed and accuracy.
  • Sales and Marketing: Companies can forecast sales trends and evaluate customer data by utilizing Power BI’s AI capabilities. This helps them create more successful marketing campaigns.

These real-world examples show how AI can be applied to improve business operations.

Future Directions

As the domain of AI is rapidly growing, AI capabilities within Power Platform are also expected to get better day by day.

  • More Sophisticated AI Tools: New Improved AI models and capabilities that address a wider range of business needs.
  • Deeper Integration with Microsoft Services and products: Power Platform’s seamless integration with Azure’s advanced AI services for enhanced functionality.
  • More Pre-Built Models and Templates: Simplifying the implementation of AI by providing ready-to-use models and templates.

These advancements will further empower businesses to innovate and stay competitive in a rapidly evolving digital landscape.

Conclusion

Microsoft’s Power Platform is now much more capable thanks to the collaboration with OpenAI, which has been pivotal in fueling Generative AI innovations, making it a strong and adaptable suite for contemporary businesses. Microsoft has made AI more accessible to people of all technical backgrounds, allowing them to take advantage of the technology’s potential to boost productivity, obtain new insights, and spur creativity. The Power Platform will continue to be an essential resource for businesses hoping to prosper in the digital era as AI develops.

Discover how AI is transforming business operations through Microsoft’s Power Platform. This article explores the platform’s key components, such as Power BI, Power Apps, Power Automate, and Power Pages, highlighting how AI integration enhances productivity, decision-making, and user empowerment. Learn about real-world applications and future advancements that make the Power Platform an essential tool for modern businesses.

Written By: Thanura Marapana
Categories
Blog

Introduction to Koa JS

Koa js Blog Post Image
Koa js Blog Post Image

Introduction

Koa.js is a minimal, flexible, and open-source Node.js web framework designed by the same team behind Express.js. Released in 2013, Koa.js is referred to as the Node.js framework for the next level. According to its official website, Koa.js is described as a leaner, more expressive, and more robust foundation for online apps and APIs. By making use of async functions, Koa is able to eliminate callbacks and dramatically improve error management. Unlike Express, Koa’s core does not include a large collection of middleware. Instead, it provides an elegant suite of tools designed to enable developers to write applications both quickly and efficiently.

Features of Koa.js

Minimalistic Core

Koa focuses on offering essential features for building web server. This allows developers to customize their applications by incorporating only the necessary components, avoiding the overhead often found in monolithic frameworks.

Async/Await for Cleaner Code

Koa uses async functions, which simplify writing asynchronous code. This eliminates the need for complex callback structures, leading to cleaner, more readable code that’s less prone to errors.

Streamlined and Powerful Middleware

Koa’s middleware system is designed to be more streamlined and powerful. Unlike Express.js, Koa’s middleware functions operate in a stack-like manner, similar to the co library. This approach grants developers finer control over data flow and error management within the application.

Improved Error Handling

Error handling in Koa is more straightforward. Middleware can be written using try/catch blocks, simplifying error management, and debugging within the application.

Modular Design for Maintainability

Koa encourages the use of modules, which can be plugged into the application as needed. This modular approach helps keep the application lightweight and focused.

Koa.js vs Express.js

Although Koa.js and Express.js were designed by the same team and have similar purposes, they differ significantly in the following ways:

Philosophy and Design

Koa offers a minimalist, modular approach with modern JavaScript features, while Express provides a more comprehensive, supporting framework with built-in features.

Middleware Handling

Express (stack-based) can lead to callback hell with complex middleware. Koa (cascading, async) avoids this with a cleaner approach.

Error Handling

Koa centralizes error handling with try/catch blocks, while Express uses separate error handling middleware.

Flexibility and Customization

Koa allows high flexibility by requiring custom middleware configuration, while Express offers built-in options with less customization.

Performance

Koa can be more performant due to its lightweight nature, but Express is also optimized.

Learning Curve

Express has an easier learning curve with established documentation, while Koa might be steeper due to its use of async/await.

Setting Up a Koa.js Backend

Let’s move to a simple example to demonstrate how to set up a Koa.js backend and create a REST API to access posts.

1. Install necessary dependencies

First, make sure to install Node.js on your PC. After that you need to initialize the package.json file by executing following command in the command prompt of your folder.

npm init

Then, you need to install Koa.js and its dependencies.

npm install koa

Next, install koa-router to handle routing, and koa-bodyParser to parse request bodies. Finally, you’ll need the crypto module to generate unique ID’s.

npm install koa-router koa-bodyparser crypto
2. Create server

Create a new file named index.js and add following code to setup koa server:.

const Koa = require('koa');
const app = new Koa();
const PORT = process.env.PORT ?? 5000;

// Basic middleware to respond with 'Hello World'
app.use(async ctx => {
  ctx.body = 'Hello World';
});

app.listen(PORT, () => {
  console.log(`Server running on port ${PORT}`);
});
3. Create API’s for the server

Create a new file named posts.api.js and add following code to setup API’s:

import { randomBytes } from 'crypto';

// to store data in a memory temporarily 
const posts = new Map();

export const savePost = ({text}) => {
    const post = {id: randomBytes(16).toString('hex'), text, postedDate: new Date()};
    posts.set(post.id, post);
    return post;
};

export const getAllPosts = () => {
    return [...posts.values()];
};
4. Add Router for REST API

Create a new file name posts.router.js. Then add following codes to setup router endpoints and call above APIs.

import Router from '@koa/router';
import { getAllPosts, savePost} from '../api/posts.api.js';

// define prefix
const postRouter = new Router({
    prefix: '/posts'
});

postRouter.post('/', (ctx) => {
    const data = ctx.request.body;
    ctx.body = savePost(data);
    ctx.set('Content-Type', 'application/json');
    ctx.status = 201;
});

postRouter.get('/', (ctx) => {
    ctx.body = getAllPosts();
    ctx.set('Content-Type', 'application/json');
    ctx.status = 200;
});

export default  postRouter;
5. Handling errors in Koa.js

In Koa, to handle errors effectively, place an error middleware at the beginning of your index.js file. Middleware can only catch errors that occur after it’s defined in the chain. Here’s an example of error handling middleware on a Koa server:

app.use(async (ctx, next) => {
  try {
    await next();
  } catch (err) {
    err.status = err.status || 400;
    ctx.body = {
      message: err.message,
    };
  }
});

You can add a custom middleware function like the one below to handle when the server can’t find the resources you’re requesting.

app.use((ctx) => {
  ctx.set("Content-Type", "text/html");
  ctx.body = "<h3>Not Found</h3>";
  ctx.status = 404;
});
6. Modify the Server

Finally modify the server index.js file by integrating routes, error handling, and all the necessary methods.

import koa from "koa";
import bodyParser from "koa-bodyparser";
import postRouter from "./router/posts.router.js";

const app = new koa();
const PORT = process.env.PORT ?? 5000;

// bodyParser is a middleware used for parsing the request body and accessing request data.
app.use(bodyParser());

// error handling
app.use(async (ctx, next) => {
  try {
    await next();
  } catch (err) {
    err.status = err.status || 400;
    ctx.body = {
      message: err.message,
    };
  }
});

// integrate all the routes define in the router
app.use(postRouter.routes()).use(postRouter.allowedMethods());

app.use((ctx) => {
  ctx.set("Content-Type", "text/html");
  ctx.body = "<h3>Not Found</h3>";
  ctx.status = 404;
});

app.listen(PORT, () => {
  console.log(`Server running on ${PORT}`);
});

Conclusion

Koa.js, with its minimalistic approach and modern features like async/await and advanced middleware system, provides a powerful yet flexible framework for building web applications and APIs. Its design philosophy contrasts with Express.js by providing more control and modularity, making it a convincing choice for developers who need a lightweight and efficient solution. By following the steps outlined above, you can quickly set up a Koa.js backend and start building robust RESTful APIs.

This lightweight, modern Node.js framework can simplify your web development process with its minimalist design and powerful features. Perfect for developers seeking a seamless, efficient approach to building robust applications.

Written By: Charith Widanapathirana