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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

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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
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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
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Blog

Power of Predictive Analytics

What is Predictive Analytic?

Using data, statistical algorithms, and machine learning approaches, predictive analytics forecasts the likelihood of future outcomes based on historical data. The objective is to provide the most accurate prediction of what will happen in the future in addition to understanding what has already occurred.

Predictive analytics’ history dates to 1689. Many people contributed to the development of this field to the last 50 years’ worth of technological advancements. It’s right to say that modern technologies like relational databases, faster CPUs, and even Hadoop and MapReduce have made predictive analytics a usable tool for decision-making. Predictive analytics, however, have been used for centuries, according to history. One of the first applications of predictive analytics was in underwriting back when shipping and trade was primarily conducted traveling the seas.

Science & techniques behind predictive data analytics

Predictive models develop (or train) a model that may be applied to forecast values for various or new data sets using known outcomes. Based on estimated appropriateness from a set of input variables, modeling produces outcomes in the form of predictions that reflect a probability of the target variable (for example, revenue).

This differs from descriptive models that assist you identify important relationships and figure out why something happened or descriptive theories that describe what happened. Analytical approaches and processes have entire books devoted to them. Here are some fundamentals to get you know.

There are two types of predictive models. Classification models predict class membership. For instance, you try to classify whether someone is likely to leave, whether he will respond to a solicitation, whether he’s a good or bad credit risk, etc. Usually, the model results are in the form of 0 or 1, with 1 being the event you are targeting. Regression models predict a number – for example, how much revenue a customer will generate over the next year or the number of months before a component will fail on a machine.

The three most popular methods for predictive modeling are neural networks, regression, and decision trees.

  • Decision trees

Decision trees are categorization models that divide data into subgroups according to groups of input factors. This helps you understand a person’s action plan. A decision tree resembles a tree with each leaf serving as a typology or decision and each branch serving as an option among several alternatives.

  • Regression

One of the most widely used statistical techniques is regression (both linear and logistic). Regression analysis determines how different variables are related. It identifies important patterns in huge data sets and is frequently used to evaluate the degree to which factors, such as price, influence the movement of an asset. It is designed for continuous data that may be assumed to follow a normal distribution.

  • Neural networks

Advanced modeling tools like neural networks may simulate incredibly complicated relationships. They are well-liked since they are strong and adaptable. They are powerful because they can handle nonlinear relationships in data, which are becoming more prevalent as we gather more data. They are frequently used to validate the results of straightforward methods like decision trees and regression. Pattern recognition and some AI techniques that visually “model” parameters are the foundation of neural networks.

Other Popular Techniques

  1. Bayesian analysis
  2. Ensemble models
  3. Gradient boosting
  4. Incremental response
  5. K-nearest neighbor (KNN)
  6. Memory-based reasoning
  7. Partial least squares
  8. Principal component analysis
  9. Support vector machine
  10. Time series data mining

Benefits Of Predictive Analytics

  • Making Better Decisions

Decision-making can be done at a higher level because of predictive analytics. You can make more wise decisions the more data the system has at its available. Large amounts of data can be analyzed to find patterns and trends, giving organizations information they might not have had before.

  • Increasing Operation Efficiency

The use of predictive analytics enhances resource management, inventory forecasting, organizational efficiency, performance optimization, and income generation. It enables them to proactively enhance their production processes and respond appropriately when necessary.

  • Finding Fraud

Patterns can be found using predictive analytics to spot and stop illegal activity. Predictive analytics aids in the identification of anomalies that could point to cyberthreats and vulnerabilities as cybersecurity becomes more prominent. This assists businesses in putting appropriate policies in place, protecting their information and operations from fraud and other online threats.

  • Reduce risk

In the financial and insurance industries, predictive analytics is utilized to create accurate and trustworthy client profiles that aid in efficient decision-making. For instance, credit ratings determine a person’s creditworthiness, which lowers the risk to the company.

  • Marketing Campaign Effectiveness

Marketing strategies heavily rely on understanding consumer behavior and buying patterns, and predictive analytics makes it easier to analyze data and find fresh ways to draw in or keep customers.

Where Can Predictive Analytics Be Applied?

  • Manufacturing
  • Health & Insurance
  • Oil, Gas & Utilities
  • Governments & the Public Sector
  • Banking & Financial Services
  • Retail sector
  • Educational sector

Predictive Analytics Software Packages

By extracting data from data sets to identify patterns and trends, predictive analytics software mines and analyzes historical data patterns to forecast future results. Business managers can use decision models created by analysts using predictive analytics solutions to plan for the optimal outcome using a wide range of statistical analyses and algorithms. Predictive analytics software is used by analysts, business users, data scientists, and developers to better understand consumers, partners, and products as well as to spot possible dangers and commercial possibilities. Predictive analytics programs enable organizations to use big data (both stored and real-time) to move from a historical view to a forward-looking perspective of the customer. These tools can be deployed both on premise (usually for enterprise users) and in the cloud.

Popular Predictive Analytics Software Packages in 2022

  • Qlik Sense

Ability to make better data-driven decisions and act using Qlik Sense. From visualization and dashboards to natural language analytics, customizable and embedded analytics, reporting, and alerting, the system offers enhanced analytics for every business need. Its innovative associative technology offers unique capabilities for merging data and exploring information, boosting human intuition with AI-powered insights.

  • Alteryx

Alteryx is a creative, end-to-end, low-code / no-code data analytics platform that enables anybody, anywhere, to transform huge amounts of information into rapid insights that support daily breakthroughs. Today, businesses all around the globe on Alteryx to quickly upskill their staff and achieve results that have a significant impact on the bottom line.

  • SAP Analytics Cloud

With the SAP Analytics Cloud solution, you can easily access various sources of data while combining analytics and planning with integration with SAP applications. You can make decisions with confidence due to SAP Analytics Cloud’s support for integrated planning processes throughout the whole company and its role as the analytics and planning solution within the SAP Business Technology Platform.

  • IBM SPSS Modeler

One of the leading visual data science and machine learning solution is the IBM SPSS Modeler. By expediting the core functions for data scientists, it supports businesses in accelerating time to value and desired results. Leading businesses around the world rely on IBM for machine learning, predictive analytics, model management, and data discovery to leverage their data assets. With full, pre-built algorithms and models that are suitable for hybrid, multi-cloud systems with strong governance and security postures, the IBM SPSS Modeler gives enterprises the ability to tap into data assets and current apps.

  • Minitab Statistical Software

To assist data-driven decision making, Minitab Statistical Software provides visualizations, statistical analysis, prediction, and improvement analytics. When an organization’s employees have access to simple analysis tools, they are all given more authority, regardless of their statistical knowledge or geographical location. By providing a complete and best-in-class package of data analysis and process optimization tools, Minitab has helped businesses and institutions identify patterns, address issues, and uncover insightful data for almost 50 years.

Any more information or demo we are willing to help Contact us

– Heaven Gunarathna

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Selecting a BI Solution for your Business

Companies may build products, analyze their advertising efforts, customize content, and create content strategies with the aid of data analysis. Data analytics can ultimately help firms increase performance and boost their bottom line.

Main factors to be aware of when selecting a BI solution for your business.

  • Ability to meet specific business needs
  • Ease of use
  • Cost for BI Solution
  • Technology to be used
  • Time Estimation
  • Limitations of BI solution
  • Product Scalability
  • Product Security
  • After service
  • Globalization growth

What kind of data do you want to analyze:

Before choosing the right BI tool, you need to know what kind of data you want to analyze. That is because different BI dashboards are designed for diverse types of data analysis. For example, suppose you want to analyze transactional data (e.g., sales transactions). In that case, a business intelligence tool designed for analyzing transactional data will be more useful than one designed for analyzing social media or other unstructured data sources. You should also think about if your company requires numerous platforms to manage different sorts of data or just one platform to handle all data. For instance, your company might need two different platforms if it must analyze and visualize both transactional and social media data. However, if it only needs to evaluate transactional data, a single platform should be adequate (e.g., sales transactions). The screening process will go much more smoothly if you define your BI tool’s purpose clearly.

Ability to meet specific business needs:

There are eight areas to be evaluated.

  1. Access data and create new dashboards: Access the data you need to build interactive dashboards without technical assistance. Technology helps users to develop dashboards in a low code manner.
  2. Customize existing dashboards: Easy to use tools to manipulate and visualize data exactly how you want.
  3. Drill into reports: Zoom into details and underlying data for enhanced analysis. Can add color rules. When needed, the report can warn the reader from colors.
  4. Interact from mobile: Share and analyze with dashboards directly from any mobile device. Today it supports various mobile operating systems. (Android OS.,   Apple iOS., Blackberry OS.)
  5. Data visualization: Capability to automatically transform data into Line charts, Area charts, Bar and column charts, Doughnut charts, Funnel charts, Gauge charts or other types of visual presentation.
  6. User-friendly interfaces: All the features of the BI must be easy to access. Also, mobile environments should offer responsive touch interfaces in a native app.
  7. Analytical capabilities of the BI solution:  It maximizes the opportunities for your organization.
  8. Checks Business Validations: This will ensure that business validations are followed correctly. Otherwise, the system can send alerts.

Ease of use:

  1. Accessibility, flexibility, and availability:  A BI solution should be easy for users across the organization to access whether they are in the office, working remotely, or on the road.
  2. Easiness to train team members/employees to use the solution
  3. Self-service capabilities: A good BI solution must be designed for businesspeople to use on their own as a self-service solution.
  4. Easy navigation across the solution: BI solution should be easy to navigate with point-and-click or drag-and-drop features. It should give users a choice of doing a task themselves or employing automation to handle it.

Cost for BI Solution:

  1. Accessibility, flexibility, and availability:  A BI solution should be easy for users across the organization to access whether they are in the office, working remotely, or on the road.
  2. Easiness to train team members/employees to use the solution
  3. Self-service capabilities: A good BI solution must be designed for businesspeople to use on their own as a self-service solution.
  4. Easy navigation across the solution: BI solution should be easy to navigate with point-and-click or drag-and-drop features. It should give users a choice of doing a task themselves or employing automation to handle it.

Cost for BI Solution:

Most BI tools have an initial cost, but there are also recurring expenses related to using them. These consist of maintenance expenses, consultation and training expenditures, and license renewal fees. Take a note that costs vary from one vendor to another, but here are some things you can expect to pay for:

  1. Pricing models: Some vendors charge per seat or per user, while others charge by the number of data sources in the system. The latter model is often more expensive because it requires more customization.
  2. Licensing fees: These fees may be based on the number of users or seats. Some vendors offer multiple license levels — such as basic, standard, or enterprise — and charge accordingly.
  3. Data management fees: If your organization needs help setting up datasets and loading them into the system, expect to pay extra for this service.

Any more information or demo we are willing to help Contact us

– Heaven Gunarathna

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ERP transformation with Cloud

Business world is continuously improving in order to make the things easier quicker and cost effective. The cloud technology is also such technological invention which pitched in the IT industry to serve business needs. Businesses are increasingly using cloud solutions to integrate their various systems. ERP (Enterprise Resource Planning) is becoming a more popular cloud solution for small and medium sized businesses (SMBs).

Cloud-based ERP is an enterprise resource planning software that can be accessed via the Internet. All cloud-based ERP software supports basic business functions. It efficiently automates essential business workflow across multiple industries. Basic functions like finance and accounting, human resource management, marketing, customer relationship management and supply chain management functions are some common functions facilitated through ERP. In general, cloud ERP solutions offer a more efficient approach by allowing businesses to take advantage of vast computing power. Shifting to cloud ERP generally offers lower upfront costs, faster implementation and reduced physical and staff resource usage. There are many benefits and reasons why companies are switching from on-premises to cloud ERP. Some of them are,

Lower costs

The initial cost of implementing cloud ERP software is significantly lower than on-premises systems, which can reduce existing costs and thus avoid the need for large-scale capital.

It not only saves capital costs, but also reduces operating costs by eliminating the need for hardware configuration and software upgrades, system management, and performance management compared to on-premises ERP.

Security

Customer or the ERP user can experience higher security with the cloud technology. The security of cloud-based software is often better than the security of on-premises ERP systems. In addition, ERP cloud servers are located in secure and centralized facilities, reducing the risk of physical theft. Cloud ERPs providers focus a lot of their efforts on avoiding downtime and having robust power backups and fail-safes in place.

Implementation

Cloud solutions can be configured and deployed much faster than on-premises systems. These systems are specifically aimed at creating highly responsive, adaptable, and easy-to-implement solutions.

Improved accessibility, collaboration, and agility

Cloud ERP offers improved accessibility, usability, and mobility Compared to on-premise ERPs. Using cloud-based ERP allows for more corporate agility which means employees can easily access the tools they need from anywhere, anytime via the internet. It also gives access to real-time information and provides insights into the status of all processes and data.

Reduce dependency and free up internal IT teams

Moving to a cloud-based ERP can have a major impact on company’s internal IT team. This is especially useful for small businesses to get the most out of their IT team also companies who do not have an IT sector can run smoothly without making these additional investments.

Scalability

Companies are having varying requirements for handling data. It could be seasonal or periodical. Adding new hardware and networking is a main part of expansion when it comes to on-premises ERP expansion. This is time consuming and costly. But cloud ERP systems are much more flexible hence easy to adopt with the demand and available to increase and decrease the capacity on incremental cost basis.

Access to emerging technology

Technology is rapidly evolving today. With the advancement of technology, the competitiveness of companies has also increased. Advanced analytics, artificial intelligence, machine learning, and other technological advancements enable the use of massive volumes of data. They can improve forecasting accuracy, discover hidden insights, improve productivity, and offer innovative products. Cloud ERP allows taking advantage of this prominent technology more efficiently and cost-effectively. The service already includes Artificial intelligence, machine learning, analytics, and other business intelligence technologies. This means that businesses can benefit without the physical infrastructure, expenditures, or technical talent that on-premises systems require.

Real-time data

Cloud based ERP provides real-time data better than traditional systems. The possibilities of inaccurate data are reduced when bottlenecks at various software integration points are removed. Hence, the information given is always up to date because the applications and information are hosted in a central cloud location.

Easier customization

Configuring on-premises ERP systems to match the company objectives is more expensive and needs more time and IT resources than cloud-based ERP. This involves integrating the relevant ERP features with third-party applications and performing any necessary software and hardware upgrades.



– Nisandi Nethmani

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From ESG to EESG

Term ESG is not a novel idea or a concept in modern business parlance and same is true for derivative concepts such as ESG Reporting and ESG Investing. ESG guides us to be environmentally sustainable, socially responsible, and standby principles of good governance when we transact business. All responsible corporate citizens have shown their willingness to adhere to principles of ESG with varying degree of commitment. Since inception we as CONIFS never lost sight of ESG and our actions speak for that. More recently we have been asking the question ‘are we doing enough?’ from ourselves. That is how we have been drawn to ‘Equity’ and then from ESG to EESG.

Our construct of ‘Equity’ in the context of ESG centers around the question whether we are doing enough compared to our capabilities?’ Yes, we have tried to be environmentally sustainable by allowing our employees to work from home and thereby reduce carbon emissions and improve psychological wellbeing of employees by cutting down the stress of commuting. But can we do more to towards environmental sustainability and be socially responsible. Driven by this we have given more opportunities for female candidates and at the moment women account for 50% of workforce. We constantly promote female employees to leadership positions and committed to opening new avenues to create flexible environments for working parents.

As responsible employer we pegged our employee salaries Euro to absorb the effect of inflation in Sri Lanka and we did it with transparent and equitable manner. In our effort to be socially responsible, we focused our efforts on youth empowerment. This year we have increased number of internship opportunities for university undergraduates and working hard to increase the quality of internship with focused training, opportunities for explore new things and better compensation. In an economic situation where most are vying to reduce expenditure, we have decided to increase our CSR budget with the aim of consoling fellow citizens who are in distress.

At CONIFS, ESG is not cliché or just another tag line but contrary it is a governing principle and all at CONIFS are committed to it. From ESG we in journey towards EESG with increased focus on whether our efforts and commitment towards ESG is proportionate to our capabilities.

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Uniformity in Integration

In the area of internet of things (IoT) system integration and integration platforms will reign high and dominate most IT discourse.

It is commonplace that in most enterprise IT infrastructures there is a central enterprise resource planning (ERP) system and multiple supplementary or complimentary applications that augment the capabilities of ERP at the center to serve the needs of business. Today there is an emerging trend that large scale organizations adopt two tier ERP setup where there is high power ERP at center work in tandem with an ERP of lesser caliber placed at identified organization location together with a series of peripheral devices. The complexity of the integration landscape gets compounded by the need to deal with customer or supplier ERPs, different ERPs in subsidiaries or sister companies within the same organization and with multiplicity of devices. All these dimensions of integration entail a robust yet simple integration mechanism that ensure all the applications and devices work seamlessly. But most organizations do not enjoy the luxury of robust yet simple integration mechanism.

One of our customers headquartered in Scandinavia with a global presence used at least three different integration platforms to establish a connection between peripheral applications such as payrolls, expense registration systems, invoice scanning systems, tools handling applications and the central ERP, the IFS Applications at the center.  One such integration platform was a ‘gateway’ tool with extremely limited market penetration, another was the inbuilt ‘connect’ capabilities of their ERP and third was the old fashion ODBC method. None of these were capable of handling complexities of customer’s IT landscape and reach the level of sophistication they desired.

Then they were introduced to the WSO2, a world-renowned integration and identity access management platform (www.wso2.com ). With WSO2 our customer has been able to replace multiple modes of integration with a single mode of integration and thereby achieve the uniformity in integration they desired. Apart from technological superiority and security offered by WSO2, moving to WSO2 brought forward operational advantages to the organization.

As we noticed such operational advantages are,

Reduce Rework and Increase Reuse

Our customer is a global service provider to energy sector with special focus on the offshore market. On daily basis they deal with enormous number of employees and use high number of IT applications such as planning tools, time registration tools, expense management tools, payroll to cater to country specific needs. All these applications need one common set of data, the employee information. Previously there were multiple and ad hoc ways to deal with each peripheral application but with WSO2 there is one employee information output from their ERP that can cater to unique needs of peripheral applications. When the next new application comes no need begin from nothing, but instead existing interfaces can cater to it. So, rework is reduced, and use of existing features is increased.

Access to Resources

One of the integration platforms previously used, the ‘gateway’ tool was a creation of one of their IT suppliers. The knowledge about the tool was with handful of employees at the supplier. Since its low market penetration, no third parties to which the customer could resort to for support or consultancy.

Therefore, the access to quality resources when the customer needs them was extremely limited. But WSO2 comes with state-of-the-art customer support of their own and network of partners who are equally capable of supporting users of WSO2.

Reduce Dependency

With lesser-known products comes the hazel of lesser access to capable resources, lesser competitive pricing and higher dependency on vendor. But with a mature product with significant market penetration, you get the benefit of competent resources at competitive prices and thereby reduce or even diminish dependency on vendors with limited global presence.

Uncompromised Uptime

No product is fool proof, and any product can get glitches here and there. WSO2 ensures uncompromised uptime with superiority of its product backed by professional user support provided by WSO2 itself and a series of capable partners such as CONIFS. This availability of support and thereby constant push to reduce downtime is not a luxury that most integration platforms can boast of.

Integration itself is not a destination but instead it is more of a journey with constant product updates WSO2 together with its partner like CONIFS is committed to enable the next level of IT enablement for enterprises.

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Importance of Business Intelligence

Business Intelligence (BI) is a technological process of transforming data into action information for the benefit of stakeholders. This information helps executives, managers and workers make informed business decisions. Set of data which have been collected over a period or wide array is used as the base in BI solutions. BI solutions predominantly focused on business insights for the proactive decision making and used for planning and risk management purposes. However, BI solutions could be used to provide historical, current and predictive views of business operations. Business reporting, online analytical processing, data and process mining, text mining, complex event processing, benchmarking, business performance management are some common functions of business intelligence.

Business intelligence helps businesses to analyze customer behavior and competitor behavior, predict market or industry behavior, set benchmarks and track performance. Overall business performance and competition related decisions are fueled by the Business Intelligence functions. Insightful analysis discovers potential issues or problems for business’s contingency planning.

Different industries are aligned business intelligence tools to manage and coordinate its day-today operations.

Logistics Industry

Logistics is one of the most complicated industries which requires constant overlooking and immediate action based on delivery stage in the supply chain. Large data sets are processed and display through customized dashboards to track the orders effectively. Graphical representation methods being used to make the dashboards user friendly and informative.

Banking and Finance 

Financial and banking companies deal with large amount of data and requirement is to analyse data and identify marketing opportunities by combining advanced technologies such as Microsoft SQL, machine learning.  AI with cloud-based Power BI helps financial and banking companies collect, store, and analyze real-time data.

Construction Industry

Construction industry is complicated industry with multiple needs. It required fine deal with work schedules and deadlines, suppliers, and production processes. Collaboration and coordination for running everything smoothly and getting all the processes done is an integral part when it comes to construction industry. Business intelligence helps to project wise overlooking of all touchpoints in one place through all-inclusive dashboards.

Retail Industry

Stock following is one of the retailer’s prominent BI benefits from the time an order is delivered to the point of sale. BI use data warehousing to empower retailers to track the supply handing. In addition to tracking the orders, BI helps to determine the quantity based on the sales forecast and alerting to re-order on time. Human decision making on continuous supply is replaced with I suggestions on the process.

Manufacturing Industry

Industry-specific knowledge is vital for manufacturing businesses. Business Intelligence helps to analyze manufacturing process to identify the business process bottlenecks and establish effective and efficient process. BI serves as the primary source of structured and reliable solutions for bringing together all related processes to eliminate errors. Manufacturing processes can be easily optimized and improved through a manufacturing dashboard that integrates key data through real-time analysis, features vast interactivity and other features, and predicts what will happen in the future.

At CONIFS our mission is to transform treasure trove of transactional data sitting in your ERP into value generating information insights for decision makers. In our view implementing a BI solution in an organization is more of a business venture than a technical venture handled by few individuals in an isolated corner within IT department. We can help you to make a dream of holistic, robust, and user-friendly BI solution a reality.

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4Ps of ERP Support

ERPs in the world are yet achieve the level sophistication that either they are so simple or intuitive that any user can use it without any user support. Contrary to utopian dream of plug and play ERPs users of modern ERPs need not periodic but continuous user support keep them afloat in sea of functionality and labyrinth of processes.

We believe that success of any ERP support depends on four main pillars. They are policies, processes, platforms and undeniably the people.

Policies

The organizational level policy on user support plays a pivotal role in shaping the ERP user support landscape of an organization. The policy framework influences the availability of support, expected service levels at each level of support and comparative weightage allocated user support aspect among the total IT activities. 

Processes

Processes define framework within which the user support is executed. The modes available to report and issue, the initial response, degree of processing at the first level support in cases of a multi layered support organization, assigning priority, escalation handling, user feedbacks and anything and everything in support execution is process driven.

Platforms

Support platform is the medium through which users interact with support experts, it is central ledger for all support issues and the tool to trace and track all support issues. Selection of platform is critical as it defines ways and means available for user to report an issue, modes available for content sharing, history tracking and most importantly provide leadership with vital data on process improvements and training need in the organization with intelligent processing of support issues over a period.

People

People on the either side of the aisle, namely the providers of support and receivers of support takes the center stage in any ERP support operation. The experience and expertise of the support professionals on ERP they support, level of understanding on the real business, communication together with empath of them will define the destiny of any support operation.