Top Big data Analytics tools: Emerging trends and Best practicesSpringPeople
For many IT experts, big data analytics tools and technologies are now a top priority. Let's find out the top big data analytics tools in this slide to initialize and advance the process of big data analysis.
Top Big data Analytics tools: Emerging trends and Best practicesSpringPeople
For many IT experts, big data analytics tools and technologies are now a top priority. Let's find out the top big data analytics tools in this slide to initialize and advance the process of big data analysis.
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
The Institution's Innovation Council (Ministry of HRD initiative) and the Institution of Electronics and Telecommunication Engineers (IETE) invited me to grace "World Telecommunication & Information Society Day" on 18 May 2020.
What is Data Warehouse?OLTP vs. OLAP, Conceptual Modeling of Data Warehouses,Data Warehousing Components, Data Warehousing Components, Building a Data Warehouse, Mapping the Data Warehouse to a Multiprocessor Architecture, Database Architectures for Parallel Processing
Know different types of tips about Importance of dataware housing, Data Cleansing and Extracting etc . For more details visit: http://www.skylinecollege.com/business-analytics-course
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
The Institution's Innovation Council (Ministry of HRD initiative) and the Institution of Electronics and Telecommunication Engineers (IETE) invited me to grace "World Telecommunication & Information Society Day" on 18 May 2020.
What is Data Warehouse?OLTP vs. OLAP, Conceptual Modeling of Data Warehouses,Data Warehousing Components, Data Warehousing Components, Building a Data Warehouse, Mapping the Data Warehouse to a Multiprocessor Architecture, Database Architectures for Parallel Processing
Know different types of tips about Importance of dataware housing, Data Cleansing and Extracting etc . For more details visit: http://www.skylinecollege.com/business-analytics-course
Apart from timely availability of data and insightful business knowledge, this presentation will find the list of benefits that are gained by investing in business intelligence services.
Business Intelligence Consultants How to Choose the Right One? - By DataToBizKavika Roy
Your journey to business intelligence starts here. In this Article By DataToBiz, We explored the necessary steps to create a list of best fits for your company.
An basic ideas about needs and concepts of business intelligence.
Presented on DotNetters Tech Summit - 2015 RUET
Presenter: Maksud Saifullah Pulak
Event Url: https://www.facebook.com/events/512834685530439/
Difference between Business Intelligence and Business Analytics_Mujeeb Riaz.pdfMujeeb Riaz
Business Intelligence is used to analyze historical data to predict future trends. It's typically used for things like sales forecasts and product recommendations. On the other hand, business analytics uses predictive modeling to predict how different variables affect each other.
Power BI HR Analytics Dashboard: Simplify HR ProcessesKavika Roy
Data analytics for HR helps streamline the human resource processes within the organization. The Power BI dashboard is commonly used to derive actionable insights to improve employee performance and retention rates. Let’s read more about the importance of HR analytics in an enterprise.
How to Become Business Intelligence Analyst?Intellipaat
Intellipaat BI Training courses: https://intellipaat.com/?post_type=course&s=BUSINESS+INTELLIGENCE&course-cat=business-intelligence
Tableau training: https://intellipaat.com/tableau-training/
Informatica training: https://intellipaat.com/informatica-online-training-certification/
MSBI training: https://intellipaat.com/msbi-online-training-course/
Power BI training: https://intellipaat.com/power-bi-training/
IBM Cognos training: https://intellipaat.com/cognos-online-training-certification/
How to Become Business Intelligence Analyst?Intellipaat
Youtube link : https://www.youtube.com/watch?v=BWOotjetDNs
Intellipaat BI Training courses: https://intellipaat.com/?post_type=course&s=BUSINESS+INTELLIGENCE&course-cat=business-intelligence
Tableau training: https://intellipaat.com/tableau-training/
Informatica training: https://intellipaat.com/informatica-online-training-certification/
MSBI training: https://intellipaat.com/msbi-online-training-course/
Power BI training: https://intellipaat.com/power-bi-training/
IBM Cognos training: https://intellipaat.com/cognos-online-training-certification/
Forward Looking BI: The Future of Decision MakingInside Analysis
The Briefing Room with Dr. Robin Bloor and IBM Business Analytics
Live Webcast on June 17, 2014
Watch the archive:
https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=56bf8e3cbf90e205142eac90d889bb4d
The storyline on business intelligence is solid: companies of all sizes use it to gain insight and improve decision making. Still, organizations are always looking for ways to further optimize the business and accelerate growth. What’s the next step? Predictive, specifically the type with which business users and data analysts alike can engage.
Register for this episode of The Briefing Room to hear veteran Analyst Robin Bloor as he explains how and why traditional business intelligence must evolve. He’ll be briefed by David Clement of IBM who will tout his company’s forward-looking BI platform, which includes versatile, powerful predictive capabilities coupled with traditional dashboards and reports. He will demonstrate how its new graphical features empower business users to do more with advanced analytics, and how the combination of predictive and BI leads to deeper understanding of and across the enterprise.
Visit InsideAnlaysis.com for more information.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
1. “Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
Chapter 4
Getting Started with
Business Intelligence
2. Learning Objectives and Learning Outcomes
Learning Objectives Learning Outcomes
Getting started on Business Intelligence
1. Understanding Business Intelligence
2. The Acronym “BI”
3. What are the other terms for “BI”?
4. The meaning of “BI”, BI defined?
5. What relationship then DSS, EIS, MIS, etc.
have with BI?
6. Is the term “Data-warehouse” synonymous
with “BI”?
7. Difference between ERP (Enterprise Resource
Planning) and BI
8. Need for Business Intelligence at virtually all
levels
9. BI for Past, Present and Future
(a) To understand why BI is
important
(b) To understand the need for a
Data warehouse
(c) To understand the need for BI
at all levels
(d) To understand the difference
between BI and ERP
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
3. Session Plan
Lecture time : 90 minutes approx.
Q/A : 15 minutes
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
4. Agenda
• What is BI?
• Business Intelligence by Other Names
• BI Defined
• BI is…
• BI and decision making
• Why BI?
• Need for BI at virtually all levels
• How BI?
• Case studies
• ERP vs. BI
• Introduction to Business Analytics
• Differences between Business Intelligence and Business Analytics
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
5. What is BI?
Business Intelligence (BI) is about getting
the right information, to the right decision
makers, at the right time.
BI is an enterprise-wide platform that
supports reporting, analysis and decision
making.
BI leads to:
fact-based decision making
“single version of the truth”
BI includes reporting and analytics.
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
6. Business Intelligence by Other Names
Decision Support
System
Business
Analytics
Business
Investment
Competitive
Intelligence
Reporting
Business
Insight
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
7. BI Defined
Howard Dresner, of the Gartner Group, in 1989 coined the term BI. He
defined BI as
“a set of concepts and methodologies to improve decision making in
business through use of facts and fact-based systems”.
• The goal of BI is improved decision making. Yes, decisions were made
earlier too (without BI). The use of BI should lead to improved decision
making.
• BI is more than just technologies. It is a group of concepts and
methodologies.
• It is fact based. Decisions are no longer made on gut feeling or purely on
hunch. It has to be backed by facts.
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
8. Answer a Quick Question
Can you explain why
“Businesses choose to leverage analytics for decision making” ?
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
9. BI Defined
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
10. BI is…
• Fact-based decision making
• Single version of truth
• 360 degrees perspective on your business
• Virtual team members on the same page
• Visibility into enterprise performance
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
11. Answer a Few Quick Questions
• What problems can an enterprise encounter, if the single version of truth is
compromised?
• Cite a few examples from everyday life of fact-based decision making.
• What is your understanding of 360 degrees perspective and why do you think it is
important?
• You are a Project Manager. You lead a 10 member team. Your team members are in
three geographically different locations. What measures will you take to ensure that all
your team members are on the same page?
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
12. BI and Decision Making
Operational
Tactical
Strategic
Low
High
High
Low
Impact Frequency
Types of Decisions
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
13. Explain…
Explain “Strategic, Tactical and Operational Decisions” by providing
appropriate examples.
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
14. Answer a Quick Question
Do you think “BI only deals with analysis of past data” ?
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
15. Why BI?
What happened?
Why did it happen?
What is happening?
Why is it happening?
What will happen?
Know the past
Predict the Future
Analyze the Present
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
16. • There is too much data, but too little insight!
• Business Intelligence has been there in the boardroom for long. There is a
need to expand business intelligence from the boardroom to the front lines!
• Structured and unstructured data need to converge!
Need for BI at Virtually All Levels
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
18. RETAIL
SHOP
Cost to Serve ?
30%
Off
Promotional
Offers?
Attract
Customers?
Manage
Inventory?
Challenges in Retail Industry
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
20. Explain…
Explain how BI is being leveraged in the insurance/healthcare/banking
sector, etc.
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
21. Data Mining in Retail
The Diapers-Beer Example
• A (hypothetical) pattern learned from transaction data: “On Friday evenings,
shoppers who buy diapers also buy beer.”
• Highlights new, surprising correlations that can be acted on by the store.
– To promote more users to display this behavior, consider dynamic store
layout decisions that might alter locations of products based on co-
purchase
– Consider couponing strategies that can be used to cross promote related
products in some cases.
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
22. Data Mining in Credit Card Fraud
• Credit card fraud costs the industry billions of dollars each year and pattern
discovery tools and machine learning models such as neural networks are routinely
used to analyze fraud databases to identify triggers.
• “A self-service transaction at a gas station followed by an expensive purchase” is
indicative of fraud.
– An example pattern learned from credit card fraud data
– Pattern is then used in real-time to flag transactions that might be fraudulent.
For instance, if you fill gas in your car with a credit card and then make an
expensive purchase then the merchant may be instructed (by the point of sale
system) to check the user’s ID.
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
23. Data Mining in UI Optimization
“A simple example had to do with discovering that on the Yahoo Front Page,
centering the search box on the page (as opposed to having it be left-justified) would
increase consumer usage. This led to better user engagement and there was no cost
to Yahoo! to make the change. This was discovered by discovering the hidden
pattern that showed that Netscape users tended to use search more than IE users, and
by discovering that the only visible difference was the subtle position of the box! It
was centered on Netscape browsers but left justified on IE browsers. A very
unnoticeable difference, yet an important one. Who would figure that out???”
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
24. Data Mining for Marketing
Online Product Recommendations
• Amazon.com pioneered the use of collaborative filtering based
approaches to recommend products to users online.
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
25. ERP vs. BI
ERP BI as an enterprise application
ERP is for data input BI is for data retrieval
Essentially an operational /transactional/
OLTP system
Essentially OLAP
Supports the capture, storage and flow of
data across multiple units of an organization
Supports the integration of data from varied
data sources, transforms the data as per
business requirements and stores it in the
business data warehouse
Has support for a few prebuilt reports which
usually help to meet the transactional needs
of the organization
Supports advanced form of reporting
(boardroom quality) and visualization. Has
support for dynamic reports, drill down
reports, drill across reports , etc.
Has little or no support for analytical needs
of the organization
Supports the analytical needs of the
organization
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
26. Introduction to Business Analytics
Business analytics is heavily dependent on data.
For its successful implementation, business analytics requires a high
volume of high quality data.
The challenges faced by business analytics are: storage, integration,
reconciliation of data from multiple disparate sources across several
business functions and the continuous updates to the data warehouse.
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
27. Business Intelligence Business Analytics
Answers the questions: •What happened?
•When did it happen?
•Who is accountable for what
happened?
•How many?
•How often?
•Where did it happen?
•Why did it happen?
•Will it happen again?
•What will happen if we change
x ?
•What else does the data tell us
that we never thought to ask?
•What is the best that can
happen?
Makes use of: •Reporting (KPIs, metrics)
•Automated Monitoring/Alerting
(thresholds)
•Dashboards /Scorecards
•OLAP (Cubes, Slice & Dice,
Drilling)
•Ad hoc query
•Statistical/Quantitative Analysis
•Data Mining
•Predictive Modeling
•Design of experiments to
extract learning out of business
data
•Multivariate Testing
Differences between Business Intelligence and Business
Analytics
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
28. Summarizing
BI is a $15 Billion market today and is growing at 15 to 20%
BI leads to:
fact-based decision making
“single version of the truth”
BI focuses:
To find value in gigantic amounts of data collected in modern
enterprises
On enhancing the quality of the decision-making process
To improve the rate at which the enterprises are able to make decisions
Gartner Says Worldwide Business Intelligence, Analytics and
Performance Management Software Market Grew 4 Percent in 2009
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.
29. Ask a few participants of the learning program to summarize the lecture.
Summary please…
“Fundamentals of Business Analytics”
RN Prasad and Seema Acharya
Copyright 2011 Wiley India Pvt. Ltd. All rights reserved.