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UNIT-1Component of Decision-Making Process Q.1.What are the steps of decisions making process? Answer:Following are the important steps of the decision making process. Each step may be supported by different tools and techniques.

Step 1: - Identification of the purpose of the decision: In this step, the problem is thoroughly analyzed. There are a couple of questions one should ask when it comes to identifying the purpose of the decision. a) What exactly is the problem?b) Why the problem should be solved?c) Who are the affected parties of the problem?d) Does the problem have a deadline or a specific time-line? Step 2: - Information gathering: A problem of an organization will have many stakeholders. In addition, there can be dozens of factors involved and affected by the problem. In the process of solving the problem, you will have to gather as much as information related to the factors and stakeholders involved in the problem. For the process of information gathering, tools such as 'Check Sheets' can be effectively used. Step 3:- Principles for judging the alternatives: In this step, the baseline criteria for judging the alternatives should be set up. When it comes to defining the criteria, organizational goals as well as the corporate culture should be taken into consideration. As an example, profit is one of the main concerns in every decision making process. Companies usually do not make decisions that reduce profits, unless it is an exceptional case. Likewise, baseline principles should be identified related to the problem in hand. Step 4: - Brainstorm and analyze the different choices: For this step, brainstorming to list down all the ideas is the best option. Before the idea generation step, it is vital to understand the causes of the problem and prioritization of causes. For this, you can make use of Cause-and-Effect diagrams and Pareto Chart tool. Cause-and-Effect diagram helps you to identify all possible causes of the problem and Pareto chart helps you to prioritize and identify the causes with highest effect. Then, you can move on generating all possible solutions (alternatives) for the problem in hand. Step 5: - Evaluation of alternatives: Use your judgement principles and decision-making criteria to evaluate each alternative. In this step, experience and effectiveness of the judgement principles come into play. You need to compare each alternative for their positives and negatives. Step 6: - Select the best alternative: Once you go through from Step 1 to Step 5, this step is easy. In addition, the selection of the best alternative is an informed decision since you have already followed a methodology to derive and select the best alternative. Step 7: - Execute the decision: Convert your decision into a plan or a sequence of activities. Execute your plan by yourself or with the help of subordinates. Step 8: -Evaluate the results: Evaluate the outcome of your decision. See whether there is anything you should learn and then correct in future decision making. This is one of the best practices that will improve your decision-making skills. Q.2. Explain Business intelligence with exampleAnswer:

Business intelligence (BI) leverages software and services to transform data into actionable insights that inform an organization’s strategic and tactical business decisions.

2. BI tools access and analyze data sets and present analytical findings in reports, summaries, dashboards, graphs, charts and maps to provide users with detailed intelligence about the state of the business. 3. The term business intelligence often also refers to a range of tools that provide quick, easy-to-digest access to insights about an organization's current state, based on available data. Business Intelligence Examples: -

Reporting is a central facet of business intelligence and the dashboard is perhaps the archetypical BI tool. Dashboards are hosted software applications that automatically pull together available data into charts and graphs that give a sense of the immediate state of the company.

2. Although business intelligence does not tell business users what to do or what will happen if they take a certain course, neither is BI solely about generating reports. Rather, BI offers a way for people to examine data to understand trends and derive insights by streamlining the effort needed to search for, merge and query the data necessary to make sound business decisions. 3. For example, a company that wants to better manages its supply chain needs BI capabilities to determine where delays are happening and where variabilities exist within the shipping process, says Chris Hagans, vice president of operations for WCI Consulting, a consultancy focused on BI. That company could also use its BI capabilities to discover which products are most commonly delayed or which modes of transportation are most often involved in delays. 4. The potential use cases for BI extend beyond the typical business performance metrics of improved sales and reduced costs, says Cindi Howson, research vice president at Gartner, an IT research and advisory firm. 5. She points to the Columbus, Ohio, school system and its success using BI tools to examine numerous data points — from attendance rates to student performance — to improve student learning and high school graduate rates. 6. BI vendors Tableau and G2 also offer concrete examples of how organizations might put business intelligence tools to use: 7. A co-op organization could use BI to keep track of member acquisition and retention.BI tools could automatically generate sales and delivery reports from CRM data. 8. A sales team could use BI to create a dashboard showing where each rep's prospects are on the sales pipeline. Q.3. Explain Business intelligence vs business analytics.Answer:

One thing you will have noticed from those examples is that they provide insights into the current state of the business or organization: where are sales prospects in the pipeline today? How many members have we lost or gained this month?

2. This gets to the key distinction between business intelligence and another, related term, business analytics. 3. Business intelligence is descriptive, telling you what's happening now and what happened in the past to get us to that state. 4. Business analytics, on the other hand, is an umbrella term for data analysis techniques that are predictive — that is, they can tell you what's going to happen in the future — and prescriptive — that is, they can tell you what you should be doing to create better outcomes. (Business analytics are usually thought of as that subset of the larger category of data analytics that's specifically focused on business.) 5. The distinction between the descriptive powers of BI and the predictive or descriptive powers of business analytics goes a bit beyond just the timeframe we're talking about. 6. It also gets to the heart of the question of who business intelligence is for. As the Stitch data blog explains, BI aims to deliver straightforward snapshots of the current state of affairs to business managers. 7. While the predictions and advice derived from business analytics requires data science professionals to analyze and interpret, one of the goals of BI is that it should be easy for relatively non-technical end users to understand, and even to dive into the data and create new reports. 8. For more, see “Business intelligence vs. business analytics: Where BI fits in your data strategy.” Q.4. Explain the business intelligence software and systems.Answer: A variety of different types of tools fall under the business intelligence umbrella. The software selection service Select Hub breaks down some of the most important categories and features: 1) Dashboards2) Visualizations3) Reporting4) Data mining5) ETL (extract-transfer-load —tools that import data from one data store into another)6) OLAP (online analytical processing) Of these tools, Select Hub says the dashboards and visualization are by far the most popular; they offer the quick and easy-to-digest data summaries that are at the heart of BI's value proposition. There are tons of vendors and offerings in the BI space, and wading through them can get overwhelming. Some of the major players include: 1. Tableau, a self-service analytics platform provides data visualization and can integrate with a range of data sources, including Microsoft Azure SQL Data Warehouse and Excel 2. Splunk, a “guided analytics platform” capable of providing enterprise-grade business intelligence and data analytics 3. Alteryx, which blends analytics from a range of sources to simplify workflows as well as provide a wealth of BI insights 4. Qlik, which is grounded in data visualization, BI and analytics, providing an extensive, scalable BI platform 5. Domo, a cloud-based platform that offers business intelligence tools tailored to various industries (such as financial services, health care, manufacturing and education) and roles (including CEOs, sales, BI professionals and IT workers) 6. Dundas BI, which is mostly used for creating dashboards and scorecards, but can also do standard and ad-hoc reporting 7. Google Data Studio, a supercharged version of the familiar Google Analytics offering 8. Einstein Analytics, Salesforce.com’s attempt to improve BI with AI 9. Birst, a cloud-based service in which multilple instances of the BI software share a common data backend. 10. For a deeper look at today’s most popular business intelligence systems, see "Top 12 BI tools of 2019" and "Top 10 BI data visualization tools." Q.5.What is DSS? Explain purpose of DSS.Answer:

A decision support system (DSS) is an information system that aids a business in decision-making activities that require judgment, determination, and a sequence of actions.

2. The information system assists the mid- and high-level management of an organization by analyzing huge volumes of unstructured data and accumulating information that can help to solve problems and help in decision-making. 3. A DSS is either human-powered, automated, or a combination of both. Purpose:

A decision support system produces detailed information reports by gathering and analyzing data.

Hence, a DSS is different from a normal operations application, whose goal is to collect data and not analyze it.

3. In an organization, a DSS is used by the planning departments a) Such as the operations department b) Which collects data and creates a report that can be used by managers for decision-making. 4. Mainly, a DSS is used in sales projection, for inventory and operations-related data, and to present information to customers in an easy-to-understand manner. 5. Theoretically, a DSS can be employed in various knowledge domains from an organization to forest management and the medical field.One of the main applications of a DSS in an organization is real-time reporting. It can be very helpful for organizations that take part in just-in-time (JIT) inventory management. 6. In a JIT inventory system, the organization requires real-time data of their inventory levels to place orders “just in time”to prevent delays in production and cause a negative domino effect. 7. Therefore, a DSS is more tailored to the individual or organization that is making the decision rather than a traditional system. Q.6. What are the component of DSS and explain it.Answer: The three main components of a DSS framework are: 1. Model Management Systema) The model management system S=stores models that managers can use in their decision-making.b) The models are used in decision-making regarding the financial health of the organization and forecasting demand for a good or service. 2. User InterfaceThe user interface includes tools that help the end-user of a DSS to navigate through the system. 3. Knowledge BaseThe knowledge base includes information from internal sources (information collected in a transaction process system) and external sources (newspapers and online databases). Q.7.What are the types of DSS?Answer:1. Communication-driven: Allows companies to support tasks that require more than one person to work on the task. It includes integrated tools such as Microsoft SharePoint Workspace and Google Docs. 2. Model-driven: Allows access to and the management of financial, organizational, and statistical models. Data is collected, and parameters are determined using the information provided by users. The information is created into a decision-making model to analyze situations. An example of a model-driven DSS is Dicodess – an open-source model-driven DSS. 3. Knowledge-driven: Provides factual and specialized solutions to situations by using stored facts, procedures, rules, or interactive decision-making structures like flowcharts. 4. Document-driven: Manages unstructured information in different electronic formats. 5. Data-driven: Helps companies to store and analyze internal and external data. Q.8. Give advantages of DSSAnswer:

A decision support system increases the speed and efficiency of decision-making activities. It is possible, as a DSS can collect and analyze real-time data.

2. It promotes training within the organization, as specific skills must be developed to implement and run a DSS within an organization. 3. It automates monotonous managerial processes, which means more of the manager’s time can be spent on decision-making 4. It improves interpersonal communication within the organization. Q.9. Give disadvantages of DSSAnswer:

The cost to develop and implement a DSS is a huge capital investment, which makes it less accessible to smaller organizations.

2. A company can develop a dependence on a DSS, as it is integrated into daily decision-making processes to improve efficiency and speed. However, managers tend to rely on the system too much, which takes away the subjectivity aspect of decision-making. 3. A DSS may lead to information overload because an information system tends to consider all aspects of a problem. It creates a dilemma for end-users, as they are left with multiple choices. 4. Implementation of a DSS can cause fear and backlash from lower-level employees. It is because many of them are not comfortable with new technology and are afraid of losing their jobs to technology. Q.10. Difference between Data warehouses and Data LakeAnswer:

Characteristics

Data Warehouse

Data Lake

Data

Relational data from transactional systems, operational databases, and line of business applications

All data, including structured, semi-structured, and unstructured

Schema

Often designed prior to the data warehouse implementation but also can be written at the time of analysis(schema-on-write or schema-on-read)

Written at the time of analysis (schema-on-read)

Price/Performance

Fastest query results using local storage

Query results getting faster using low-cost storage and decoupling of compute and storage

Data quality

Highly curated data that serves as the central version of the truth

Any data that may or may not be curated (i.e. raw data)

Users

Business analysts, data scientists, and data developers

Business analysts (using curated data), data scientists, data developers, data engineers, and data architects

Analytics

Batch reporting, BI, and visualizations

Machine learning, exploratory analytics, data discovery, streaming, operational analytics, big data, and profiling

Q.11. Difference between Data warehouses and Data base.Answer:

Characteristics

Data Warehouse

Transactional Database

Suitable workloads

Analytics, reporting, big data

Transaction processing

Data source

Data collected and normalized from many sources

Data captured as-is from a single source, such as a transactional system

Data capture

Bulk write operations typically on a predetermined batch schedule

Optimized for continuous write operations as new data is available to maximize transaction throughput

Data normalization

De-normalized schemas, such as the Star schema or Snowflake schema

Highly normalized, static schemas

Data storage

Optimized for simplicity of access and high-speed query performance using columnar storage

Optimized for high throughout write operations to a single row-oriented physical block

Data access

Optimized to minimize I/O and maximize data throughput

High volumes of small read operations

Q.12. Explain work of Data warehouse.Answer:

A data warehouse may contain multiple databases. Within each database, data is organized into tables and columns. Within each column, you can define a description of the data, such as integer, data field, or string.

2. Tables can be organized inside of schemas, which you can think of as folders. When data is ingested, it is stored in various tables described by the schema. Query tools use the schema to determine which data tables to access and analyze. Q.13. Short Note on Data warehouseAnswer:

A data warehouse is a central repository of information that can be analyzed to make more informed decisions.

2. Data flows into a data warehouse from transactional systems, relational databases, and other sources, typically on a regular cadence. 3. Business analysts, data engineers, data scientists, and decision makers access the data through business intelligence (BI) tools, SQL clients, and other analytics applications. 4. Data and analytics have become indispensable to businesses to stay competitive. 5. Business users rely on reports, dashboards, and analytics tools to extract insights from their data, monitor business performance, and support decision making. 6. Data warehouses power these reports, dashboards, and analytics tools by storing data efficiently to minimize the input and output (I/O) of data and deliver query results quickly to hundreds and thousands of users concurrently.Q.14. short note on Data warehouses architected.Answer:

Data warehouse architecture is made up of tiers. The top tier is the front-end client that presents results through reporting, analysis, and data mining tools. The middle tier consists of the analytics engine that is used to access and analyze the data.

2. The bottom tier of the architecture is the database server, where data is loaded and stored. Data is stored in two different types of ways: a) Data that is accessed frequently is stored in very fast storage (like SSD drives) and b) Data that is infrequently accessed is stored in a cheap object store, like Amazon S3. 3. The data warehouse will automatically make sure that frequently accessed data is moved into the “fast” storage so query speed is optimized.

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