What are the usage of Data Analytics?

Usage of Data Analytics


Broadly, predictive analytics can be used to:

1. Description: Provide an overview and summary of the existing state of the world. For example: what is the average age of our customers?How much do they spend, on average, each time they buy? What is the distribution of amounts spent? etc.

2. Comparison: is group A different in some meaningful way from group B, and if so, in what way and by how much? Examples: Do men spend more than women? Does one advertisement work better than others?

3. Clustering / Grouping / Co-occurrence: Group together things that are “similar” according to some definition of “similar”. Example: Are there groups of customers with similar buying/purchase habits? If you know some marketing, cluster analysis is what is used to divide customers into “segments”.

4. Classification: assign a probability that something belongs to 1 of several mutually exclusive classes. Example: Is this credit card trans-action fraudulent? (A: probability Yes/No) Will this person donate to my charity? (A: probability Yes/No) Is this person suffering from a heart attack, or some other mimic condition? (A: probability of Attack)

5. Prediction: predict the most likely value of a continuous variable.Example: what will sales be next quarter? How much will this group of customers spend over the next year? What will be the market share of our new product? 

What are the applications of Data Analytics?

Applications of Data Analyticsˆ 

• Policing/Security
• Transportationˆ
• Fraud and Risk Detection
• Delivery Logistics
• Proper Spendingˆ
• City Planning
• Healthcare
• Internet/web search
• Basket Analysis
• Sales Forecasting
• Inventory Planning

What is Data Analytics? Write down three ways that data analytics is impacting business today.

What is Data Analytics?

Data Analytics mainly helps you to take rapid and better decision based on data.

Data as a collection of facts, observations or other information related to a particular question or problem.

Data can be structured or unstructured. Structured data is information with a high degree of organization that could be included in databases or spreadsheets and is easily searchable by simple search engine algorithms.

Unstructured data is the opposite and is usually text heavy though it may contain video, data or numbers and facts as well. Think of an open field text box that allows you to provide additional comments on a survey.Adding to the complexity Data can also come from a variety of internal and external sources for organizations.

Analytics is the science of examining raw data in order to draw conclusions about the information.

It’s an exciting field, and is dramatically impacting how organizations in many industries are making decisions. The availability of huge volumes of structured and unstructured data sets, combined with advanced computing capabilities. Low cost storage and powerful visualization technology is enabling organizations to gain from market research and social media, to the network of physical objects we call the internet of things. The world we live in today is creating a constant and ever-increasing stream of data. For most organizations, the data they can access is increasing at a rate of 40%each year which creates significant challenges in the way data is captured and secured, organized, analyzed and reported.

 Three ways that data analytics is impacting business today:

Let’s quickly touch on three ways that data analytics is impacting business today.

First, data is enabling new products and services, creating markets that didn’t previously exist and bringing new capabilities to existing markets.Wearables, such as your Fitbit or Apple watch are some examples of new products.

Second, it is disrupting existing markets with innovative upstarts unseating traditionally secure businesses, think of Uber.

Third, data and analytics is driving increased efficiency. For example,retailers have the ability to automate and optimize their supply chain.

In short data is providing the organizations the ability to identify growth opportunities, drive innovation, operate more efficiently, and manage risk in new ways.

Why eliciting and understanding requirements from system stakeholder is a difficult process?

Eliciting and understanding requirements from system stakeholders is a difficult process for several reasons:

1. Stakeholders often don’t know what they want from a computer system except in the most general terms; they may find it difficult to articulate what they want the system to do; they may make unrealistic demands because they don’t know what is and isn’t feasible.

2. Stakeholders in a system naturally express requirements in their own terms and with implicit knowledge of their own work. Requirements engineers, without experience in the customer’s domain, may not understand these requirements.

3. Different stakeholders have different requirements and they may express these in different ways. Requirements engineers have to discover all potential sources of requirements and discover commonalities and conflict.

4. Political factors may influence the requirements of a system. Managers may demand specific system requirements because these will allow them to increase their influence in the organization.

5. The economic and business environment in which the analysis takes place is dynamic. It inevitably changes during the analysis process. The importance of particular requirements may change. New requirements may emerge from new stakeholders who were not originally consulted.

What are questions answered in feasibility study?

A feasibility study is a short, focused study that should take place early in the RE process. It should answer three key questions: a) does the system contribute to the overall objectives of the organization? b) can the system be implemented within schedule and budget using current technology? and c) can the system be integrated with other systems that are used?

Describe Requirement elicitation and analysis process.

The process activities are:

1. Requirements discovery: This is the process of interacting with stakeholders of the system to discover their requirements. Domain requirements from stakeholders and documentation are also discovered during this activity. There are several complementary techniques that can be used for requirements discovery, which I discuss later in this section.

2. Requirements classification and organization: This activity takes the unstructured collection of requirements, groups related requirements, and organizes them into coherent clusters. The most common way of grouping requirements is to use a model of the system architecture to identify sub-systems and to associate requirements with each sub-system. In practice, requirements engineering and architectural design cannot be completely separate activities.

3. Requirements prioritization and negotiation: Inevitably, when multiple stake-holders are involved, requirements will conflict. This activity is concerned with prioritizing requirements and finding and resolving requirements conflicts through negotiation. Usually, stakeholders have to meet to resolve differences and agree on a compromise requirements.

4. Requirements specification: The requirements are documented and input into the next round of the spiral.

What are the ways of writing SRS?

Notation	Description
Natural language sentences	The requirements are written using numbered sentences in natural language. Each sentence should express one requirement.
Structured natural language	The requirements are written in natural language on a standard form or template. Each field provides information about an aspect of the requirement.
Design description languages	This approach uses a language like a programming language, but with more abstract features to specify the requirements by defining an operational model of the system. This approach is now rarely used although it can be useful for interface specifications
Graphical notations	Graphical models, supplemented by text annotations, are used to define the functional requirements for the system; UML use case and sequence diagrams are commonly used.
Mathematical specifications	These notations are based on mathematical concepts such as finite-state machines or sets. Although these unambiguous specifications can reduce the ambiguity in a requirements document, most customers don’t understand a formal specification. They cannot check that it represents what they want and are reluctant to accept it as a system contract.

What are the problems to write requirement using natural language?

The flexibility of natural language, which is so useful for specification, often causes problems. There is scope for writing unclear requirements, and readers (the designers) may misinterpret requirements because they have a different background to the user. It is easy to amalgamate several requirements into a single sentence and structuring natural language requirements can be difficult.

Who are the users of requirement documents?

System Customers: Specify the requirements and read them to check that they meet their needs. Customers specify changes to the requirements.

Managers: Use the requirements document to plan a bid for the system and to plan the system development process.

System Engineers: Use the requirements to understand what system is to be developed

System Test Engineers: Use the requirements to develop validation tests for the system

System Maintenance Engineers: Use the requirements to understand the system and the relationships between its parts.

What is SRS? How SRS is written in agile ways?

The software requirements document (sometimes called the software requirements specification or SRS) is an official statement of what the system developers should implement. It should include both the user requirements for a system and a detailed specification of the system requirements. Sometimes, the user and system requirements are integrated into a single description. In other cases, the user requirements are defined in an introduction to the system requirements specification. If there are a large number of requirements, the detailed system requirements may be presented in a separate document.

However, agile development methods argue that requirements change so rapidly that a requirements document is out of date as soon as it is written,so the effort is largely wasted. Rather than a formal document, approaches such as Extreme Programming (Beck, 1999) collect user requirements incrementally and write these on cards as user stories. The user then prioritizes requirements for implementation in the next increment of the system.