Business Intelligence

It refers to all information used by managers in decision making and they come from several sources beyond the source of information for the organization. Sources are drawn from the company’s own information system or external sources.

What are the three levels of decision-making that business intelligence supports?

Operational Level- this is where employees who are working primarily at the operational level can make several decisions as they deal directly with customers and handle all the routine transactions. Therefore, many decisions made follows predetermined policies and procedures that spell out how to handle different situations.

Tactical Level- this is where people at the tactical level draw on business intelligence to make decisions that are mid-level, the kind that may guide individual business units.

Strategic Level- this is where the leadership guides long-term strategy. Decision made at this level can have a widespread effects throughout the organization and beyond, to suppliers, customers and even the whole industry.

What are the most important sources of business intelligence inside the organization? What makes them useful?

• Transactional Databases. These include organizational own databases about customers, employees, suppliers and every other financial transaction. They are useful for daily operation.
• Data Warehouses. Data can be extracted separated from the databases, cleaning them and loading them to the warehouse. Frequent freshening of the warehouse guarantees real-time
• Internal Data Sources.

They are useful for daily operation in an organization.

What are some examples of external sources of business intelligence?

• Intelligent agents where a company use them to extract useful business intelligence from publicly accessible websites.
• BI and Big Data which adds semi-structured and unstructured information in addition to the structured information found in databases or retrieved by agents.

1. How can managers use data mining techniques to analyze patterns, trends, andrelationships? How does this lead to better data-driven decision-making?

• Predictive analytics. This is a data mining approaches and statistical techniques used to predict future behavior, especially to unlock the value of business intelligence for strategy. This technique therefore allows managers in predicting the future having analyzed the trends, patterns and relationship over time.
• Online analytical processing (OLAP). This is a software that allows the user to slice and dice or drill down into massive amounts of data warehouses to reveal significant patterns and trends. It allows users to interactively retrieve meaningful information from data, examine it from many different perspectives and drill down into specific groupings. Therefore, managers can use this technique to retrieve useful information that can be used to make analysis.
• Statistics and modeling techniques. This is used in displaying changes over time or averages and identify real time patterns those which may not occur by chance.
• Text mining. This refers to digging into the vast storehouse of unstructured text-based data that is contained in emails, blogs, tweets, online product reviews and comments as they yield critical business intelligence. Its software tools rely on keywords, semantic structures, linguistics relationships, parts of speech, common phrases, emotion-laden words and even misspellings to extract meaningful information.

These techniques are useful as they can analyze immense qualities of data in order to identify patterns, spot relationships, test hypotheses and assess sentiments in online comments.

 

What is text mining?

This refers to a technique that is used to analyze unstructured text that examines keywords, semantic structures, linguistic relationships, emotion-laden words and other characteristics to extract meaningful business intelligence.

2. What are examples of statistical techniques that managers can use to simulatebusiness situations, optimize variables, and forecast sales or other figures?

• Market basket analysis that looks for relationships to reveal customer behavior patterns as they purchase multiple items.
• Predictive analytics. This is a statistical approach that can be used by a manager to predict future behavior especially to unlock the value of business intelligence for strategy.
• Goal seeking method, where the manager sets a target value for a particular metric like profit/loss and tells the program which variable to change to try to reach the goal.
• What-if analysis- this involves building a model based on relationships among variables that the user can change.
• Goal-seeking, optimization, data mining and statistical forecasting.

What are examples of applications that draw on artificial intelligence for decision support?

• Expert systems. This mimics the reasoning of a human expert drawing from a base of knowledge about a particular subject area in order to come to a decision or a recommendation.
• Neutral networks. They attempt to mimic the way the human brain works, with its vast network of interconnections.
• Captcha- this is a test created by a software developer that the visitor must pass before continuing to register or enter the site.

How are web analytics used to assess the effectiveness of websites?

With the help of a clickstream data, it makes it easy to know every single click by every visitor, along with associated data revealing customer behavior patterns, like the time spent on the page, the URL the visitor just left and the IP address of the visitor. Due to millions of clicks that can be done per day, clickstream data can add up quickly. This helps in managing the usage of the website making it effective, as every measure can reveal something a little different that can help describe how people are interacting with the site, and how well the site is meeting the goals set for.

Also with the help of website metrics, organization can get several data from the server logs with detailed information that refer to a particular time period that the analysist can select like the previous week, month or year. Each entry in the server log contains detailed information about the date and time, the page, the source and any clicks on the page itself.  The logs also contain information about each user, including his/her IP address and the browser. This makes the site effective.

Social Media metrics. This site collects information like the number of active users, posts per user, photo tags, profile information, purchases, and data on friends, group memberships and products ratings. It has privacy settings that prevent the release of this information. This improves the effectiveness of the site

E-commerce metrics is another measure meant to ensure the site is effective.

 

How do dashboards, portals, and mashups support decision making?

Dashboards- this is a graphical user interface that organizes and summarizes information vital to the user’s role and the decisions that the user makes. They summarizes the key performance indicators (KPIs) which are the quantifiable metrics most important to the individual’s role and the organization’s success. This supports decision making in an organization. It also has a capability of coming with the most business intelligence software like KPIs, data quality, timeliness, density, chart formats, maps and visual displays that helps in decision making in an organization.

Portals. These are gateways that provide access to variety of relevant information from many different sources on one screen. Most of the portal users are the customers and suppliers as well as employees where they can easily log into the system access information that are granted to them. Decision making is then supported as the organization can decide what to allow portal users view in the portal and not. They can decide to include what will appeal the users. The use of MY Yahoo! Also influences decision making in an organization as the users can access most of the organization’s information hence the organization can be marketing their products in the process.

Mashups– this is an approach meant to aggregate content from multiple internal and external sources on customizable web pages that relies on Web 2.0 technologies. An organization can therefore aggregate an exploding array of content from countless business intelligence sources, merging maps with customer data, combining dashboards, news sources and excel spreadsheet data; adding live camera feeds and blending information that supports their work roles. These information are helpful in decision making hence mashups supports decision making.

 

How does the human element affect decision-making?

Human element contains a targeted, timely and a well-summarized business intelligence at fingertips making it have more than enough of what is needed to make smart decisions and in trying new strategies at every level of decision-making.

Human element can also use computers to analyze information, in drawing organization’s structured and unstructured resources. They can also tap the growing mounds of data online, combining public and private information in new ways to sort out options and reveal new trends. Therefore, decision-making involves human element and humans are not always rational creatures, weighing the evidence the way a computer weighs input hence influence decision-making.