BI-Warehouse exam
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BI-Warehouse exam - Leaderboard
BI-Warehouse exam - Details
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63 questions
| 🇬🇧 | 🇬🇧 |
| What is Business Intelligence (BI) ? | - The use of computer technology (IT) to enhance decision making, drawing information from within the company and from outside the company Definition of BI - An umbrella term that combines databases, analytical tools, applications, and methodologies |
| What does business Intelligence (BI) do? | - Uses various tools and techniques to structure data so that it can be used for decision making |
| What are some Drivers of BI? | 1. Increasing need to improve business operations 2. Increased computing power and connectivity 3. Vast amount of data that is now available 4. Legal requirements to report accurately and quickly |
| What is the Evolution of BI? | DSS → EIS → BI Data -> Information -> Decisions |
| What is the Objective of BI? | – Give ready access to information in a form that is easily understood – Graphs, charts, tables, etc. – Transform data to information to decisions to actions |
| What are the 4 major components of BI? | 1. Data Warehouse – data collected and stored 2. Business Analytics – a collection of tools for manipulating, mining, and analyzing the data in the DW (e.g., Power BI) 3. Business Performance Management – for monitoring and analyzing performance 4. User Interface – e.g. a dashboard for accessing the information |
| What is Transaction Processing (OLTP)? | – Computer systems (IT) that automate the operations of the company. E.g., ATM, payment terminals at grocery stores, changes in bank balances – Supported by operational databases – Called online transaction processing (OLTP) – ERP systems (SAP) |
| What is Analytic processing (OLAP) ? | – Computer systems (IT) that support decision making – Data is copied from the operational databases into a separate database called a data warehouse – Data Warehouses – organize the data to make it easy to create reports for decision-making – Stored company data from OLTP used to analyze what is happening in the business |
| Transaction Processing (OLTP) vs Analytic Processing (OLAP) | • Transaction Processing (OLTP) – Used to automate operations – Efficient for transaction processing – Inefficient for ad-hoc report generation – Requests for reports created by IT staff • Analytical Processing (OLAP) – Used for analysis – Data is a snapshot of company data at a point in time – Data stored in data warehouses – Data reorganized and structured in such a way that it was fast and efficient for querying, analysis, and decision support. |
| 4 best practices for implementing BI | 1. Decide IF BI can meet needs of the business 2. Standardize IT infrastructures - standardize transaction processing systems across compony 3, Focus on usability of the user interface 4. Ensure high data quality - consistent handling of data -accurate and complete records |
| ALL three business analytics ( Descriptive, Predictive, Prescriptive) | Descriptive = past and present Predictive = future and why it is happening Prescriptive = what should we do and why |
| What does Descriptive analytics enable? | - Business reporting - dashboards -scorecards -data warehousing |
| What is the outcome of Descriptive analytics? | Well-defined business problems and opportunities |
| What does Predictive analytics enable? | - data mining - text mining - web/ media mining -forecasting |
| What is the outcome of Predictive analytics? | Accurate projections of future events and outcomes |
| What does Prescriptive analytics enable? | -optimization -simulation -decision modeling -expert systems |
| What is the outcome of Prescriptive analytics? | Best possible business decisions and actions |
| What is big data ? | - Big Data is data that cannot be stored or processed easily using traditional tools/means |
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| The nature of data | - data is a collection of facts ( obtained through ,experiences, observations or experiments) - data can consist of numbers, words, images - data is lowest level of abstraction , data --> information --> knowledge - date is the source of information - data quality and integrity --> critical to analytics -structured data ( numbers) - unstructured date (text, images) |
| Definitions of data | Data: facts obtained through experiments, observations, sensors, or transactions |
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| What are the structured datas? | Structured data is what computers typically process - categorical •nominal : descriptive non numeric ( color of a phone) •ordinal : order data ( first, second, third - low, medium, high) - numerical •interval data : measures the difference between two values (IQ score, temperature) • ratio data : has an a zero (weight, height) |
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| What is unstructured data | -Textual -multimedia ( audio, image, Video) |
| What is Data preprocessing? | Data preprocessing: getting data ready for analysis |
| What are some data preprocessing ? | 1. data consolidation -sourcing • collect relevent data 2. data cleaning - quality • eliminate incorrect values, input missing values 3. data transformation - put in correct form for processing •numerical variables into categorical ( random numbers, into low, medium, high) 4, data reduction |
| What is a RFID? | Radio Frequency Identifying Device tag that when scanned transmits the data on the tag For example, ski resorts use it to allow skiers with passes to go through checkpoints before getting on the chairlift |
| What is statistics? | – A collection of mathematical techniques to characterize and interpret data |
| What is Descriptive Statistics ? | Describing characteristics of the data (as it is) Used for descriptive analytics |
| What is Inferential statistics? | Drawing insights about the population based on sample data. Sample Population |
| Describe characteristics of negative skewness? | -drops to left - mode > median > mean |
| Describe characteristics of positive skewness? | -drops to right -mode < median < mean |
| Describe characteristics of kurtosis? | Normal distribution, kurtosis = 3 - is associated with height and flatness - smaller (negative kurtosis) more flat /short - higher ( positive kurtosis) the more peaked/tall |
| Simple Regression versus Multiple Regression | Simple regression has one input variable while multiple regression has more than one SEE MORE ON SLIDES |
| Interpreting regression analysis | The Multiple R is the Correlation Coefficient that measures the strength of a linear relationship between two variables. The larger the absolute value, the stronger is the relationship. • 1 means a strong positive relationship • -1 means a strong negative relationship • 0 means no relationship at all • R Square signifies the Coefficient of Determination, which shows the goodness of fit. It shows how well the data fits this regression model. In our example, the value of R square is 0.97, which is an excellent fit. In other words, 97% of the variation in the dependent variable (y-values) is explained by the independent variables (x-values). • Adjusted R Square is the modified version of R square that adjusts for predictors that are not significant to the regression model. • Standard error is also a goodness of fit measure. |
| What is a time series | A time series is a sequence of data points of a variable of interest over a period of time. The data points must be evenly spaced. Eg. Quarterly sales over several years. SEE MORE ON SLIDES |
| Difference Between MAD, MSE , and MAPE | MAD = measures the average absolute errors MAPE= measures the average percentage difference MSE = gives the average squared differences |
| What is a business report? | Business Report: Information is presented in a useful form for business decision makers |
| What is a business report's purpose ? | Purpose: - to improve managerial decisions – Persuade: argument with supporting evidence – Inform – provide information, analysis, etc. – Empower the user to act |
| Time Series NAIVE APPRoach | Assumes demand in next period is the same as demand in most recent period – e.g., If January sales were 68, then February sales are predicted to be 68 |
| Time Series Moving Average Method | Moving Average is a series of arithmetic means • All data points are equally weighted • Used if little or no trend • Used often for smoothing |
| DTime Series Weighted Moving Average Method | Used when some trend might be present – Older data usually less important • Weights based on experience and intuition • More recent data weighted more heavily than older data |
| Time Series exponential smoothing method | Form of weighted moving average – Most recent data weighted most – Weights decline exponentially • Requires smoothing constant (α) – Ranges from 0 to 1 – Subjectively chosen – Higher the value of α, the more weight placed on more recent data s• Advantage: Involves little record keeping of past data |
| What kind of chart is this ? | Line chart |
| What kind of chart is this ? | Bar chart |
| What kind of chart is this ? | Multivariable chart |
| What kind of chart is this? | Stacked bar chart |
| What kind of chart is this? | Scatter plot |
| What kind of chart is this? | Histogram is like a bar chart but shows frequency distribution of a continuous variable |
| What is a Data Warehouse? | - A data warehouse is a repository of data in a form that can be easily accessed to create reports and answer queries. - used for OLAP, data mining, reporting |
| Advantage of data warehouses | - provide a large store of data - doesn't slow down OLTP systems - consistent version of the truth - allows ad-hoc reporting ( trend analysis, comparisons) |
| Disadvantage of data warehouses | - time consuming and expensive to implement and maintain |
| Characteristics of DWs | Subject oriented -Data are organized by detailed subject or area of interest, such as sales, products, or customers, containing only information relevant for decision support. Integrated -Data warehouses must place data from different sources into a consistent format time variant (time series) -maintains historical data nonvolatile - previous data isn't erased |
| Data warehousing process | - collect the data ---> store data -----> access the data |
| What is a data mart ? | - A DM is a subset of a data warehouse, typically consisting of a single subject area (e.g., marketing, operations). ex. departments |
| What is Extraction, transformation, and Loading (ETL)? | ETL is the process of populating the data warehouse from one or multiple sources. – Copy data from its source(s) to the DW. Extraction: is the process of identifying data sources and copying the data required for analysis. – Data copied to the staging area Transformation: is the process of mapping and harmonizing the data that is, making certain the data are consistent, cleansed, and reliable—from their sources to the targets. – Convert the data to the required form Loading: is moving data from the staging area to the DW |
| What is a star schema? | The star schema is the most commonly used and the simplest style of dimensional modeling |
| STAR SCHEMA VS SNOWFLAKE | -a snowflake schema is an extension of a star schema -Snow flake is more in-depth |
| What are Data lakes? | Created to store “Big Data” – large volume of data that has high velocity, high volume and high variety • Stores a larger quantity of data than DW • Pros of data lakes: Easily store a lot of data • Cons: The business can become overwhelmed with the data if not properly organized |
| What are the 3Vs' that describe big data? | - volume •Gigabytes, terabytes, petabytes and zettabytes -variety • Structured – Numeric, character, • Unstructured – text, email, photos, voice, video -velocity •how fast it's being processed |
| The definition of data mining? | The process of analyzing large amounts of data to discover patterns, relationships, and trends to gain insights. |
| Examples of data mining ? | • Customer Relationship Management – Identify customer preferences and buying patterns – Most profitable customers • Fraud Detection – Identify unauthorized use of credit cards • Advertising – Stream targeted ads to online users based on their browsing history and social media activity • Retailing – Predict accurate sales volumes at different locations |
| How does data mining work? | Data mining: using data builds models that discover patterns 1. associations: finds correlations in groups 2. predictions 3. clusters ( finds natural grouping of things) 4. sequential relations, finds time order events ( has checking account, will most likely open a savings account) |
| What are some Data mining techniques ? | Description Models - Describe trends, patterns, and relationships without making predictions. – Exploratory in nature – Clustering – Association Rule Mining – Outlier Detection Prediction Models - Predict the future – Regression Analysis – Time Series |
| What is the Data mining process : CRISP- DM | Cross Industry Standard Process for Data Mining (CRISP- DM) |