Predictive Analytics – is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data.
API – is a set of routines and programming standards for accessing a software application or web-based platform. The acronym API refers to the English term "Application Programming Interface".
Granularity Attribute – Granularity refers to the level of detail or accuracy of the data. Again, a common example is the fastest way to understand. Consider date values: For daily sales, you need the specific date values of the day in question; for quotas, it may be sufficient to specify the quarter, but if your analytics data is the race results of a sporting event, the granularity may be milliseconds. The level of precision of the data values is the granulation.
Key Attributes – This is a unique value (such as your social security number) in your data structure that is usually generated by the tool, however, it can be user-defined as well.
Balanced Scorecard – Is a performance measurement and management methodology developed by Harvard Business School (HBS) professors Robert Kaplan and David Norton in 1992, whose main objective is to measure business performance through quantifiable and verifiable indicators.
Database or database – A set of data with pre-defined relationships to each other. The data contained in a database is organized in the form of tables, columns and rows. Tables store a set of columns and rows.
Big Data – is the term that describes the immense volume of data, structured and unstructured, that impacts businesses on a daily basis. Big Data can be used for analysis and gaining insights that lead to better decisions and strategic business directions.
Business Analytics – Is a data-centric approach that combines the science of predictive analytics, using advanced analytical algorithms to process data records and create models that can make predictions about future outcomes and add value to businesses.
Business Intelligence, Business Intelligence or BI – is defined as a process of collecting, organizing, and analyzing data that supports decision making in the business environment through dynamic and intuitive graphical solutions (reports, management indicators, and dashboards). BI tools aim to transform huge volumes of data into relevant information for decision making.
Cell – refers to the space at the intersection of a measure dimension member and a member of each attribute hierarchy in a cube.
Charts or Graphs – These are visualization components that present data in a graphically friendly way. No BI solution is complete without the use of this type of component. Among the main types of charts are the pie chart, line, column, bar, area and polar charts.
Cloud Analytics – This is a service model in which analytical data is delivered through a public or private cloud. Cloud analytics applications and services are typically offered under a subscription-based or usage-based pricing model.
Cloud computing – refers to the use of the memory and storage and calculation capacity of shared computers and servers interconnected via the Internet, following the principle of grid computing. It applies to hardware, memory, database, file storage folders, network infrastructure, etc.
Crosstab or Pivot Table – A method of categorizing and combining related data. It consists of three parts: rows, columns, and data. Used in cross-referencing information.
Cube or Cube – An OLAP cube, also known as a multidimensional cube or hypercube, is a data structure created, using OLAP databases, to enable near-instant data analysis.
Cube – A data structure that aggregates measurements by the levels and hierarchies of each of the dimensions. Cubes combine various dimensions (such as time, geography, and products) with summary data (such as sales or customer numbers). A cube is made up of measure, dimensions and dimension attributes.
Dashboard – is the visual presentation of the most important and necessary information to achieve one or more business goals, consolidated and adjusted on a screen for easy monitoring of your business.
Data integration – It is the process of combining data from various sources allowing the consolidation and integration of data in a single environment.
Data mining – Data mining is the process of discovering actionable information in large data sets through mathematical analysis to derive patterns and trends that exist in the data. Usually, these patterns cannot be discovered with traditional data mining because the relationships are too complex or because there is too much data.
Data Scientist – A data scientist who can take the millions of pieces of data that exist in one or more data sources and make sense of them using techniques and methods.
Dimension – Is a set of analyzed information that can be used as a filter; Ex: Client Name, Supervisor, Company, State, etc. It is the table that stores descriptive records (labels, descriptions) referring to facts.
Measurement Dimension – is the dimension that contains all the measurements in a cube.
Database and Cube Dimensions – In a model, you can define independent dimensions that will be included in the cubes of the same model. When you add a dimension to a cube, it is called a cube dimension. On its own in a project, as an independent item in the Object Searcher, it is called a database dimension. Why this difference? Because you can define its properties independently. In the product documentation you will see both terms used, so it is worth understanding what they mean.
DW – Data Warehouse is a database used to store organization-related information. Its design allows the analysis of volumes of data collected from the most diverse information systems of the company, in short, it is the database of BIMachine.
It is the use of computational resources (such as memory, storage capacity and calculations) in a shared way among several computers interconnected through the Internet.
Query Scope (Cube Space) – The scope of a query refers to the boundaries within which data is selected. It can range from a whole cube (a cube is the largest query object) to a cell. Thus, the cube space is the product of the members of a cube’s attribute hierarchies with the cube’s measurements.
Data Structure – is where we store the information extracted from the database. In this structure, the information that is most convenient is collected and intelligently stored so that the BIMachine views can be assembled.
Facts – are tables that relate the measures with the dimensions.
Front-end and Back-end – These are generalized terms that refer to the initial and final stages of a process. The front-end is responsible for collecting user input in various forms and processing it to fit a specification that the back-end can use. Front-end is what you see and what you interact with, i.e. it is the graphical interface. Back-end is the back-end behind this interface, which works on the server side. Front-end is the name given to what is seen on the page, such as: button styles, login layout, etc. The back-end is the raw code that runs on the server side where the application is running.
Gartner – Gartner develops technologies related to the insight its customers need to make their decisions every day. Gartner works with over 10,000 (ten thousand) companies, including CIOs and other IT executives, in corporations and government agencies. The company consists of Research, Program Execution, Consulting, and Events.
Gauges – A Gauge KPI has as its main feature a circular arc and displays a single value that tracks progress against a goal. The goal, or target value, is represented by the line (needle) and progress against that goal is represented by the shading. Also, the value representing progress is shown in bold within the arc and all of them are evenly distributed along the arc, from minimum (leftmost value) to maximum (rightmost value).
Measurement Groups – are a collection of one or more measurements. Most of them are user-defined, and you can use them to gather related measures. The exception is measures of distinct counts. They are always placed in a dedicated measure group that contains only the different measure.
Hierarchy – These are those that are usually visualized in the form of a tree, with links between parent nodes and their respective child nodes. For example: the year (parent), has its months (children) which in turn have the days (children of the children).
Balanced Hierarchy – is the name given when the hierarchy in tree format, has the same amount of data on both the left and right side. Illustrate.
Natural hierarchy – are those that arise naturally as a result of the data we have, for example, Year-Month-Day.
HTML (HyperText Markup Language) – stands for Hypertext Markup Language and is a markup language used to construct pages on the Web. HTML documents can be interpreted by browsers, and the technology is the result of the HyTime and SGML standards coming together.
KPI – is a graphic tool used to measure the performance of a company’s processes and, with this information, develop an action plan with the users involved in order to achieve the established goals.
R Language – It is a programming language widely used among data analysts to develop software for statistical calculations and graphics. It is an environment that enables data storage, matrix calculations, and integration with intermediate tools for analysis.
Machine Learning – It means machine learning, that is, intelligent machines and systems that when programmed are capable of acquiring knowledge on their own, so that from this they can perform tasks replacing the human being. In Business Intelligence, it is a form of data analysis that uses the identification of data patterns to make decisions with minimal human intervention.
MDX – MDX (Multidimensional Expressions) allows you to query multidimensional objects, such as cubes, and returns sets of multidimensional cells that contain cube data. This topic and its subtopics provide an overview of MDX queries. In other words, it is the language used by BIMachine to extract the information from the database and display it in the created views. It is similar to SQL and allows the customization of a calculated measure.
Measures or metrics – are numerical values that represent the dataset and status of a business indicator associated with dimensions.
Calculated member – is a dimension member defined and calculated at query time. A calculated member can be defined as a user query or as an MDX calculation script and stored on the server.
Members – are the real-time data within the dimension.
Metrics – are measures that serve as the basis for a KPI.
Currency is another example: a financial application may track monetary values to multiple decimals, while your local school district may only need values to the unit. It is important to understand granulation to avoid storing unnecessary data. Removing the milliseconds from the hour mark or cents from the sales figure can save storage space and processing time when this level of detail is not relevant to your analysis.
Levels – Where the data is in the hierarchical division, for example, Year is at the first level, Months at the second, and days at the third.
Note: If the granularity attribute and the key attribute are different, all non-key attributes must be linked, directly or indirectly, to the granularity attribute. Within a cube, the granularity attribute defines the granularity of a dimension.
OLAP – Online Analytical Processing, is the service that receives the application’s request and sends it to our data extraction service so that it, in turn, extracts and displays the data on your screen. It is a technology used to organize large commercial databases and support business intelligence. OLAP databases are divided into one or more cubes, and each cube is organized and designed by a cube manager to fit the way you retrieve and analyze data so that it is easier to create and use the pivot table reports and dynamic graph reports you need.
Data sources – is the source cube. Also called data structure.
Pie charts – are charts divided into sectors, each sector of the pie chart displays the size of a piece of related information. Pie charts are typically used to display the relative sizes of the parts of a whole.
Polar charts – Displays a series as a set of points grouped by category in a 360 degree circle. Values are represented by the length of the point as measured from the center of the circle. The further the point is from the center, the larger its value. Category labels are displayed on the perimeter of the graph.
Predictive Analytics or Predictive Analysis – This is a set of tools and techniques for extracting information from data sets to determine future patterns and outcomes. It is used to analyze current data and historical facts to better understand customers, products, and partners and to identify potential risks and opportunities for a company. A number of techniques are used, including data mining, statistical modeling, and Machine Learning to help analysts make future business predictions.
Relational Databases or Relational Database – Is a database that models data in a way that it is perceived by the user as tables, or more formally relationships. All data is stored in tables. These have a structure that repeats itself with each row, as you can see in a spreadsheet. It is the relationships between the tables that make them "relational".
Scatter Charts – These are data visualizations used to show the overall relationship in a large amount of data. The data is displayed as points, each with the value of one variable determining the position on the horizontal axis and the value of the other variable determining the position on the vertical axis.
Snowflake – The main characteristic of this model is that the dimensional tables relate to the fact table, but some dimensions relate only to each other.
Social BI – This is the analysis of social data sources such as Twitter, Facebook and LinkedIn and is used with traditional reporting and BI methods to help organizations make better data-driven decisions.
SQL (Structured Query Language) – Is the standard declarative query language for relational databases. Many of the original features of SQL were inspired by relational algebra.
Stacked Bar Charts – These are bar charts that divide their bars into stacked segments of different colors. The Color axis in the legend is set to (Column Names), which means that each selected column is represented by segments in a specific color.
Star Schema – Data modeling methodology used of a Data Warehouse design. Data is modeled in dimensional tables linked to a fact table. The dimensional tables contain the characteristics of an event. The fact table stores the events that have occurred and the keys to the corresponding characteristics in the dimensional tables.
Subcube is a subset of a cube that represents a filtered view of the cube. Subcubes can be defined with a Scope statement in the MDX calculation script or in a subsection clause in an MDX query, or as a session cube.
Table Fact – This is the table that stores the detailed values of measures, values, or facts, in a Data Warehouse.
Table Variables – Table variables are objects similar to temporary tables. The declaration of a table variable starts as an empty table of specified structure. Its definition includes columns with their data types, precision, size, and optional caveats. These elements must be defined during declaration, and cannot be changed or added to after execution.
Tables – are database objects that contain all the data in a database. In tables, data is organized logically in a row-and-column format similar to that of a spreadsheet. Each row represents a unique record and each column represents a field in the record.
