Glossary
Agile: A methodology cribbed from software development that now sees application in many areas of business. Agile aims to help teams respond to unpredictability through incremental, iterative work cadences and shortened feedback loops (e.g. using short, daily meetings where project workers describe what they are working on.) Agile methodologies are an alternative to waterfall, or traditional sequential development.

 

Analytics: The discovery, interpretation, and communication of meaningful patterns in data. They are essentially the backbone of any data-driven decision making.

Analysis Services: Also known as Microsoft SQL Server Analysis Services, SASS, and sometimes MSAS. Analysis Services is an online analytical data engine used in decision support and business analytics. It provides the analytical data for business reports and client applications such as Power BI, Excel, Reporting Services reports, and other data visualization tools. Analysis Services are used by organizations to analyze and make sense of information that could be spread out across multiple databases, or in disparate tables or files.

Business Analytics (BA): Refers to the skills, technologies, and practices for investigation of past business performance to gain insight and drive business planning. It focuses on developing new insights and understanding of business performance based on data and statistical methods. While business intelligence (BI) focuses on a consistent set of metrics to both measure past performance and guide business planning, business analytics is focused on developing new insights and understanding based on statistical methods and predictive modeling.

Furthermore, while BI methods such as querying, reporting, OLAP, and alert tools answer questions such as:
– What happened?
– How many?
– How often?
– Where is the problem?
– What actions are needed?

Business analytics can address questions like:
– Why is this happening?
– What if these trends continue?
– What will happen next?
– How can we optimize?

Back-End: In software, ‘back-end’ applications or programs interact directly with resources or databases without directly interfacing with an end user. To access back-end processes, users will usually do so via a user interface situated on the ‘front-end.’ The presentation layer is the front–end, whereas the access layer is known as the back-end.

BI application designer: Someone responsible for designing the initial reporting templates and dashboards in the front-end applications. They generally require a combined enthusiasm for data visualization, user experience design, and applications reporting. Typically, BI application designers become the source for ongoing front-end BI application support.

BI Project Sponsor: Ideally, a project sponsor is an executive level individual who understands the importance of BI projects, has compelling business motivation, and can help drive results. This person will be the project’s ultimate client and its strongest advocate. Not involved in the day to day of a project, but instead, they provide oversight, direction, and momentum.

Big Data: This term is rapidly achieving buzzword status—but colloquially, it refers to an amount of data so impossibly large that it cannot be parsed by traditional techniques. According to research firm Gartner, “‘Big Data’ is high-volume, high-velocity, and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision-making, and process automation.”

Business Driver: This term can refer to either a resource, process, or condition that is essential to the growth and continued success of a business. For an example in terms of a BI project, when the sponsor is too far removed from the project team, a business driver is helpful. The driver typically becomes responsible for the less strategic BI responsibilities. This role is usually filled by a middle manager, but possesses the same characteristics as the sponsor.

Business Intelligence (BI): A catchall term encompassing a variety of tools, applications and methodologies that enable organizations to collect data from internal systems and external sources. BI can be used to prepare data for analysis, develop and run queries, and create reports, dashboards and visualizations with the end goal of providing results to decision makers and end users.

Business Lead: A mid-to-senior level resource who understands both the business and technical side of a company well enough to communicate between the two. Within the context of a BI project, they understand the requirements, obstacles, and issues of each to make decisions on various courses of action. Additionally, this person should be highly involved in a BI project – communicating with the project manager constantly. Sometimes the business driver fills this role.

Business Owners: The business owner role needs to be filled from the business user groups by enthusiastic fans of the BI project who are also subject matter experts in their fields. Each set of business users from within the organization that will be using the BI tool (finance, operations, HR, etc.) should appoint a business owner. Business user involvement is critical and care should be taken to keep them involved from the very beginning of the project. Without business owners and users, a BI project is merely an academic technical exercise.

Business User: A user of a service or product, who may not necessarily have contact with the supplier/provider—therefore existing at the end of the data ‘supply chain’. i.e. a content management system (CMS) end user, or an accountant entering purchase orders into an enterprise resource planning (ERP) system.

Collaborative Business Intelligence, or Collaborative BI: Is the marriage of traditional business intelligence tactics with tools like social networking, wikis, or blogs, to enhance the collaborative problem-solving nature of BI. Microsoft SharePoint is an example of a popular collaborative BI product.

Cube: Multi-dimensional sections of data built from tables and fields in your database. Cubes contain calculations and formulae and are often grouped around specific business functions such as sales, finance, purchasing, inventory, etc. Each cube contains contextual, pertinent, and useful metrics for that particular area of the business.

Dashboard: Provides at-a-glance statistical analysis and historical trends of an organization’s key performance indicators (KPI’s), presented in easily digestible, graphical representations. For example, a human resources dashboard may show numbers related to staff recruitment, retention, and composition. Whereas a marketing dashboard may show numbers related to inbound web traffic, search volume, and lead velocity.

Data Architect: A practitioner of data architecture, an information technology discipline concerned with designing, creating, deploying, and managing an organization’s data architecture. This person is usually responsible for designing the Extract, Transform, and Load (ETL) process and building the structure (dimensional model) that the data will reside in after it goes through the ETL process. Data architects also tie together the required technical functionality for the BI project. Multiple skills are needed: expertise in dimensional modeling is required, as well as a deep sensitivity to the requirements of the business. Also imperative is expertise with ETL functions such as SQL Server Integration Services, and experience in conducting ETL tasks. Hardware infrastructure and supporting software skills are also necessary.

Data Architecture: A set of rules, policies, standards and models that govern and define the type of data collected and how it is used, stored, managed, and integrated within an organization and its database systems.

 

Database:In its most generic sense, a database is collection of information that is organized so that it can be accessed, managed, and updated. Usually stored in a computer or on a server, the data could be images, numeric, scripts, full-text, etc. characterizing almost any kind of information. In the context of business intelligence (BI), databases represent systems like Microsoft Dynamics, Excel, CRM, Salesforce, etc. that contain aggregations of data records or files, such as sales transactions, product catalogs and inventories, and customer profiles. Information is input and stored in the database, and a BI solution is required to get a meaningful, organized and informing format of that data out. See also relational database and multidimensional database.

Database Management System (DBMS): A computer software application that interacts with the user, other applications, and the database itself to capture and analyze data. A general-purpose DBMS is designed to allow the definition, creation, querying, update, and administration of databases.

Data Cleansing: The process of detecting and correcting faulty records, leading to highly accurate BI-informed decisions, as enormous databases and rapid acquisition of data can lead to inaccurate or faulty data that impacts the resulting BI and analysis. Correcting typographical errors, de-duplicating records, and standardizing syntax are all examples of data cleansing.

Data Feed or Live Data Feed: A mechanism for users to receive updated data from data sources. It is commonly used by real-time applications in point-to-point settings as well as on the Internet.

 

Data Intelligence: Focuses on internal data used for future endeavors and is sometimes mistakenly labelled as business intelligence. Whereas business intelligence involves organizing, rather than just gathering data to make it useful and applicable to the business’s practices, data intelligence focuses on extrapolating data to assess future services or investments.

Data Manager: Any team requires management, and a data science team is no different. On a data science or analysis team, the data manager acts as the middleperson between technical team members and strategic management. Because of this, it’s ideal that the data manager possess a technical IT background with strategic experience.

 

Data management: The process by which data is acquired, validated, stored, protected, and processed. In turn, its accessibility, reliability, and timeliness is ensured to satisfy the needs of the data users. Data management properly oversees the full data lifecycle needs of an enterprise.

Data Mining: Refers to the process of analyzing large batches of data to find patterns and instances of statistical significance. By utilizing software to look for patterns in large batches of data, businesses can learn more about their customers and develop more effective strategies for acquisition, as well as increase sales and decrease overall costs.

Data Model: Defines how data is structured, related, and standardized for the purpose of extracting meaningful insight. Businesses can utilize multiple data models (like you do with Microsoft’s Power BI) to ensure all relevant data is included.

Data Modeling: Refers to the process of defining, analyzing, and structuring data within data models.

Data Source: The source of the data. It can be a file, a particular database on a DBMS, or even a live data feed. The purpose of a data source is to gather all of the technical information needed to access the data—driver name, network address, network software, etc.—into a single place and hide it from the business user. The user should be able to look at a list that includes Payroll, Inventory, and Personnel, choose Payroll from the list, and have the BI application connect to the payroll data. This is completed without the user knowing where the payroll data resides or how the application got to it.

Data Visualization: The practice of structuring and arranging data within a visual context to help users understand it. Patterns and trends that might be unrecognizable to the layman in text-based data can be easily viewed and digested by end users with the help of data visualization software.

Data Warehouse: A large store of data drawing from a wide range of sources that can be processed, split, and analyzed to extract insights that guide management decisions. Data warehouses are typically relational databases that contain historical data and are designed for query and analysis.

Data Warehousing: The process of aggregating data from disparate sources for the purpose of building a data warehouse. Data warehousing involves design, development, testing, deployment, operations, impact analysis, and change management.