Active Directory – a directory service that Microsoft developed for Windows domain networks. It is included in most Windows Server operating systems as a set of processes and services.

Ad Hoc Reporting – The ability to create a one-off or a modified version of a BI Dashboard or report. This is typically done by a non-technical user.

Ad-hoc is Latin for “as the occasion requires.” With BI, this means users can use their reporting and analytics platform to answer their business questions “as the occasion requires,” without having to request changes from IT.

Why use Ad Hoc reporting? The goal is to empower users to ask and answer their own questions of their data, without a request to IT. The benefit of this freedom is faster turnaround on analysis, better decision making, and more efficient workflows for both the user as well as IT.

Ad Hoc Query: Got a super specific question? This is just what you’ve been looking for. This is an on demand, one-off report from your analytics software that will answer a specific question.

Analytics – the discovery, interpretation, and communication of meaningful patterns in data. Organizations use analytics to gain valuable insight and knowledge from their data, which are then used to take actions or decisions.

Analysts – Analysts are the business users who use business intelligence solutions to derive insights, make better decisions, and more accurate projections. They can be across departments in an organization, but are usually more technical than a typical business user.

Anonymization: Just what it sounds like but it happens inside a database. Links between people can be severed to maintain privacy and confidentiality and to prevent the discovery of the source of the original records.

API – An Application Programming Interface (API) is a set of subroutine definitions, protocols, and tools for building application software. In general terms, it is a set of clearly defined methods of communication between various software components.

 

Area Charts – Area Charts are a type of graphical visualization that is most commonly used to represent cumulated totals using numbers or percentages (stacked area charts in this case) over time. Use the area chart for showing trends over time among related attributes.

Augmented Analytics – Augmented analytics, leveraging advanced technology, which includes device learning and AI, is an effective tool that complements how users explore and examine information in business intelligence (BI) platforms. It automates diverse elements of data technology, device gaining knowledge of, and AI model improvement, management, and deployment, making those procedures greater efficient. This performance allows organizations to find precious insights from records in a timely way, in the end driving extra knowledgeable choice-making.

Authentication – a reporting best practice that involves the verification of a user’s identity through a user name and password. This authorization is used to grant or deny access to reports, dashboards, functionality, and more, in order to ensure the security of the data.

When used in a BI solution, authentication should be two-part:

1. Login Authentication with the BI or reporting tool with a User ID and Password
2. That User ID would be connected to a third part application that feeds data into the BI solution, which also controls security to the data

Automated Analysis: Automated analysis can be set up to find hidden insights within your data that you didn’t even know you were looking for! Or even highlight a question you didn’t even know to ask. That’s pretty BI if you ask me.

Automated Report Scheduling – Automated Report Scheduling is the process of automating a report to run at a predetermined date and time, or recurring at an interval. This allows the delivery of data to be regular and not dependent on any one person.

Balanced Scorecard: A snapshot of your organization’s performance from several different views. Management can quickly see how sales might compare to inventory levels or if current on-board talent will be able to handle to incoming sales pipeline.

Behavioral Analytics: Ever wonder what your team members are thinking about doing in the next six months? By using data about employee behavior, BI can often predict intent or even future actions. Like your own little crystal ball right there on your screen!

Benchmarking – is comparing one’s business processes and performance metrics to industry bests and best practices from other companies and industry standards. It is critical to creating corporate performance goals and targets, as well as process improvements.

Robert Camp (who wrote one of the earliest books on benchmarking in 1989) developed a 12-stage approach to benchmarking.

The 12-stage methodology consists of:

1. Select subject
2. Define the process
3. Identify potential partners
4. Identify data sources
5. Collect data and select partners
6. Determine the gap
7. Establish process differences
8. Target future performance
9. Communicate
10. Adjust goal
11. Implement
12. Review and recalibrate

BI Analyst: Are you really good at seeing patterns? Are puzzles and brain teasers your thing? Then becoming a BI Analyst might be right up your alley. BIA are responsible for watching and mining data to find patterns and mapping data from system to system to help solve problems.

Big Data – Data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Big Data presents special challenges, but when analyzed properly, can lead to insights into your business, customers, processes, and more that would typically be unrecognizable through traditional methods.

BI Governance: Are you the Key Master? Because BIG is the Gate Keeper.  This is where all permissions over who has access to your company data originate. Governance makes the determination of who sees information, how often and when the information can be accessed.

Bottlenecks: When all your systems have to continually wait on one specific point for information to flow – that my friend, is a bottleneck.

Bubble Charts – a type of visualization or chart that is used to display three dimensions of data. They are ideal to show social, economic, medical, and other scientific relationships.

Business Intelligence – Business Intelligence, also known as descriptive analytics, is the process of collecting and turning data into meaningful information used to make business decisions, also known as actionable intelligence. Business intelligence tools continue to evolve to provide more and better insights into data, which in turn, provides organizations the ability to better understand their business.

BI can be broken into many components, but some of the most common components are:

• Real-Time Reporting, which provides data about the company in a report format
• Dashboards and Visualizations,

Centralized BI: This is a BI model that governs data permissions to ensure heightened data security. Why would this be useful? It allows a group of users to share insights while seeing all the same original data known to be true to the organization.

Charts – Charts, also called graphs, are graphical representations of data, and are used to easily show relationships and correlations between large quantities of data. Since they are easy to quickly digest and understand, they are a better way to present data than a standard report. Charts are a critical part of any Business Intelligence solution.

There are many different types of charts, with the most common types being line, bar, pie, and histogram. Depending on the data to be displayed, it is important to choose the appropriate chart to best represent your data. This Wikipedia article goes over some of the various types of charts and what kind of data they are best suited for: https://en.wikipedia.org/wiki/Chart

Collaborative BI: One happy family working together and sharing knowledge across platforms. Sounds like utopia, doesn’t it? Well, it exists. It’s called Collaborative BI.

 

Collective Knowledge: We are stronger together. That is the principle behind Collective Knowledge. It’s information that benefits the entire enterprise by enrichment from the entire group, across department or across the world.

Contextual Data: Contextual data (CD) is all the extra information about a customer that helps you have a better understanding of just who they are. CD is the intelligence that is gathered from customer’s recent web activity that helps you understand them and thus better serve that customer.

Customer Relationship Management (CRM) – CRM, or customer relationship management, is a tool designed to manage and track a company’s interactions with its prospective and current customers. CRMs are designed to improve the relationship between the organization and customers, resulting in increased sales and revenue, as well as better customer service.

CSV – CSV, or Comma-Separated Values, is an unformatted output format which stores tabular data in the form of text and numbers.

 

This is one of the output types within SplashBI.

Dark Data: If a tree falls in the forest and no one is around to hear it, does it make a sound? If data is collected and isn’t put to use, does it really make a difference? Dark data is all the info that is collected and never put to use. Commonly, up to 90% of company data is dark data, collected but never utilized. Wow, if only some Business Intelligence could be put to work on that info – what insights could be gained??

Dashboards – Dashboards are compilations of charts and graphs with the goal of providing a snapshot of an organization, typically specific to a department or business process. They provide business analysts a way to monitor the health, performance, inefficiencies, and more of the organization.

The components that typically make up a dashboard are:

• Key performance indicators (KPIs), which are visual indicators of metrics that are important to a department or organization. Typically, once they are reached, a notification would be sent to necessary personnel.
• Charts and graphs containing business metrics and summaries
• Historical Trends
• Stock tickers or news feeds

Benefits of dashboards:

Dashboards hold many benefits to all levels of an organization. They provide an easy to digest way to represent data, which can lead to better, faster decisions. An effective dashboard should be able to be refreshed with the latest data available, have drill down capability to help with research, and be exportable for use in presentations or management summaries.

Database: Being able to access your data quickly and easily is paramount for a company. A database is a place / platform that many different programs can access to retrieve information quickly and efficiently.  The beauty of a database is they are usually not software specific and can be accessed by many different programs.

Data Aggregation – Data Aggregation is the compiling of information from databases with intent to prepare combined datasets for data processing.

Data Analyst: This position requires that same pattern-finding-passion that a BI Analyst needs to be successful. This person can look at a large amount of data and see areas of opportunity for a company or places where savings might be realized and acted upon.

Data Analytics: The information garnered from realized by the studying of a company’s gathered data.

Data Broker: This is a business that has found a niche in collecting personal information about individuals and then selling it to other companies to use.

Data Cleaning: Data cleaning is the process of preparing information for analysis with the help of ensuring that it is miles accurate, repeatable and usable. This includes finding and correcting errors, filling in missing values, eliminating duplicates, standardizing procedures to make the information reliable Data cleaning ensures that dataset is free of anomalies including incorrect or incomplete information, inappropriate information, formatting anomalies and is highly relevant. Basic steps in preparing records include identifying and correcting errors, normalizing facts, and returning them to normal practice. By ensuring data integrity and accuracy, companies can gain more meaningful insights from their analytics efforts.

Data Governance – is a set of processes that ensures that important data assets are formally managed throughout the enterprise to ensure the data can be trusted and that people can be made accountable for any adverse event that happens due to poor data quality.

Data Mart – A data mart is a simple form of a data warehouse that is focused on a single subject (or functional area), such as Sales or Finance or Marketing. It typically is static and needs to be refreshed as often as is required by the data consumers.

Data Mashup – The process of combining related data from multiple sources. “Mashing” data allows data consumers to have a complete picture, allowing for better, more accurate reports and dashboards.

As more and more organizations move away from a large ERP system and implement a best of breed approach, related data continues to be harder to consolidate. It is critical for organizations to have a BI solution that can mashup their data to provide a better platform for actionable intelligence.

Data Management: Data Management encompasses all activities essential for collecting, controlling, safeguarding, manipulating, and delivering statistics. It involves the usage of systems like databases, data warehouses, and facts marts, along side equipment for records series, garage, retrieval, and validation. Effective data management guarantees statistics first-rate, accuracy, and integration with various packages and analytical equipment. A sturdy records method is crucial for corporations to establish accountability and ensure records are as they should be managed and aligned with unique areas of duty. In HR or people analytics, powerful facts control is important for amassing and reading the body of workers’ statistics, permitting informed choices regarding recruitment, retention, overall performance, and overall team of workers optimization. This ensures that employees’ decisions are primarily based on dependable, accurate, and actionable insights.

Data Mapping: Data mapping is the process of relating one set of data fields to another for the purpose of accuracy, standardization or usability. It minimizes errors and ensures consistency of data, especially while carrying data migration. The main processes involved include identifying the data, designing the data flow, deploying the data, testing its accuracy, and finally managing the data maps as years roll on. In particular, HR and people analytics rely heavily on data mapping for the purpose of merging disparate datasources and making data-driven decisions.

Data Model: A data model is a plan of information that is required and how it will flow through the company. This plan is a collaborative effort between the end database users and the IT database analysts. The model must first be simply defined by the end user, what do they expect and what do they need. The next stage is for the IT team to document the process for that magic that they do to get the results the end user is wants. Together, these processes result in a data model for the business.

Data Modeling: Data modelling refers to the activities where diagrams are created to show the inflow and outflow of the data from the database. This process makes it easier to manage and retrieve data, lessens duplication among information, and affords insight into the system’s organization. This is important because it makes proper utilization of the data, avoids replication, and makes it easier to collect information and records when the database’s structure and function are being explained.

The five elements of data modelling are gathering information from stakeholders, designing a conceptual model, formulating the physical model, utilizing the model, and seeking improvement. These steps ensure that the data captured is structured well for better decisions and analysis.

Data Point: Just what it sounds like. It’s an individual item on a graph or chart.

Data Silos: Silo to house grain for the upcoming Winter = good, data silo’s that are isolated from the rest of the organization and causes data bottlenecks = bad.

Data Sources: This is where all the data originates. Solutions like SplashBI have the ability to take data from multiple sources and do a mashup so you have all the info at your fingertips.

Data Visualization – are ways to represent your data in a pictorial format, rather than a text and number report. They are typically shown as various types of charts, including line, bar, pie, heat maps, geographical mats, and more, and make understanding data trends easy to interpret and base decisions off.

Data Warehouse – A central repository for integrated data from one or more data sources. They are designed to help organizations better analyze large amounts of data, and are typically a core component of business intelligence.

Some of the major benefits of a data warehouse are:

• Maintain historical data in a centralized location, making historical reporting easier
• Integrate data from multiple sources into a centralized location
• Improve data quality
• Provide a single data model across sources
• Increase reporting and analysis performance

Descriptive Analytics – Descriptive analytics summarize data history, or tell the story of an organization. They are one of 3 major components of business analytics:

1. Descriptive Analytics tell the past story of a business
2. Predictive Analytics help us understand the future of the business based on past trends
3. Prescriptive Analytics help guide the future of the business with certain actions

Descriptive analytics are essential to BI, as predictive and prescriptive analytics aren’t possible without a solid analysis of the past. Descriptive analytics are typically represented as reports, dashboards and visualizations showing trends and status.

Drill Down – Drill down capability is essential with reporting and business intelligence, as it provides users with a more comprehensive understanding of the underlying data below the top layer.

Drill down is the capability to go from a high-level data down to transactions, balances, journals, subledgers, and more. As expected, this functionality provides many benefits

Embedded Analytics: Two heads are better than one. EA is the integration of reporting and analytics in one BI solution. Users can access all their data without having to leave their BI platform.

End User – is the person for whom a software is designed for. In other words, end users are the people who use the product.

ERP – Enterprise resource planning (ERP) is the integrated management of core business processes, often in real-time and mediated by software and technology. These business activities can include:

1. product planning, purchase
2. production planning
3. manufacturing or service delivery
4. marketing and sales
5. materials management
6. inventory management
7. shipping and payment
8. finance

Excel Hell: And yes, I think it consists of seven levels (or at least it can feel that way). This happens when a company’s servers become full of thousands of unnecessary copies of data. Documents get shared and re-shared and re-shared and no one is certain which version is the most recent or the original version.

Federated BI: This is the opposite of Centralized BI. This is when a company’s network is segmented and people work on separate isolated desktops, thus creating data silo’s. This causes unnecessary copies of documents and a real mess when trying to determine the most resent or original version of a specific document.

Filter: A filter is an amazing tool when you are trying to drill down to very specific data. You can filter out any unwanted information, or have the database only search for the detailed info you are looking for – an example might be you need to find customers between 50 and 65 who love to travel and have been out of the country at least once in the last year. Yes, depending on your data and filters applied, you can get that specific and really target customers with personalized marketing efforts.

Funnel Chart – a type of chart used to show to continuous reduction of data as it passes from one phase to another, often used in displaying the stages of a sales process.

Gap Analysis: Would you like to see your sales forecast for the next year, your budget performance over the last five years, and your company’s staff trajectory for the next six months?? This would all be amazing info to have at your fingertips – but, do you have the data in your database to support these reports? That is where a gap analysis is needed. A GA will let you know what info is needed to make your reporting dreams a reality.

Geo-analytic Capabilities: This is the ability for a BI platform to take your geo-targeted data and reflect that information onto maps on the user’s dashboard.

Infographics: Visual representations of data that drive engagement and are easy to understand.

Insights: This is the good stuff. Insights are when your analysis of data help you see and realize situations that can benefit your business. A better understanding of your current business climate could help you re-direct your thinking or re-evaluate your goals. Data is important, but insights are like the cherries on top of your sundae.

Key Performance Indicator (KPI): A well-defined KPI acts as a guide post for your business. KPI’s are specific targets that are set by a company and then scored against. This strategy gives the team a clear set of markers of what is expected of them as well as a quick gauge of how the business is doing vs. what has been planned and expected.

Live Data – otherwise known as ‘real-time data’, is data that is collected and immediately ready for analysis; there is no delay in availability of this data.

Metadata: Metadata is literally “data about data”. Intriguing, isn’t it? So, if I have a photo on my website, that is data. The name of the photo, the file size, where it’s stored, the picture’s resolution, who was the last team member to open that picture, etc.- that is metadata.

Metrics: The way a company measures its performance. These are the numbers, the brass tax – when it’s all said and done, these are the numbers that will show progression and evaluate trends for a company.

Modern BI: This is the next step for BI. This is an approach using state of the art technology from a centralized platform. The next step in BI is more intuitive reporting, self-service on-demand custom reports and less involvement from the IT team to create predefined data modules. The future is bright and SplashBI is right there leading the technology charge.

Multipolar Analytics: A big data model where data is stored and analyzed in different areas of the company instead of being centrally located and analyzed.

Online Analytical Processing (OLAP): OLAP is powerful data discovery software that views the data in a multidimensional database rather than the standard relational database. What does that mean and why should you care? Processing data in a multidimensional platform allows the user to compare and see their information in more intuitive and dynamic ways. Comparing and contrasting items in a 1-step report from different regions and different time periods with different filters were all but impossible in a relational database – but with the OLAP, it’s a snap. Seeing your data in a new and intuitive way is what OLAP is all about.

Oracle – is a database software used for data management, business intelligence, and data analysis.

Predictive Analytics – is the practice of retrieving information from existing data to analyze patterns and predict future outcomes/trends.

Query – a request for information from a database.

Scalability: What good is technology if it can’t grow with you? Scalability is the ability of your software to expand and serve a larger number of users as your business expands.

Schema: This is your outline for your company or business. A visual plan for how your company works and functions.

Self-Service BI: This is the ability for users to access and work with data even though they don’t have an analyst or computer science background. They can access all the features of the software and create reports and model the data to their needs. This type of BI must be centralized and governed by a good IT department.

Slice & Dice: Tools within your spreadsheet software that allow you to view data from any angle.

Smart Data: These are segments from Big Data that are valuable to the company and are easily turned into actionable data.

Snapshot: Great – Hold it right there! You look Mah-ve-lous!  A snapshot is a view of specific data at a particular moment in time.

Social BI: Won’t you be my neighbor? Social BI is all about incorporating social media capabilities into your BI platforms so that users can communicate and share insights.

Social Enterprise: Wouldn’t you like to know about a problem BEFORE it affected your bottom line?? Social enterprise utilizes a new level of social connectivity. It allows for a more efficient operation where problems are uncovered and fixed before they have time to affect your revenue streams.

Standard Query Language (SQL): This is the language of your IT team. The way your programmers communicate and is used in programming for managing relational databases.

Structured Data – refers to data that is highly organized and easily searchable in a database.

Suggestive Discovery Engine – It knows you better than your bestie! It’s the power behind the program that shows the user the most relevant insights to focus on, based on their personal preferences and behavior.

Unstructured Data – refers to data that is not contained in a database, or any other structure.

User Interface – otherwise known as UI, is the visual part of a computer application or operating system. This includes screens, keyboards, and much more.

Workboards – This is like a supped-up dashboard – but better. It’s an interactive visualization tool that shows you all your data with the possibility to work directly on it in order to do further analysis.