Data is a big part of modern business success. It’s used to create reports, predict earnings, and factors into every decision that drives a company’s growth and profits. Learn the meaning of some of the most common data terms to gain a better understanding and create greater value for your business.
Artificial Intelligence (AI)
A machine’s ability to perform functions that mimic a human mind’s capabilities. The machine learns by example and improves its capabilities with additional experience. This includes the ability to understand language, respond appropriately, recognize objects, find solutions to pressing problems, and make decisions.
Augmented Intelligence
The collaboration of artificial intelligence and human input to boost performance and make better decisions.
Business Intelligence (BI)
Typically refers to data analysis and reporting. Business intelligence organizes data in useful ways to extract value, insights, and improve the decision-making process.
Collective Intelligence
A group working together to complete tasks and find solutions to complex problems through both collaboration and competition.
Data Consumption
Evaluates data to glean insights and meaning that are used to spur action. Data consumption can also refer to the amount of data uploaded or downloaded to a mobile device using a mobile data plan.
Data Culture
How an organization as a whole regards its data practices. This includes how data is gathered, shared, and used.
Data Engineering
The first step in the process of gathering and using data in valuable ways. Data engineering refers to all aspects of identifying relevant data sources, compiling and organizing it, and storing the data.
Data Fabric
The data services used to manage data stored within the cloud and onsite. It standardizes management practices for better consistency and efficiency.
Data Governance
The internal standards and practices used by a business or the stakeholders of a given organization to monitor information needs. Data governance sets the rules that of how data is collected, processed, stored, and even disposed of. It also decides which types of data are kept under governance, and who is allowed access to various kinds of data.
Data Literacy
Date literacy refers to the ability to effectively understand and communicate the information found in data collected from various sources through reading and writing.
Data Mining
Various methods used to identify patterns found in a large amount of data and then extracting the relevant information from the data set to generate original information or make predictions.
Data Orchestration
Data Orchestration is the process of combining and organizing data taken from multiple locations to use in data analysis and automated decision-making.
Data Science
The use of advanced analytics techniques from multiple fields to pinpoint and extract the most valuable and relevant bits of data from large data sets. The information is then used in various planning and decision-making capacities.
Data Storytelling
Using data and visual representations to form a narrative. It is used to explain the meaning of the findings in a more compelling and effective way to the audience.
Data Virtualization
A data management approach that enables an application to find, change, and share data without needing to know the formatting and technical details. One common application is photo sharing on social media.
Data Visualization
A type of information design where data is presented in a visual manner through the use of maps, charts, and graphs. Data visualization makes it easy to review the information and make decisions based on the main points.
Data Warehouse
The central hub where all data is stored. Information is compiled and used to perform data analysis, create reports, and help inform decisions.
Deep Learning
Uses artificial neural networks that mimic a human brain’s structure. It processes large unstructured data sets to locate patterns.
Decision Intelligence
Applies data science techniques to a problem to transform information contained in a data set into decisions on a larger scale.
Decision Support Systems
The data and required computer software and programs that are needed to conduct analysis and drive decision-making.
Descriptive Analytics
Using business intelligence along with data visualization to examine data and determine precisely what has happened or is happening. Oftentimes, it includes the use of historical data to make comparisons.
Diagnostic Analytics
Identifies why something happened by using advanced analytics. Various techniques such as machine learning, data mining, and statistics are all employed to get a clear understanding of the data in question.
Fuzzy Logic
A computing technique that mimics the way a human brain examines available data to form a partial truth without 100% certainty of the correct answer. It’s most commonly used in language processing, but fuzzy logic is becoming more widely used in other disciplines that gather, analyze, and report on data findings.
Gamification
Uses the concepts found in games, like rules, competition, and point scoring, to collect and analyze data. It’s also an effective motivating technique.
Information Design
The way that information learned from data is presented to others. The approach to information design differs by the situation. The method that delivers the results of the findings in the most effective way to further understanding to large and small groups of people can include reports, statistics, or visuals like charts and graphs.
Machine Learning
This is a specific disciple of artificial intelligence where the machine is able to learn from experience and improve over timing without external influence, instructions, or additional programming. Algorithms and other models of analysis allow the machine to draw conclusions from patterns to learn and adapt on its own.
Management Science
Combines and studies problem-solving techniques and decision-making skills. This practice allows an organization to make better-informed decisions about company performance and growth.
Mashup
Merging multiple sets of data or functions from two or more sources into one application to expand their usefulness.
Metadata
This refers to concise data used to summarize and describe larger data sets. Metadata simplifies the ability to locate particulate data faster when it is needed in a project.
MIS Reporting
Management Information Systems provides the necessary information required for daily business tasks. It also closely monitors progress through descriptive reporting.
Pattern Recognition
The use of an algorithm to find recurrences within data or similarities in multiple data sets. The information found is then used to make informed decisions or reach conclusions to questions posed about the data.
Predictive Analytics
Uses advanced analytics on data sets to predict the likely outcome. Predictive analytics uses types of artificial intelligence like machine learning.
SaaS
Software-as-a-Service uses the internet to run and use various applications. The data is saved and shared within the cloud, making it easy to collaborate and work from any device and location.
Sentiment Analysis
Tracking consumer emotions, preferences, and opinions gathered from customer service calls, emails, social media, surveys, and more. The information is reviewed to determine the public’s feelings about a business, product, or service.
Social Science
The study of individual and group relationships within society is used to get insight into the behavior patterns of people. This is gaining popularity in data and business as a way to use the information to make informed business decisions that drive growth and success by attracting and keeping customers.
These data terms span multiple disciplines that work together to make the most out of your company’s data. Getting familiar with these terms is the first step to mastering the basics of these areas. Understanding the fundamentals is the best way to get a solid foundation to build upon.
