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Bring yourself up to speed with our introductory content.
data visualization
Data visualization is the practice of translating information into a visual context, such as a map or graph, to make data easier for the human brain to understand and pull insights from. Continue Reading
noisy data
Noisy data is a data set that contains extra meaningless data. Almost all data sets will contain a certain amount of unwanted noise. Continue Reading
Google Analytics
Google Analytics is a web analytics service that provides numerous analytical tools for marketing purposes. Continue Reading
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Generative AI capabilities increase data analytics value
GenAI can enhance data analytics uses. Automation and synthetic data let data analysts generate better quality insights more quickly and cost-efficiently than ever before. Continue Reading
data exploration
Data exploration is the first step in data analysis involving the use of data visualization tools and statistical techniques to uncover data set characteristics and initial patterns. Continue Reading
decision-making process
A decision-making process is a series of steps one or more individuals take to determine the best option or course of action to address a specific problem or situation.Continue Reading
edge analytics
Edge analytics is an approach to data collection and analysis in which an automated analytical computation is performed on data at a sensor, network switch or other device instead of waiting for the data to be sent back to a centralized data store.Continue Reading
ad hoc analysis
Ad hoc analysis is a business intelligence (BI) process designed to answer a specific business question by using company data from various sources.Continue Reading
data mining
Data mining is the process of sorting through large data sets to identify patterns and relationships that can help solve business problems through data analysis.Continue Reading
data sampling
Data sampling is a statistical analysis technique used to select, manipulate and analyze a representative subset of data points to identify patterns and trends in the larger data set being examined.Continue Reading
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self-service analytics
Self-service analytics is a type of business intelligence (BI) that enables business users to access, manipulate, analyze and visualize data, as well as generate reports based on their discoveries.Continue Reading
in-memory analytics
In-memory analytics is an approach to querying data residing in a computer's random access memory (RAM) as opposed to querying data stored on physical drives.Continue Reading
sentiment analysis (opinion mining)
Sentiment analysis systems help organizations gather insights into real-time customer sentiment, customer experience and brand reputation.Continue Reading
big data analytics
Big data analytics is the often complex process of examining big data to uncover information -- such as hidden patterns, correlations, market trends and customer preferences -- that can help organizations make informed business decisions.Continue Reading
advanced analytics
Advanced analytics is a broad category of inquiry that can be used to help drive changes and improvements in business practices.Continue Reading
Generative AI won't replace data analysts
Generative AI isn't going to replace data analysts. It can help analysts be more effective, but it lacks human insights and knowledge to properly do the job.Continue Reading
business intelligence dashboard
A business intelligence dashboard, or BI dashboard, is a data visualization and analysis tool that displays on one screen the status of key performance indicators (KPIs) and other important business metrics and data points for an organization, ...Continue Reading
self-service business intelligence (self-service BI)
Self-service business intelligence (BI) is an approach to data analytics that enables business users to access and explore data sets even if they don't have a background in BI or related functions such as data mining and statistical analysis.Continue Reading
process intelligence (business process intelligence)
Process intelligence is data that has been systematically collected to analyze the individual steps within a business process or operational workflow.Continue Reading
ensemble modeling
Ensemble modeling is the process of running two or more related but different analytical models and then synthesizing the results into a single score or spread in order to improve the accuracy of predictive analytics and data mining applications.Continue Reading
business intelligence architecture
A business intelligence architecture is a framework for the various technologies an organization deploys to run business intelligence and analytics applications.Continue Reading
heat map (heatmap)
A heat map is a two-dimensional representation of data in which various values are represented by colors.Continue Reading
association rules
Association rules are 'if-then' statements, that help to show the probability of relationships between data items, within large data sets in various types of databases.Continue Reading
8 ways to drive business value with advanced analytics
It can be difficult to get buy-in for analytical operations. These eight bottom-line benefits of data analytics -- with real-world examples -- can win over execs.Continue Reading
social analysis
Social analysis is the practice of systematically examining a social problem, issue or trend, often with the aim of prompting changes in the situation being analyzed.Continue Reading
text mining (text analytics)
Text mining is the process of exploring and analyzing large amounts of unstructured text data aided by software that can identify concepts, patterns, topics, keywords and other attributes in the data.Continue Reading
customer analytics (customer data analytics)
Customer analytics, also called customer data analytics, is the systematic examination of a company's customer information and behavior to identify, attract and retain the most profitable customers.Continue Reading
key performance indicators (KPIs)
Key performance indicators (KPIs) are quantifiable business metrics that corporate executives and other managers use to track and analyze factors deemed crucial to the success of an organization.Continue Reading
4 types of simulation models used in data analytics
Combining different types of simulation models with predictive analytics enables organizations to forecast events and improve the accuracy of data-driven decisions.Continue Reading
Examples of real-time analytics for businesses
Organizations use real-time analytics and automation to be more efficient and effective, whether it's in retail, healthcare, manufacturing or other verticals.Continue Reading
business intelligence (BI)
Business intelligence (BI) is a technology-driven process for analyzing data and delivering actionable information that helps executives, managers and workers make informed business decisions.Continue Reading
asset turnover ratio
The asset turnover ratio is a measurement that shows how efficiently a company is using its owned resources to generate revenue or sales.Continue Reading
Decision intelligence changes operations across industries
Decision intelligence speeds up the process of delivering data to decision-makers efficiently, improving operations across industries including retail, healthcare and trucking.Continue Reading
operational intelligence (OI)
Operational intelligence (OI) is an approach to data analysis that enables decisions and actions in business operations to be based on real-time data as it's generated or collected by companies.Continue Reading
Data analytics pipeline best practices: Data classification
Data analytics pipelines collect a variety of data categories requiring efficient data organization. These data classification best practices can help improve pipeline performance.Continue Reading
named entity
In data mining, a named entity is a phrase that clearly identifies one item from a set of other items that have similar attributes.Continue Reading
Data science quiz: Test your knowledge
Interested in becoming a data scientist? This short quiz will test what you know about the data science process, required skills and how data scientists do their jobs.Continue Reading
Data analytics pipeline best practices: Data governance
Data analytics pipelines bring a plethora of benefits, but ensuring successful data initiatives also means following best practices for data governance in analytics pipelines.Continue Reading
citizen data scientist
A citizen data scientist is an individual who does some data science work for an organization but doesn't hold the title of data scientist or have a formal background in advanced analytics, statistics or related disciplines.Continue Reading
8 steps to improve data visualization literacy
Data visualization literacy is a crucial element of analytics that helps communicate findings. These eight steps can help improve an organization's data visualization literacy.Continue Reading
Natural language processing augments analytics and data use
Natural language processing brings new tools to organizations to democratize data across the userbase in a simple, easy manner, but faces challenges with the nuances of language.Continue Reading
How self-service BI capabilities improve data use
Organizations can be more efficient problem solvers and enable users with self-service BI capabilities that bring more data and tools to their fingertips.Continue Reading
real-time business intelligence (RTBI)
Real-time business intelligence (RTBI) combines data analytics and various data processing tools to enable access to the most recent, up-to-the-minute relevant data and visualizations.Continue Reading
MicroStrategy
MicroStrategy is an enterprise business intelligence (BI) application and software vendor.Continue Reading
Sound business process architecture requires key traits
Business processes require a coherent enterprise-level architecture. In this excerpt from his new book, Roger Burlton identifies key traits business processes should share.Continue Reading
processing in memory (PIM)
Processing in memory, or PIM (sometimes called processor in memory), refers to the integration of a processor with Random Access Memory (RAM) on a single chip.Continue Reading
Tableau dashboard tips and tricks from an expert author
In this excerpt from his new book on using Tableau CRM, data expert Mark Tossell takes readers step by step through best practices for dashboard creation.Continue Reading
Data preparation in machine learning: 6 key steps
Trustworthy analytics outcomes depend on the right data, requiring data scientists to focus on these steps when they prepare data for use in machine learning applications.Continue Reading
logistic regression
Logistic regression is a statistical analysis method to predict a binary outcome, such as yes or no, based on prior observations of a data set.Continue Reading
Predictive analytics in marketing: Achieving success
The use of predictive analytics in marketing is transforming how companies sell to customers, but the learning curve can be steep. Here's what you need to know to be successful.Continue Reading
5-step predictive analytics process cycle
A viable predictive model that yields valuable outcomes requires a methodical team approach to goal-setting, data integrity and model development, deployment and validation.Continue Reading
Predictive analytics in healthcare: 12 valuable use cases
Predictive analytics' increasingly invasive presence in a host of healthcare applications yields more personalized patient care, earlier interventions and reduced hospital costs.Continue Reading
What is embedded analytics, and how does it benefit BI?
Here are the benefits of data managers using embedded analytics capabilities to use interactive dashboards and reporting techniques within existing business applications.Continue Reading
Qlik
Qlik is a software vendor specializing in data visualization, executive dashboards and self-service business intelligence products.Continue Reading
standard operating procedure (SOP)
A standard operating procedure (SOP) is a set of written instructions that describes the step-by-step process that must be taken to properly perform a routine activity.Continue Reading
business analytics
Business analytics (BA) is the iterative, methodical exploration of an organization's data, with an emphasis on statistical analysis.Continue Reading
SAS Institute Inc.
SAS Institute Inc. is a software vendor that specializes in advanced and predictive analytics software applications, as well as business intelligence and data visualization offerings.Continue Reading
data science as a service (DSaaS)
Data science as a service (DSaaS) is a form of outsourcing that involves the delivery of information gleaned from advanced analytics applications run by data scientists at an outside company to corporate clients for their business use.Continue Reading
How to develop a data literacy program in your organization
This handbook examines the need for data literacy in organizations and offers advice on boosting the data literacy skills of self-service BI and analytics users.Continue Reading
14 most in-demand data science skills you need to succeed
The demand for data scientists continues to grow, but the job requires a combination of technical and soft skills. Here are 14 key skills for effective data scientists.Continue Reading
Quiz: Big data analytics technologies and techniques
Big data analytics technologies and techniques can help you gain valuable business insights. Take this quiz to test your knowledge of big data analytics tools and best practices.Continue Reading
Successful digital transformation strategies start with data
Data has become a natural resource essential to business ops. Companies need to consider four digital transformation elements when developing strategies to harness data.Continue Reading
social media analytics
Social media analytics is the process of collecting and analyzing audience data shared on social networks to improve an organization's strategic business decisions.Continue Reading
analytic database
An analytic database, also called an analytical database, is a read-only system that stores historical data on business metrics such as sales performance and inventory levels.Continue Reading
Top 5 benefits of augmented analytics
Learn how augmented analytics helps make companies more agile by expanding employee access to data and speeding the decision-making process -- all while saving costs.Continue Reading
Consider unique business needs when hiring a data scientist
The experience and educational requirements common to data scientist roles have changed rapidly in recent years. Here's how to hire the right data scientist for your business.Continue Reading
Citizen data scientist training for augmented analytics
Augmented analytics have made data analysis more accessible and led to the rise of citizen data scientists. But using these tools still require extensive training.Continue Reading
The next generation data scientist
The data scientist role continues to evolve as AI takes hold and domain knowledge becomes paramount. Explore the skills and expectations required for the future data scientist.Continue Reading
unstructured data
Unstructured data is information, in many different forms, that doesn't follow conventional data models, making it difficult to store and manage in a mainstream relational database.Continue Reading
web analytics
Web analytics is the process of analyzing the behavior of visitors to a website.Continue Reading
6 essential big data best practices for businesses
These best practices can help businesses put their big data strategy on the right track to meet analytics needs and produce the expected business benefits.Continue Reading
8 big data use cases for businesses and industry examples
As the understanding of big data requirements increases, so do big data use cases. Here are eight ways businesses are using big data to improve operations.Continue Reading
12 must-have features for big data analytics tools
Searching for a big data analytics tool for your organization? Here are 12 key features to look for during the software evaluation and selection process.Continue Reading
A look at the DataOps engineer role and responsibilities
DataOps engineers and data engineers are often conflated, but they have separate, distinct responsibilities. Take a look at what differentiates the emerging role.Continue Reading
What analytics leaders need to know about graph technology
Enterprise graph analytics adoption has been trending recently and is only expected to grow. Gartner analyst Mark Beyer explores what you need to know about graph technology.Continue Reading
Python code formatting: Tools you need and why it matters
Computers don't care about the style of your code, so why should you? See what Al Sweigart has to say about code formatting, and get a sneak peek at his new book.Continue Reading
10 BI dashboard design principles and best practices
BI dashboards are a key tool for delivering analytics data to business users. Here's how to design effective dashboards that can help drive informed decision-making.Continue Reading
Key differences in uses of DataOps vs. DevOps
As with DevOps, DataOps hinges on cooperation between teams and breaking down silos within an organization with the focus of implementing and maintaining a data architecture.Continue Reading
Big data streaming platforms empower real-time analytics
Data streaming processes are becoming more popular across businesses and industries. Read on to see how streaming platform adoptions are benefiting enterprises.Continue Reading
What makes up a strong data science team structure?
Enterprises rely on a strong data science team to get the most from their data. Read on to find out what talents you'll need to employ to support your organization's data.Continue Reading
Data scientist vs. data analyst: Comparing the 2 data roles
The differences can be subtle, but in general, a data scientist has more responsibilities and a more advanced background than analyst counterparts.Continue Reading
What are the key BI team roles and responsibilities?
A business intelligence team helps an organization deploy, manage and use BI tools. Here are the primary roles on a BI team and an overview of their responsibilities and duties.Continue Reading
Important resources in a data scientist education
There are plenty of resources for data science learning for people entering the field all the way up to managers. Read on for key resources and an excerpt from a new book on data science skills.Continue Reading
Top data visualization techniques and how to best use them
BI and analytics teams and self-service BI users can choose from various types of data visualizations. Here are examples of 12, with advice on when to use them.Continue Reading
7 steps to create a modern business intelligence strategy
Business intelligence can boost performance and create competitive advantages for companies. Here are seven steps to take in implementing an effective BI strategy.Continue Reading
What does a business intelligence analyst do?
Business intelligence analysts are key members of BI teams who analyze data, create dashboards and handle other duties. Here's a look at the job and the skills it requires.Continue Reading
Data science interview preparation: How to answer top questions
A successful interview for a data scientist position relies on the ability to effectively communicate and demonstrate your combined experiences and skills.Continue Reading
4 features of great data visualization and storytelling
Data visualization and storytelling go hand in hand when it comes to explaining data. Here are four ways to make sure you build and tell a strong data story for your audience.Continue Reading
Top embedded analytics examples in enterprise applications
Embedded analytics has been trending for ease of use and accessibility for users. Here are the top use cases for these tools in enterprise applications.Continue Reading
Embedded BI software creates common ground for diverse analytics
Learn how embedding separate business intelligence capabilities into one application empowers users to drill down, access and analyze data without opening a separate tool.Continue Reading
Data scientist vs. data analyst: What's the difference?
Data scientists and data analysts have a lot of crossover in their roles, but they're certainly not the same. Here's a look at some key differences in the positions.Continue Reading
Augmented analytics tools: Business uses, benefits and barriers
This guide examines the capabilities and potential benefits of augmented analytics technologies and offers guidance on how to use them to simplify BI and analytics processes.Continue Reading
days sales outstanding (DSO)
Days sales outstanding (DSO) is the measurement of the average number of days it takes a business to collect payments after a sale has been made.Continue Reading
NLP uses in BI and analytics speak softly but carry a big stick
Self-service analytics vendors are adding NLP features to their tools to make them even easier to use. Learn about notable NLP applications as well as some caveats.Continue Reading
Ethical data mining and analytics elude privacy, usage snafus
This handbook examines the ethics of data mining and offers advice on missteps to avoid when mining and analyzing customer data to help drive marketing campaigns.Continue Reading
5 augmented analytics examples in the enterprise
Here are the top examples of augmented analytics uses that BI vendors support and enable, including data preparation, NLP-based querying and automated insights.Continue Reading
Augmented data analytics overshadows traditional BI process
Automated functionality is being added to BI software to help users find and analyze data. This handbook looks at the benefits and challenges of augmented analytics.Continue Reading
How to integrate Power BI and SharePoint via embedded reports
Expert Brien Posey explains two methods for including Power BI reports on pages in SharePoint Online's cloud service: publishing a link to a report, or embedding one.Continue Reading
Data-rich organizations turn focus to ethical data mining
As data analytics has increasingly become a core component of organizations' strategies, concerns have arisen around how data is mined. Experts offer tips.Continue Reading
Qlik Research head talks Associative Engine, NLP and Data Swarm
Elif Tutuk, research head at Qlik, discusses projects her team is working on -- including a smarter Associative Engine, multi-attribute visualizations and NLP.Continue Reading