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• How to get a job as a data scientist: It starts with skills

  Data science offers many professional opportunities in data-driven organizations. Balance education and experience to present yourself as an attractive data-savvy candidate. Continue Reading

• What is days sales outstanding (DSO)?

  Days sales outstanding (DSO) measures the average time it takes a business to collect payments after a sale. Continue Reading

• What is big data analytics?

  Big data analytics is the 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

• What is a business intelligence analyst and what do they do?

  Business intelligence analysts are key members of BI teams. Here's a look at the job, the skills it requires, salary levels and how the role of a BI analyst is evolving. Continue Reading

• How to implement a business intelligence project in 7 steps

  The BI implementation process involves a series of steps that organizations need to take to ensure projects are completed successfully and meet business goals. Continue Reading

• Definitions to Get Started

  • What is days sales outstanding (DSO)?
  • What is big data analytics?
  • What is continuous intelligence?
  • What is embedded analytics?

  • What is composable analytics?
  • What is a metrics store?
  • What is decision intelligence?
  • What is a business intelligence dashboard (BI dashboard)?

  View All Definitions

• Big data analytics and business intelligence: A comparison

  BI and big data analytics support different types of analytics applications and using them in complementary ways enables a comprehensive data analysis strategy.Continue Reading

• How to build an effective business intelligence strategy

  Explore the eight steps organizations need to develop and implement a successful business intelligence strategy, as well as some of the challenges staff can encounter.Continue Reading

• What is continuous intelligence?

  Continuous intelligence (CI) is a data analysis process that delivers real-time analytics and insights as a part of ongoing business operations.Continue Reading

• What is embedded analytics?

  Embedded analytics is a data analysis technology that puts analytics capabilities within a business software application, platform or web portal instead of a separate tool.Continue Reading

• What is composable analytics?

  Composable analytics is an approach to data analytics and business intelligence (BI) that lets an organization combine modular components from different data and analytics tools.Continue Reading

• AI in business intelligence: Uses, benefits and challenges

  AI tools are becoming a key part of BI systems, both to add new analytics capabilities and simplify tasks. Here's what you need to know about using AI in the BI process.Continue Reading

• What is a metrics store?

  A metrics store -- also sometimes referred to as a metrics layer, metrics platform or headless business intelligence (BI) -- is a centralized repository for metrics definitions, including the calculations of metrics.Continue Reading

• Traditional BI vs. self-service BI: Differences and uses

  Traditional and self-service business intelligence are different approaches to BI initiatives. Here's what you need to know to decide which is right for your BI needs.Continue Reading

• What is decision intelligence?

  Decision intelligence is an emerging field that combines data science, artificial intelligence (AI) and decision theory to improve organizational decision-making.Continue Reading

• What is a business intelligence dashboard (BI 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

• Will generative AI replace data analysts?

  Generative AI isn't going to replace data analysts. It can help analysts be more effective, but GenAI lacks human insights and knowledge to properly do the job.Continue Reading

• What is real-time business intelligence (RTBI)?

  Real-time business intelligence (RTBI) combines data analytics and various data processing tools to enable access to the most relevant, up-to-the-minute data and visualizations.Continue Reading

• What is self-service business intelligence (self-service BI)?

  Self-service business intelligence (BI) is an approach to data analytics that enables nontechnical business users to access and explore data sets.Continue Reading

• 7 skills for improved data visualizations

  Great data visualizations require a combination of analytics, design and communication. Master seven key skills for data visualizations to effectively communicate data insights.Continue Reading

• Data literacy training requires a dual approach

  Data literacy training is a flexible learning process. Organizations can tailor training for their teams, but individuals can find educational resources to better their skills.Continue Reading

• What is 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

• What is business intelligence architecture (BI architecture)?

  A business intelligence architecture is a framework for the various technologies an organization deploys to run business intelligence and analytics applications.Continue Reading

• 6 key steps form a data-driven decision-making framework

  A data-driven decision-making framework provides guidelines that any organization or individual can use. Improve decision-making on a professional and personal level.Continue Reading

• What is sentiment analysis?

  Sentiment analysis, also referred to as 'opinion mining,' is an approach to natural language processing (NLP) that identifies the emotional tone behind a body of text.Continue Reading

• You need analytics governance

  Analytics governance might not seem exciting, but it can improve innovation and mitigate risks. It's also critical to responsible data management and analytics practices.Continue Reading

• What is 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

• What is a key performance indicator (KPI)? Strategy and guide

  Key performance indicators (KPIs) are quantifiable business metrics that corporate executives, managers and other stakeholders use to track and analyze factors deemed crucial to meeting the organization's stated objectives.Continue Reading

• 6 key features of embedded analytics software

  Data-driven organizations need employees of all technical skill levels to be able to access and use data. Embedded analytics software has six features that make data more usable.Continue Reading

• standard operating procedure (SOP)

  A standard operating procedure is a set of step-by-step instructions for performing a routine activity.Continue Reading

• R programming language

  The R programming language is an open source scripting language for predictive analytics and data visualization.Continue Reading

• web analytics

  Web analytics is the process of analyzing the behavior of visitors to a website by tracking, reviewing and reporting the data generated by their use of the site and its components, such as its webpages, images and videos.Continue Reading

• operational efficiency

  Operational efficiency refers to an organization's ability to reduce waste of time, effort and material while still producing a high-quality service or product.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.Continue Reading

• association rules

  Association rules are if-then statements that show the probability of relationships between data items within large data sets in various types of databases.Continue Reading

• business analytics

  Business analytics (BA) is a set of disciplines and technologies for solving business problems using data analysis, statistical models and other quantitative methods.Continue Reading

• logistic regression

  Logistic regression, also known as a logit model, 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

• 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

• 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

• 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

• 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

• 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

• 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

• heat map (heatmap)

  A heat map is a two-dimensional representation of data in which various values are represented by colors.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

• 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

• 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

• 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

• 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

• What is Qlik?

  Qlik is a software vendor specializing in data visualization, executive dashboards and self-service business intelligence products.Continue Reading

• What is 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

• What is 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

• 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

• 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