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• 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

• Definitions to Get Started

  • noisy data
  • Google Analytics
  • data exploration
  • decision-making process

  • edge analytics
  • ad hoc analysis
  • data mining
  • data sampling

  View All Definitions

• 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

• 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

• 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

• 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

• 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

• business analytics

  Business analytics (BA) is the iterative, methodical exploration of an organization's data, with an emphasis on statistical analysis.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