The difference between Machine Learning (ML) and Artificial Intelligence (AI)

Cloud ML:

The Cloud ML Engine is a hosted platform to run machine learning training jobs and predictions at scale. The service can also be used to deploy a model that is trained in external environments. Cloud ML Engine automates all resource provisioning and monitoring for running the jobs.

The cloud makes intelligent capabilities accessible without requiring advanced skills in artificial intelligence or data science. AWS, Microsoft Azure, and Google Cloud Platform offer many machine learning options that don’t require deep knowledge of AI, machine learning theory, or a team of data scientists.

  • The cloud’s pay-per-use model is good for bursty AI or machine learning workloads.
  • The cloud makes it easy for enterprises to experiment with machine learning capabilities and scale up as projects go into production and demand increases.
  • The cloud makes intelligent capabilities accessible without requiring advanced skills in artificial intelligence or data science.
  • AWS, Microsoft Azure, and Google Cloud Platform offer many machine learning options that don’t require deep knowledge of AI, machine learning theory, or a team of data scientists.

Cloud AI:

The AI cloud, a concept only now starting to be implemented by enterprises, combines artificial intelligence (AI) with cloud computing. An AI cloud consists of a shared infrastructure for AI use cases, supporting numerous projects and AI workloads simultaneously, on cloud infrastructure at any given point in time.

Artificial intelligence (AI) assists in the automation of routine activities within IT infrastructure, which increases productivity. The combination of AI and cloud computing results in an extensive network capable of holding massive volumes of data while continuously learning and improving.

  • Data Mining.
  • Agile Development.
  • Reshaping of IT Infrastructure.
  • Seamless Data Access.
  • Analytics and Prediction.
  • Cloud Security Automation.
  • Cost-Effective.
Cloud MLCloud AI
The Cloud ML Engine is a hosted platform to run machine learning training jobs and predictions at scale.An AI cloud consists of a shared infrastructure for AI use cases, supporting numerous projects and AI workloads simultaneously, on cloud infrastructure at any given point in time.
The service can also be used to deploy a model that is trained in external environments. Cloud ML Engine automates all resource provisioning and monitoring for running the jobs.Enterprises use the power of AI-driven cloud computing to be more efficient, strategic, and insight-driven. AI can automate complex and repetitive tasks to boost productivity, as well as perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows.
The pay-per-use model further makes it easy to access more sophisticated capabilities without the need to bring in new advanced hardware.Cloud AI Platform is a service that enables user to easily build machine learning models, that work on any type of data, of any size.
This storage service provides petabytes of capacity with a maximum unit size of 10 MB per cell and 100 MB per row. 1024 Petabytes of data.1024 Petabytes of data. The larger the RAM the higher the amount of data it can handle hence faster processing. 16GB RAM and above is recommended for most deep learning tasks.
High Flexibility and Cost Effective.Seamless Data Access. High Flexibility and Cost Effective.
Cloud ML Engine is used to train machine learning models in TensorFlow and other Python ML libraries (such as scikit-learn) without having to manage any infrastructure.In Artificial Intelligence, the Decision Tree (DT) model is used to arrive at a conclusion based on the data from past decisions. 
Cloud DLP – Data Loss Prevention provides tools to classify, mask, tokenize, and transform sensitive elements to help you better manage the data that you collect, store, or use for business or analytics.Cloud DLP – Data Loss Prevention provides tools to classify, mask, tokenize, and transform sensitive elements to help you better manage the data that you collect, store, or use for business or analytics.
The cloud makes intelligent capabilities accessible without requiring advanced skills in artificial intelligence or data science.  The cloud makes intelligent capabilities accessible without requiring advanced skills in artificial intelligence or data science.  
Google, Amazon, Microsoft, and IBMGoogle, Amazon, Microsoft, and IBM
ML’s aim is to improve accuracy without caring for success.

The goal of AI is to increase the chances of success.
ML is the way for the computer program to learn from experience.AI is a computer program doing smart work.
The ML’s goal is to keep learning from data to maximize the performance.The future goal of AI is to stimulate intelligence for solving highly complex programs.
ML allows the computer to learn new things from the available information.AI involves decision-making.
ML looks for the only solution.AI looks for optimal solutions.
  

ML and AI:

Even though many differences exist between ML and AI, they are closely connected. AI and ML are often viewed as the body and the brain. The body collects information, the brain processes it. The same is with AI, which accumulates information while ML processes it.

Conclusion:

AI involves a computer executing a task a human could do. Machine learning involves the computer learning from its experience and making decisions based on the information. While the two approaches are different, they are often used together to achieve many goals in different industries.

Correlation Measures The Relationship Between Two Variables

What is Correlation?

Correlation is a statistical measure that expresses the extent to which two variables are linearly related (meaning they change together at a constant rate). It’s a common tool for describing simple relationships without making a statement about cause and effect.

Why is Correlation important?

Once correlation is known it can be used to make predictions. When we know a score on one measure we can make a more accurate prediction of another measure that is highly related to it. The stronger the relationship between/among variables the more accurate the prediction.

Related Articles:

How to Calculate Correlation

Differences between Power BI, Tableau and Python Dash

Data visualization has gained massive popularity in recent years owing to the demand for data. In a business setup, these business intelligence tools can help in analyzing all the data and monitor performance to enhance growth for the firm, and productivity for the employees. With the world switching to digital means all together in the year that went by, data is now considered fuel for every small, medium, or big firm.

In such a scenario, what sounds better- a spreadsheet that mentions the date, time, sales, and profit OR a colorful, descriptive bar chart interactively explaining all the details? Our vote goes to the latter.

What is a data visualization tool?

An essential part of any business strategy, data visualization is the process of collecting data and transforming it into a meaningful visualization to support decision-making. These visualizations could be in the form of bar charts, maps, or anything that is visually appealing and interactive. They convey the information to the viewer by simply looking at them, whereas normally one needs to read spreadsheets or text reports to understand the data.

Talking of the best data visualization tools used by analysts in various industries according to their specifications and applications, they comprise Power BI, Tableau and Python Dash. All these software programs help businesses make decisions questions faster.

Power BI is a Data Visualization and Business Intelligence tool provided by Microsoft. It can collect data from different data sources like Excel spreadsheets, on-premise database, cloud database and convert them into meaningful reports and dashboards. Its features such as creating quick insights, Q&A, Embedded Report, and Self Service BI made it top among all BI tools. It is also robust and always ready for extensive modeling and real-time analytics, as well as custom visual development.

Tableau offers business analysts to take business decisions by its feature, data visualization available to all business users of any background. It can establish a connection with any data source (Excel, local/on-premise database, cloud database).

Tableau is the fastest growing Data Visualization Tool among all visualization tools. Its visualizations are created as worksheets and dashboards. The beauty of tableau is that it does not require any technical or programming knowledge to create or develop reports and dashboards.

Python Dash

Dash is a python framework created by plotly for creating interactive web applications. With Dash, you don’t have to learn HTML, CSS and Javascript in order to create interactive dashboards, you only need python. Dash is open source and the application build using this framework are viewed on the web browser.

Dash is Downloaded 600,000 times per month, it’s the original low-code framework for rapidly building data apps in Python, R, Julia and F#(experimental).

It’s written on top of Plotly.js and React.js. Dash is ideal for building and deploying data apps with customized user interfaces. It’s particularly suited for anyone who works with data.

Through a couple of simple patterns, Dash abstracts away all of the technologies and protocols that are required to build a full-stack web app with interactive data visualization.

Dash is simple enough that you can bind a user interface to your code in less than 10 minutes.

Dash apps are rendered in the web browser. You can deploy the apps to VMs or kubermetes clusters and then share them through URLs. Since Dash apps are viewed in the web browser, Dash is inherently cross-platform and mobile ready.

There is a lot behind the framework. To learn more about how it’s built and what motivated Dash, read announcement letter or Dash is React for Python post.

Dash is an open source library released under the permissive MIT license. Plotly develops Dash and also offers a platform for writing and deploying Dash apps in an enterprise environment.

Python Dash is mostly suited for the quick and easy representation of big data which helps in analyzing and resolving issues. Power BI, on the other hand, has its data models focused on ingestion and building relatively complex models. Python is the best when it comes to handling streaming data.

Power BI vs. Tableau vs, Python Dash:

Power BITableauPython Dash
It is provided by MicrosoftIt is provided by TableauIt is a Python library, provided by the Python Software Foundation
It is available at a moderate priceIt is expensive than Power BIIt is an open-source programming language that is freely available for everyone to use.
Need a business/private email to open an accountNeed a business/private email to open an accountAny email address is acceptable
Uses DAX for Measures and Calculated columnsUses MDX for Measures and DimensionsUses dynamic, interpretive script programming language
Connect limited Data Sources but increases it Data Source connections in Monthly updatesIt can connect to numerous Data SourcesPython has an ecosystem of modules and tools to collect data from multiple sources.
Can handle large Datasets using Premium capacityCan handle large DatasetsCan handle large Datasets
It provides Account base subscriptionIt provides Key base subscriptionNo subscription necessary
Embedding report is easyEmbedding report is a Real time challengeDash is simple enough that you can bind a user interface to your code in less than 10 minutes
It is integrated with Microsoft Azure, which helps in analyzing the data and understanding the patterns of the productTableau has built-in machine learning capabilities which makes it suitable for doing ML operations on datasetsDash is integrated with Python which offers multiple libraries in graphics that are packed with different features. Python is preferred for data analysis of the highest levels
It supports R and Python language-based visualizationsIt provides full integrated support for R and PythonDash is ideal for building and deploying data apps with customized user interfaces.

Which one to choose, Power BI, Tableau or Python Dash?

Data Analytics field has been changed over time from traditional bi practice, embedded bi and collaborative bi. Initially, data analytics was led by companies like IBM, Oracle, SAP but now this is not the situation. Now, this led by companies like Microsoft, Tableau and Python because of their features like Embedded BI Collaborative BI, Data Blending, Data Binding and Multi Data Source Connection.

Power BI, Tableau and Python Dash have their own Pros and Cons. The right product can be chosen based on touchstones & priority.

TouchstonesPower BITableauPython Dash
DescriptionA cloud-based business intelligence platform which offers an overview of critical dataA collection of intuitive business intelligence tools used for data discoveryA python framework created by Plotly for creating interactive web applications. It is best when it comes to handling Streaming Data
VisualizationProvides various visualizationsProvides a larger set of visualizations than Power BIProvides numerous set of visualizations
OS SupportOnly WindowsWindows and Macintosh OSMac OS, Windows, Linux, AWS, and others
Graphical featuresRegular charts, graphs, and mapsAny category of charts, bars, and graphsDash is ideal for building and deploying data apps with customized user interfaces. Any category of charts, bars and graphs
CostCheaperCostlyFree
OrganizationSuitable for Small, Medium & Large type of OrganizationSuitable for Medium & Large type of OrganizationSuitable for Small, Medium & Large type of Organization

Data Analysis Expressions (DAX) is a programming language that is used throughout Microsoft Power BI for creating calculated columns, measures, and custom tables. It is a collection of functions, operators, and constants that can be used in a formula, or expression, to calculate and return one or more values.

In Multidimensional Expressions (MDX), a measure is a named DAX expression that is resolved by calculating the expression to return a value in a Tabular Model. This innocuous definition covers an incredible amount of ground.

 


Differences between Power BI and Tableau

Power BI is a Data Visualization and Business Intelligence tool provided by Microsoft. It can collect data from different data sources like Excel spreadsheets, on-premise database, cloud database and convert them into meaningful reports and dashboards. Its features such as creating quick insights, Q&A, Embedded Report, and Self Service BI made it top among all BI tools. It is also robust and always ready for extensive modeling and real-time analytics, as well as custom visual development.

Tableau offers business analysts to take business decisions by its feature, data visualization available to all business users of any background. It can establish a connection with any data source (Excel, local/on-premise database, cloud database).

Tableau is the fastest growing Data Visualization Tool among all visualization tools. Its visualizations are created as worksheets and dashboards. The beauty of tableau is that it does not require any technical or programming knowledge to create or develop reports and dashboards.

Power BI vs. Tableau:

Power BITableau
It is provided by MicrosoftIt is provided by Tableau
It is available at a moderate priceIt is expensive than Power BI
Need a business/private email to open an accountNeed a business/private email to open an account
Uses DAX for Measures and Calculated columnsUses MDX for Measures and Dimensions
Connect limited Data Sources but increases it Data Source connections in Monthly updatesIt can connect to numerous Data Sources
Can handle large Datasets using Premium capacityCan handle large Datasets
It provides Account base subscriptionIt provides Key base subscription
Embedding report is easyEmbedding report is a Real time challenge
It is integrated with Microsoft Azure, which helps in analyzing the data and understanding the patterns of the productTableau has built-in machine learning capabilities which makes it suitable for doing ML operations on datasets
It supports R and Python language-based visualizationsIt provides full integrated support for R and Python

Which one to choose, Power BI or Tableau?

Data Analytics field has been changed over time from traditional bi practice embedded bi and collaborative bi. Initially, data analytics led by companies like IBM, Oracle, SAP but now this is not a situation. Now, this led by companies like Microsoft & Tableau because of their features like Embedded BI Collaborative BI, Data Blending, Multi Data Source Connection.

Both Power BI and Tableau have their own Pros and Cons. The right product can be chosen based on touchstones & priority.

TouchstonesPower BITableau
DescriptionA cloud-based business intelligence platform which offers an overview of critical dataA collection of intuitive business intelligence tools used for data discovery
VisualizationProvides various visualizationsProvides a larger set of visualizations than Power BI
OS SupportOnly WindowsWindows and Macintosh OS
Graphical featuresRegular charts, graphs, and mapsAny category of charts, bars, and graphs
CostCheaperCostly
OrganizationSuitable for Small, Medium & Large type of OrganizationSuitable for Medium & Large type of Organization

What is Power BI? 

Power BI is a Data Visualization & Business Intelligence tools that offer us to connect to single or multiple data sources and convert that connected raw data into impressive visual and share insights across an organization. It also offers us to embed the report into our application or website.

Product Suite of Power BI:

  • Power BI Desktop 
    • Free to download and install.
    • Connect and access various of on-prem and cloud sources like Excel, CSV/Text files, Azure, SharePoint, Dynamics CRM, etc.
    • Prepare by mashing up the data and create a data model using power query which uses M Query Language
    • After loading data to Power BI Desktop can establish the relationship between tables.
    • Create calculated measures, columns, and tables using Data Analysis eXpression(DAX).
    • Drag & drop interactive visuals on to pages using calculated measures and columns.
    • Publish to Power BI Web Service.
  • Power BI Service
    • This is one of the ways to embed the reports within the Website under an organization.
    • In the Power BI service forum, there are a collection of sections like Workspace, Dashboards, Reports, and Datasets.
    • Can create our own workspace as My-Workspace which helps to maintain personal work in Power BI Service.
    • Can pin number of Reports to a Dashboard to get together a number of meaningful Datasets for clear insight.
    • In this we can interact with our data with the help of Q&A {natural language query.}
  • Power BI Report Server
    • This is one of the products to allow businesses to host Power BI reports on an on-premise report server.
    • Can use the server to host paginated reports, KPI’s, Mobile reports and Excel workbook.
    • Shared data sets and shared data sources are in their own folders, to use as building blocks for the reports.

  • Power BI Mobile
    • Over Power BI provides mobile app services for IOS, Android and Windows 10 mobile devices.
    • In the mobile app, you can connect to and interact with your cloud and on-premise data.
    • It is very convenient to manage dashboard and reports on the go with your mobile app to stay connected and being on the same page with the organization.
  • On-Premise Gateway
    • This is a bridge to connect your on-premise data to online services like Power BI, Microsoft flow, Logic App’s and Power App’s services, we can use a single gateway with different services at the same time.

e.g.: – If you are using Power BI as well as Power App’s, a single gateway can be used for both which is dependent on the account you signed with it.

  • The on-premises data gateway implements data compression and transport encryption in all modes.
  • On-premises data gateway is supported only on 64-bit Windows operating system.
  • Multiple users can be share and reuse a gateway in this mode.
  • For Power BI, this includes support for schedule refresh and Direct Query.

What is Tableau?

Tableau is a Business Intelligence & Data Visualization Tool that used to analyze our data visually. Users can create and share interactive reports & dashboards using it. It offers Data Blending to users to Connect multiple data sources.

Product Suite of Tableau:

  • Tableau Server
    • Tableau Server is an enterprise-wide visuals analytics platform for creating interactive dashboards.
    • It is essentially an online hosting platform to hold your entire tableau Workbooks, Data sources and more.
    • Being the product of tableau, you can use the functionality of tableau without needing to always be downloading and opening workbooks with tableau desktop.
    • Can give security level permission to different work in an organization to determine who can access and interact with what.
    • As a tableau server user, you will be able to access UpToDate content and gain quick insight without relying on static distributed content.   
  • Tableau Desktop
    • This is a downloadable on-premise application for Computers and it is used for developing visualization in the form of sheets, Dashboards, and Stories.
    • There are some useful functionalities of tableau desktop are: Data transformation, Creating Data Sources, Creating Extracts and Publishing Visualizations on tableau server.
    • Tableau desktop produces files with extensions twb and twbx.
    • It is a licensed product but comes with two weeks of the trial period.
    • Starting from creating reports and charts to combining them to form a dashboard, all this work is done in tableau desktop.
  • Tableau Prep
    • Tableau Prep is a personal data preparation tool that empowers the user with the ability to cleanse aggregate, merge or otherwise prepare their data for analysis in tableau.
    • Tableau Prep has a simple and clean user interface that looks and feels like a final form of tableau desktop data sources screen.
    • In Tableau Prep the data is stored in flow pane manner with has universal unique identifier [UUID] which can store big data sets in a secure way.
  • Tableau Reader
    • Tableau Reader is a free desktop application that you can use to open with data visualizations built in tableau desktop.
    • It required reading and interacting with tableau packaged workbooks.
    • Tableau reader has the ability to retain interaction with visualization created in tableau desktop but will not allow connections to data which can be refreshed.
    • It only supports to read tableau data files; without the reader, you may need to share it publicly or convert the workbook into a pdf format.
  • Tableau Online
    • Tableau online is an analytics platform which is fully hosted in the cloud.
    • It can publish Dashboards and share your discoveries with anyone.
    • It has a facility to empower your organization to ask any question from any published data source using natural language.
    • It can connect to any cloud databases at any time anywhere and it can automatically refresh the data from Web-App like Google analytics and salesforce.
    • It empowers site admins to easily manage authentication and permissions for users, content, and data.
  • Tableau Public
    • This is a free service that lets anyone public interactive data visualizations to the web.
    • Visualizations are created in the accompanying app Tableau Desktop Public edition which required no programming skills.
    • It is for anyone who’s interested in understanding data and sharing those discoveries as a data visualization with the world.
    • It has some features highlights those are: – Heat Maps, Transparent sheets, Automatic Mobile Layouts, and Google Sheets.
    • As visualization are public so anyone can access the data and make a change by downloading the workbook so it is totally unsecured.
    • It has limitations of 15,000,000 rows of data per workbook.
    • It has 10GB of storage space for your workbook which is kind of limitation towards storage.
    • It supports Python with Tableau public called ‘Tabpy’, A new API that enables evaluation of python code within a tableau workbook  

Here is a link to Tableau

Strengths & Weakness of Power BI:

Strengths:

  • Free Power BI Desktop application for authors to develop reports
  • Uses DAX expressions for data calculations
  • Free Training Modules available for users
  • Composite Model (Direct Query, Dual, and Import) to connect dispersed multiple data sources and create a model
  • Multiple visuals in a single page
  • Also has Drill Down-Drill Up in visuals, Drill through pages, Toggle page or visual using Bookmarks, selection pane & buttons
  • Ability to connect multiple data sources
  • It is affordable desktop – free and pro (Power BI Service to share and collaborate with other users in the organization) – $9.99
  • Can integrate with Cortana – Windows Personal Voice Assistant
  • Power BI has integrated with all Microsoft products (Azure, SharePoint, Office 365, Microsoft Dynamics, Power Apps, Microsoft Flow)
  • Dataflow in power BI Service to connect to Azure Data lake storage 2 and other online services.

Weakness:

  • It is difficult for users who do not have knowledge of Excel
  • Clients who use large data sets must opt for Premium Capacity services to avoid unpleasant experience with datasets and its users with performance and timeouts issues
  • Power BI service compatible with few database drivers
  • Power BI has got a large set of product options which make it complex to understand, which option is best suited for a Business.


Strengths & Weakness of Tableau:

Strengths:

  • Tableau provides much beautiful visualization for which it stood top in the market among all BI tools.
  • Quickly combine shape, & clean the data for analysis.
  • It provides Data Blending.
  • Capable of Drill Down-Drill Up in visuals, Drill through pages and filters.
  • It can handle a large amount of data.
  • Uses Scripting languages such as R & Python to avoid performance and for complex table calculations.
  • Can build reports, dashboards, and story using Tableau Desktop.

Weakness:

  • Tableau is expensive when compared to other tools.
  • Scheduling or Notification of reports & dashboards.
  • Importing Custom Visualization is a bit difficult.
  • Complexity in embedding report to other applications.
  • Tableau is suitable for Huge organization which can pay for licensing cost.


Benefits of Power BI

  • Microsoft is a Brand. I hope everyone remembers the school or college days, the time when we started learning and using Microsoft products as they are very simple to understand and user-friendly. Hence, obvious that our eyes and brain are trained on all Microsoft products.
  • One who has working experience excel can easily cope up with Power BI Desktop & Mobile in no time.
  • Pin the visual available in Excel to Power BI Service Using Excel Add-on.
  • Once can build swift & reliable reports by simply drag and drop both inbuilt/custom visuals and this URL for Best practices to make an optimum performance for the report.
  • Accessibility of Colossal Learning Assets available Guided Learning in this URL.
  • As Power BI belongs to Microsoft family, hence it has privileged with Single Sign-On (SSO) and also tight integration with Microsoft products like Dynamics 365, Office 365, SharePoint Online, Power Apps, MS Flow, Azure SQL Database, Azure SQL Data warehouse, Azure Analysis server database… etc.
  • Power Query Many options related to wrangling and clean the data bring it as a perfect data model.
  • Post publishing the data into Power BI web service can schedule refresh without manual intervention.
  • Power BI backed superpower of with Artificial intelligence and Machine learning
  • Microsoft introduced Power Platform (Power BI to Measure, Power Apps to Act & Microsoft Flow to automate) and you can find more details in this URL.
  • Forthcoming Road Map provided for Power BI by Microsoft available in this URL.
  • Power BI is integrated with both Python and R coding to use visualizations.
  • Power BI Desktop Free – $0.00 & Power BI Web Service (Azure) Pro – $9.99 Monthly

Disadvantages of Power BI

Power BI desktop is the best tool to analyze your data while you connect using Direct query (or) Live connections and might struggle handle huge if you import data into the application and at times it might get hung or simply crashes. However, in future monthly updates, Microsoft Product team will surely resolve this problem.

Benefits of Tableau

  • Tableau can connect various sources, can effortlessly handle huge data and is a very good tool for Data visualization and create dashboards by simply drag and drop.
  • Tableau supports Python and R languages for creating visuals.
  • Tableau has spent its term as Leader in Gartner’s report URL from 2012 – 2018 and now moved to second place.

Disadvantages of Tableau

Tableau Creator – $70.00 & Tableau Online – $35 Monthly

  • Tableau product team has not concentrated advanced technologies missed integrated with Artificial intelligence and Machine learning.
  • Once pushed the reports to tableau online, it does not support scheduled refresh and one must refresh the data manually.
  • Analyst must use only inbuilt visual available in Tableau and no option to import custom visuals from the portal. Instead, according to the requirement developers need to create custom visuals by themselves.
  • To create a data model, data preparation options in Tableau is limited. For advance data wrangling and cleaning one must take the help of other tools like Excel, Python, R, or Tableau Prep.
  • There is integration with other Microsoft products like Dynamics 365, Office 365, Power Apps, Microsoft Flow which uses Single Sign-On (SSO).

Power BI & Tableau are most happening BI tools among all tools in business intelligence because of their features and capabilities like Embedded BI, Data Blending, Multi Data Source connection like Cloud databases and on-premise databases. They make sharing of reports and dashboards for the users, easy. Business Analyst without even having to access these tools can access reports & dashboards and take critical business decisions.

These two tools stood top in the BI market because of the attractive visualizations available. Power BI offers a feature of import of custom visual and creation of custom visual which is its beauty. These facts have made these BI tools most happening BI tools in the market till the date.

According to Gartner Magic Quadrant for Analytics and Business Intelligence Platforms report, the 1st choice is Power BI and  2nd top choice is Tableau in BI Tool in the present market.

Data Analysis Expressions (DAX) is a programming language that is used throughout Microsoft Power BI for creating calculated columns, measures, and custom tables. It is a collection of functions, operators, and constants that can be used in a formula, or expression, to calculate and return one or more values.

In Multidimensional Expressions (MDX), a measure is a named DAX expression that is resolved by calculating the expression to return a value in a Tabular Model. This innocuous definition covers an incredible amount of ground.