Microsoft is named a Leader in the 2022 Gartner® Magic Quadrant™ for B2B Marketing Automation

Microsoft is named a Leader in the 2022 Gartner® Magic Quadrant™ for B2B Marketing Automation

This article is contributed. See the original author and article here.

We are excited and honored that Gartner has recognized Microsoft as a Leader in the 2022 Gartner Magic Quadrant™ for B2B Marketing Automation.*

Today’s buyers value authentic engagements and expect the companies they buy from to know them and to provide one-of-a-kind experiences. This past year, we continued to invest in solutions that help our customers meet these demands by delivering personalization at scale.

Customers can automate buyer-centric experiences and processes using trigger-based journeys to respond to actions and signals in real time and easily optimize customer journeys with insights from across digital and physical channels. With configurable key performance indicators (KPIs), customers can use out-of-box machine learning templates or bring their own AI/machine learning modelsboth options supporting rapid innovation that expands the discovery of insights.

Microsoft Dynamics 365 equips organizations with holistic views of their customers so they can discover what buyers need with insights that power individualized experiences and the delivery of deeply personalized content in customer journeys. Going forward, we will continue to expand this functionality for our customers.

chart, scatter chart
Figure 1: Magic Quadrant for B2B Marketing Automation**

Data at the center of everything

Gartner recognizes “B2B marketing automation platforms as technologies that serve as an essential tool for customer journey orchestration in support of B2B customer acquisition, retention and growth objectives.”

Dynamics 365 supports the practice of demand generation, helping our customers to capture, qualify, and nurture leads and accounts across multiple channels.

Data is at the center of all these functions, and it is the power behind the insights that drive what businesses need to do now and what they should do tomorrow. Microsoft helps businesses thrive and grow by capturing, analyzing, predicting, and reporting on their data.

One of the ways we are helping customers like Eika use one, coherent data platform to optimize their business-to-business (B2B) customer engagement with a high level of agility is the common data platform, Microsoft Dataverse. Dataverse lets you securely store and manage data used by business applications.

By using one single vendor that unifies and simplifies support processes for all solutions, customers benefit from products built to talk and complement each other. For instance, solutions like Dynamics 365 can work closely with other Microsoft technologies, such as Microsoft 365, Azure, Power BI, and LinkedIn to enhance and extend Dynamics 365 capabilities. This native integration also simplifies user adoptionusers are more comfortable when working with a familiar set of applications, and this ease of use can help lower total cost of ownership (TCO) and IT costs.

Additionally, customers that want to extend their investments in Microsoft technologies with applications to support their unique business needs have a huge range of options. Independent software vendors (ISVs) across the globe have created hundreds of software as a service (SaaS) applications that use Dynamics 365 and the underlying Microsoft Power Platform to deliver business value on top of Microsoft’s B2B automation platform.

Helping to ensure our customers’ success

Microsoft customers can participate in two customer success programs that deliver support for their implementations and can help them drive continuous adoption. FastTrack for Dynamics 365 is an engineering-led implementation guidance service that has been shown to speed deployments, drive higher usage, and reduce customer support needs. The second program, the Subscription Support for Microsoft Dynamics 365, is a support option delivered by a dedicated customer success unit within Microsoft.

Together, these programs have driven proven and lasting customer success. FastTrack for Dynamics 365 guides customers through successful implementations, bringing the best practices from thousands of deployments. The Subscription Support for Microsoft Dynamics 365 helps them drive continuous adoption, ensuring customers are realizing ongoing business value from the solutions they deploy.

Reimaging your B2B marketing automation

As today’s digital-first buyers become more informed, meeting their expectations is becoming a more difficult challenge. They’re more likely than ever to ignore anything that doesn’t feel authenticforcing businesses to connect customer insights and data across the organization to meet customers where they are and to capitalize on every opportunity.

B2B marketing automation platforms serve as an essential tool for customer journey orchestration in support of B2B customer acquisition, retention, and growth objectives. As Gartner mentioned in their report, “while B2B marketing automation platforms are designed to primarily support B2B use cases, they can also provide much needed functionality to B2C organizations selling high-consideration products and/or B2B2C models with more complex, indirect sales processes.”

We are excited to be positioned as a Leader in the Gartner Magic Quadrant and are committed to helping both our B2B and business-to-consumer (B2C) customers meet the changing expectations of their customers. We invite you to learn more about how Microsoft is helping marketers around the world reimagine customer experience.

Man sitting at a table, looking at a mobile phone in his hand.

2022 Gartner Magic Quadrant for B2B Marketing Automation Platforms

Microsoft is excited to be recognized as a Leader in the Gartner Magic Quadrant.

Source: Gartner, Magic Quadrant for B2B Marketing Automation Platforms, Rick LaFond, Julian Poulter, Jeffrey L. Cohen, Matt Wakeman, Jeff Goldberg , 19 September 2022

*Gartner is a registered trademark and service mark and Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

**This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Microsoft.

The post Microsoft is named a Leader in the 2022 Gartner® Magic Quadrant™ for B2B Marketing Automation appeared first on Microsoft Dynamics 365 Blog.

Brought to you by Dr. Ware, Microsoft Office 365 Silver Partner, Charleston SC.

What’s wrong with 1:M relationships between ADX tables In Power BI

What’s wrong with 1:M relationships between ADX tables In Power BI

This article is contributed. See the original author and article here.

What’s wrong with 1:M relationships between ADX tables?


In this article I want to talk about the behavior of 1:M relationships and what kinds of joins are created to support 1: M.


Aren’t 1:M just the normal relationships in PBI?


Yes, they are but not when both sides are ADX queries in Direct Query mode.

In most cases Power BI “thinks” that the relationship is M:M because of the way distinct count works in ADX.

To get 1:M, you have to change the relationship’s properties using tabular editor or another method.

Also, if the dimension table is small, the distinct count of the key will return the exact value and the relationship will be defined as 1:M.


So, if they are the default, what’s wrong with 1:M?


The problem is with the KQL joins which are generated based on 1:M relationships.

Let’s assume that we have a Product Category dimension, and you filter by one category.

the relationship is 1:M between Product Category and FactSales.

Assuming you used IsDimension=true on the dimension, The KQL statement generated will be something like:


| join kind=rightouter hint.strategy =broadcast SalesFact

| summarize A0=sum(Sales) by …

| where Category==”Cat1”



Because of the right outer join, the filter on Category is applied after the join is performed on the entire fact table.

The query results will be correct, but the performance will be bad.

What can be done to make the query perform better?


We need to convince PBI to create an inner join instead of the rightouter join.

There are two way to force an inner join:

  • Define the relationship as M:M

  • Define the relationships as 1:M and checking the option Assume Referential integrity.

  • DanyHoter_0-1677407863482.png



In the case of inner join , the filter(s) on the dimension that appear at the end of the query, will be pushed by the ADX engine to the early stages of execution and so the join will be only on the products that belong to Cat1 in this example.

The result will be a much faster query.



If you see in the queries generated by PBI any other join except inner, you have to change your PBI model so that the joins will be inner.






Getting Started with AI for Low Code Development.

Getting Started with AI for Low Code Development.

This article is contributed. See the original author and article here.


AI, without a doubt, is revolutionizing low code development. The capabilities of Artificial Intelligence into Low code have the power to revolutionize the way you work and enhance the applications and solutions you build.


You may be wondering what’s in it for you with AI as a low code developer. Well, AI has immense potential from automating repetitive tasks, adding intelligence into your applications, building chatbots, automated workflows, predictive analysis and much more on AI.


As a low code developer, you understand the power of technology to streamline the development to deployment process. Well in addition, with the recent development of AI this is a chance to take your skills to the next level. This is a rapidly growing field with massive impact and as a low code developer, you certainly do not need ten years of experience to develop AI models or rather add intelligence into your solution. In this blog, we’ll explore the basics of AI for low code developers, what opportunities you have in this platform and responsible AI.




What is Artificial Intelligence?

AI refers to the development of algorithms that can perform tasks that typically require human intelligence such as recognition, decision-making, solving problems and cognitive services. This usually involves training a computer/model to recognize patterns, make decisions and solve problems based on data. With the current development of AI, the main goal is to be able to create systems that can learn, adapt, and improve over time.


The results of AI are immense and have the potential to revolutionize many industries and change the way we live and work. For a low code developer this means you can automate tasks, improve accuracy and speed, provide valuable insights that can enhance user experience.


Opportunity of AI for a low code developer.

As a low code developer, the opportunity to integrate AI into your development process is too good to ignore. Regardless of your level of experience as a low code developer, AI is a powerful tool that can help you add intelligence into your solution and get the most out of it. As AI continues to evolve, we can expect to see more innovative solutions and use cases of AI in our solutions. Some examples of the several ways you can use AI as a low code developer include:

  1. Creating chatbots – Leveraging Power Virtual Agents helps you create conversational chatbots where customers can get quicker services from your business with tasks such as customer service, ticket processing and general inquiry. It is easier also to integrate chatbots solutions into your existing solutions easily. For instance, once you are done building your solution you can publish the chatbot onto your website, mobile apps, messaging platforms (teams, Facebook). Get started here Intelligent Virtual Agents and Bots | Microsoft Power Virtual Agents

    Top tip: Remember to publish your chatbot for any updates you make to reflect changes.



  1. Automated workflows – AI can be used to automate workflows in low code applications, reducing manual effort and improving efficiency. This also helps an organization follow a structured manner in the business processes with the organization.Julia_Muiruri_1-1676874515317.png


  2. Decision making – Using AI you can be able to gain some valuable insights from the data that you have. For instance, if you need to predict a future trend based on data from the past few years, you can achieve this as a low code developer using AI

  3. Image and object detection – Leveraging object detection allows you to fully utilize AI as a low code developer from tasks such as document scanning and extraction of texts from images. This will help you improve the quality of your applications and extract substantial amounts of data in a short time.

  4. Natural language processing – This can be used in low code applications to improve accuracy and speed of text- based tasks such as sentiment analysis. If you need to detect the tone in a customer service review, you can leverage on this to detect the sentiments whether it is positive, neutral, or negative.




Here are some of the key benefits of using AI in low code development.

  1. Automation of repetitive tasks – With AI, you can automate repeated tasks such as data entry, form processing and this will let you focus on other high priority business activities

  2. Improved accuracy and speed – Some processes that are tedious and manual can be prone to errors and time consuming. Pre-built AI Models can be integrated into your solutions to enhance your application’s accuracy and speed.

  3. Gaining valuable insights – AI can help low code developers extract valuable insights from data such as trends, this helps you make data-driven decisions for greater business success.


Getting started with AI as a Low code developer.

You can quickly bring AI into your solutions using Microsoft Power Platform, connecting to your business data regardless of where they are stored in One Drive, SharePoint, Azure, Dataverse

With a few simple steps you can easily get started using AI Builder


  1. Understand business requirements – Before you begin, it is important to understand the business process that needs to be integrated with AI.

  2. Choose a use case – Once you have decided on the business process that needs AI choose the best use case you can start with. Some of the common use cases we have are similar across businesses, including form processing, invoice processing and sentiment analysis.
    If the business problem is unique to your business, you can create a model from scratch to better suit your business.

  3. Using AI Builder – To use AI Builder, you need to have a Microsoft Power Apps or Power Automate license, with which you can access AI Builder from your Power App or flow. If you don’t have one, you can create a free developers account here

  4. Choosing an AI Model – You can either use a pre-built template or create a custom model from scratch using your own data or pre-existing data. Using a prebuilt model means that you are utilizing already built AI scenario templates, like invoice processing.

  5. Test and deploy your model – Once you have selected the model that you want to use based on the business process case, with AI builder, you can test the model’s performance before deployment. With this you can validate the model’s accuracy and adjust it as needed. Once you are satisfied you can deploy it to your Power Apps application or Power Automate flow.

  6. Monitoring and Improvement – After deploying your model, you can monitor its performance and adjust as per your need.


Responsible AI

As you get started with AI as a low code developer, it is important to ensure that the AI you build is developed and used for the right purpose and in the right environment. Microsoft highlights six key principles for responsible AI namely:

  • Transparency

  • Fairness

  • Reliability and safety

  • Privacy and security

  • Inclusiveness

  • Accountability.

To achieve this as a low code developer who is exploring AI, Microsoft provides resources to help the developers, businesses, and individuals to understand and use responsible AI practices and ethics. This provides a set of principles to guide the development of AI in a responsible way.

Learn more about responsible AI here and how Microsoft is achieving this.


Wondering how to get started and explore more resources, check these out:

  1. Overview of AI Builder – AI Builder

  2. AI Builder—AI Templates for Apps

  3. AI Best Practice Architecture and Frameworks

  4. Watch this video for a demo on how to get started

Guia de Estudos: DP-900 Microsoft Azure Data Fundamentals

Guia de Estudos: DP-900 Microsoft Azure Data Fundamentals

This article is contributed. See the original author and article here.

Preparando-se para o exame DP-900 Microsoft Azure Data Fundamentals e não sabe por onde começar? Este artigo é um guia de estudo para certificação DP-900!


Fiz uma curadoria de artigos da Microsoft para cada objetivo do exame DP-900. Além disso, compartilho os conteúdos da #SprintDP900, uma série de mentorias do Microsoft Reactor.


No Microsoft Reactor, oferecemos diversos conteúdos gratuitos de capacitação em tecnologias da Microsoft e organizamos sprints de estudos para certificações. Na #SprintDP900, estamos realizando uma série de 3 aulas sobre certificação Azure Data Fundamentals, nos dias 28 de fevereiro, 01 e 02 de março. Todas as pessoas que participarem do Cloud Skills Challenge e assistirem as aulas, poderão participar do quiz de avaliação de conhecimentos e concorrer a um voucher gratuito para realização da prova.


Agenda #SprintDP900


28 de fevereiro, às 12:30h
#SprintDP900: Introdução à Banco de dados no Azure e tipos de serviços

No primeiro encontro, você irá aprender sobre os conceitos básicos de banco de dados na nuvem, entendo cargas de trabalho, funções e serviços comuns. 

clique aqui para se inscrever

01 de março, às 12:30h
#SprintDP900: Banco de Dados não relacional no Azure

No segundo encontro do #SprintDP900, vamos aprofundar os conceitos de banco de dados não relacional e conhecer os recursos disponíveis no Azure.

clique aqui para se inscrever

02 de março, às 12:30h
#SprintDP900: Análise de Dados no Azure

No terceiro encontro do #SprintDP900, vamos abordar os serviços de análise de dados no Azure.

clique aqui para se inscrever


As gravações das aulas estarão disponíveis em nosso canal, basta acessar o link de cada sessão.




O Microsoft Cloud Skills Challenge é uma plataforma integrada com o Microsoft Learn, que é uma plataforma global, disponível 24 horas por dia, 7 dias por semana. Você pode criar sua agenda de estudos, pois o desafio estará disponíveis no período de 28/02/2023 a 10/02/2023. As aulas semanais ocorrem no formato ao vivo e se você não puder participar, terá a possibilidade de assistir as gravações.

O Cloud Skills Challenge irá utilizar a trilha de estudos DP-900: Fundamentos Dados do Microsoft Azure em português (Brasil). A prova de certificação também está disponível em português.


Inscreva-se no Cloud Skills Challenge da #SprintDP900:


O que eu preciso fazer para ganhar um voucher de certificação?

Você deverá realizar sua inscrição para as aulas ao vivo e realizar a trilha de estudos proposta no Cloud Skills Challenge. Na aula que será realizada no dia 02 de março, vamos disponibilizar um quiz de validação de conhecimentos para selecionar as 100 pessoas que receberão, por e-mail, um voucher gratuito para realização da certificação DP-900: Azure Data Fundamentals. O critério de priorização dos vouchers é a conclusão do Cloud Skills Challenge, participação nas aulas e obtenção de 80% de acerto no quiz.


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Recursos adicionais de aprendizado

Se você não tem muita familiaridade com computação em nuvem, recomendo estudar a trilha Azure Fundamentals:

As porcentagens indicadas em cada tópico da prova são referentes ao peso / volume de questões que você poderá encontrar no exame.


Descrever os principais conceitos de dados (25-35%)


Descrever maneiras de representar dados

Identificar opções para armazenamento de dados

Descrever cargas de trabalho de dados comuns

Identificar funções e responsabilidades para cargas de trabalho de dados


Dados relacionais no Azure (20-25%)


Descrever conceitos de banco de dados relacionais

Descrever os serviços de dados relacionais do Azure

Segurança e Conectividade

Ferramentas para queries



Descrever dados não relacionais em Azure (15-20%)


Descrever os recursos do armazenamento do Azure

Descrever capacidades e recursos do Azure Cosmos DB

Segurança e Conectividade

Ferramentas para queries



Análise de Dados no Azure (25-30%)


Descrever elementos comuns de análise em larga escala

Descrever a análise de dados em tempo real

Descrever a visualização de dados no Microsoft Power BI