Azure AZ101 Checklist of Materials

Here is my checklist of Azure infrastructure handy materials that I use for AZ101 students as homework or additional class learning. I was asked for this by one of my students and I thought I’d share it here. The focus here is on just some of the topics and it is not exhaustive:

  • Azure Migrate
  • Azure Site Recovery
  • Disaster Recovery

Azure Migrate

Currently, you can only create an Azure Migrate project in West Central US or East US region. However, this does not impact your ability to plan your migration for a different target Azure location. The location of the migration project is used only to store the metadata discovered from the on-premises environment.​

Create an Azure Migrate project 

Reference on how to Create an Azure Migrate Project

Current Limitations on creating a project in Azure Migrate

Geography Storage location
Azure Government US Gov Virginia
Asia Southeast Asia
Europe North Europe or West Europe
United States East US or West Central US

Creating a Collector

Download the collector appliance

Create the Collector VM

Run the collector to discover VMs

Assessing Migration Readiness

Assessing VM Size

Troubleshooting Readiness Issues and Common Errors

Costing Considerations

Reviewing the VM Sizing

Other useful nuggets

Azure Migrate FAQ

Videos for Migrating Servers to Azure

Channel 9 video on how to migrate your applications and workloads to the cloud.
Microsoft Mechanics video on how to discover, assess and migrate Windows and Linux virtual machines from VMware to Azure.
Microsoft Channel 9 video on Azure Migrate with Michael Leworthy and Lindsay Berg of the Product Team

Azure Site Recovery

Overview of Azure Site Recovery and Site Recovery Pricing

Getting Ready for Azure Site Recovery

Prepare Azure Reference

Prepare on-premises Hyper-V servers for disaster recovery to Azure

Tutorial: Prepare Azure resources for replication of on-premises machines

Tutorial: Azure resources for replication of Hyper-V machines

Azure Storage Reference

Deployment Planner

Site Recovery Deployment Planner for Hyper-V to Azure

Analyze the Azure Site Recovery Deployment Planner Report

Recovery Services overview

Troubleshooting remote desktop connection after failover using ASR

Completing Migration

Enable replication

Excluding disks from the replication process

Add a group to a Recovery Plan

Add a script or manual action to a Recovery Plan

Tutorial: Setting up a replication policy

Recovery Plans

Failover in Site Recovery

Create and customize recovery plans

Test failover to Azure in Site Recovery

Run a failback for Hyper-V VMs

Add Azure Automation runbooks to recovery plans

Troubleshooting Hyper-V to Azure replication and failover

Set up disaster recovery to Azure for Hyper-V VMs using PowerShell and Azure Resource Manager

Videos for Azure Site Recovery

 

Azure Site Recovery Video Series

Video 1 Infrastructure Setup

Video 2 Enable protection for Hyper-V virtual machines

Video 3 Recovery Plan Test Failover, Planned Failover and Unplanned Failover to Azure

Video 4: Failover from Azure to on-premises (failback)

Video 5: Failback from Azure to On-Premises (VMWare)

Disaster Recovery

This section covers the five steps in the process to migrate VMware VMs to Azure: Configure the Infrastructure, Configure the vCenter Server, Protect the Virtual Machines, Configure Disaster Recovery (DR) and Failover, and Configure Failback.

 

Backing up Virtual Machines

Plan your VM backup infrastructure in Azure

Use the Azure portal to restore virtual machines

Tutorial: Back up and restore files for Windows Virtual Machines in Azure

Azure Virtual Machine PowerShell samples

Virtual Machine Architecture

Architectural Components

Azure Site Recovery pricing

Virtual Machine Replication

Replication Settings

Azure Database Migration Guide

Automatic update of the Mobility Service in Azure to Azure replication

Accelerated Networking with Azure virtual machine disaster recovery

Create a Windows virtual machine with Accelerated Networking

Set up disaster recovery for Azure VMs to a secondary Azure region

Troubleshooting

Troubleshoot Azure-to-Azure VM replication issues

Troubleshoot issues with the Azure Site Recovery agent

Troubleshoot issues with the Azure Site Recovery agent

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

AWS RDS vs Microsoft Azure SQL Database: What does it mean for the business?

As a freelance industry analyst who has worked with GigaOm, I’m pleased to see the GigaOM Transactional Field Test derived from the industry-standard TPC Benchmark™ E (TPC-E) report which compares Amazon Web Services Relational Database Service (AWS RDS) and Microsoft Azure SQL Database. It’s written by William McKnight and Jake Dolezal from GigaOm.

From the business intelligence perspective, it is incredibly useful to compare AWS RDS and Microsoft Azure SQL Database. Before we dive in, what do they have in common? They are both fully-managed cloud SQL Server offerings, and from the Business Intelligence perspective, it can be difficult for business decision makers to choose between them. In this post, we’ll distill the technical language so that business decision makers find it easier to make the right decision for them.

Why SQL Server?

SQL Server is one of the most mature, well-known and common databases in the world, according to data from DB-Engines ranking. In fact, there is some evidence to suggest that SQL Server is going to overtake Oracle, potentially due to the cheaper price tag.

SQL Server offers fantastic business intelligence features, and this is probably one of the key areas where it wins over Oracle. In my experience, this is where I saw SQL Server ‘creep’ into IT estates due to its superior business intelligence solutions in SQL Server, which were easier to use. In particular, the SQL Server Reporting Services solution solved problems for Finance departments; they could get repeatable financial reports very easily, and Microsoft SQL Server solved a business problem. So it took hold.

Why SQL Server in the cloud?

Generally speaking, the cloud is attractive to businesses due to its lower costs, scalability and pay-as-you-go pricing model.

For IT Departments, cloud databases are mainly used for read-intensive, data intensive applications such as data warehousing, data mining and business intelligence operations which need elasticity and scalability. Cloud databases offer reliable computing, storage, backup and network facilities at the lowest cost, which is particularly important when the IT Department is regarded as a cost center.

AWS RDS and Microsoft Azure SQL Database are both web services that makes it easy to set up and scale a relational database in the Cloud. They are designed for developers or businesses that want cloud databases.

AWS RDS vs Microsoft Azure SQL Database

GigaOm have held a GigaOM Transactional Field Test which aims at comparing the two databases. In the test, the performance was scaled to meet the needs of 80,000 customers, and the performance was tuned to meet the database engine’s optimal capability to process data. The tests consisted of five test runs that lasted a duration of two hours each for both platforms. You can read more detail about the test runs in the GigaOM Transactional Field Test.

Cost Differentiation

The paper found that Microsoft Azure SQL Database was considerably less expensive to run in comparison to AWS RDS over a month. Of course, the reader should try out different scenarios in order to make the decisions that are right for their environment. That said, the cost savings are compelling, and if you have not looked at Microsoft Azure SQL Database before, then it is worth reviewing the cost differences. This held to be true over different reviews of the solutions, such as cost, licencing and time reservations.

The paper concluded that the database, along with the cloud, matters to latency which is
the killer for important transactional applications. Microsoft Azure SQL Database presents a compelling proposition for the modern transactional workload, meeting the need for data warehousing, data mining and business intelligence operations which are the engine of many of today’s businesses.

Conclusion

The reality is that we live in a multi-cloud world. Apart from cost, how can organizations show that they are creating uniquely desirable products and services rather than simply discussing cost?

Since Microsoft are the creators and owners of Microsoft Azure SQL Database, the differentiation here is that Microsoft lead with research, development and innovation in SQL Server. This means that Microsoft lead with the ability to design, deliver and support SQL Server in its different guises, whether on-premises, in the cloud, or a hybrid mixture of both platforms.

Since Microsoft own SQL Server, they are uniquely placed to understand the dynamics of the customer needs and the market, thereby meeting those needs to develop uniquely well-specified products for the market. Therefore, it makes sense that Azure would be finely tuned to rise up and meet these needs. Hence, it makes sense that Microsoft SQL Azure Database costs are optimized to be lower, and this is reflected in the costing differences found in the study.

To read the GigaOM Transactional Field Test derived from the industry-standard TPC Benchmark™ E (TPC-E) report, please refer to the article. Please feel free to leave your thoughts in the comments.

Keynote and Session highlights at ASG Evolve19

I’m excited to be attending ASG Technology’s Evolve19 event in Dallas, Texas on October 21 – 23.

I’m particularly interested in learning more about metadata management. As I’ve written previously, I believe that metadata management is often overlooked in data science. It’s an important part of obtaining full value from data, and it should be part of the data maturity model. I’m looking forward to seeing how businesses can securely use data to make better business decisions faster, and compliance teams can reduce data related risks and resolve any data problems.

I’ve blogged more about my favourite sessions and the keynotes over at the Data Relish site and I look forward to seeing you there! If you are going to be at the event, please don’t hesitate to get in touch. I’d love to say hello!

 

Future Decoded: Microsoft imbues ethics and responsibility into AI as a core part of Azure AI technology.

Microsoft are designing AI to be trustworthy requires creating solutions that reflect ethical principles that are deeply rooted in important and universal values. This is becoming a core principle and practice, and Microsoft are doing a great deal of research in this area.

Why is this important? Information technology is ever-growing, and becoming more sophisticated by the day. We are living in an era that is more defined by its rapidly evolving technology and how this technology is becoming a part of every person’s everyday life, regardless of their own abilities or experience. Because of this, it is the responsibility of the organisations behind this technology to ensure that they are taking responsibility for the massive impact that they cause. The future is likely to be even more defined by the technology that is currently evolving – and if companies neglect to take a practical, thoughtful and responsible approach to implementing and developing this software, they run the risk of not being able to catch up with the consequences. 

Technology can be both empowering and threatening. Issues such as breaches of privacy and data protection, cyber-attacks, and irresponsible programming are present threats that run the risk of becoming even more serious problems. The rise of AI poses another type of threat that is different but equally as important to recognise, wherein the developers and programmers behind the technology pose the risk of embedding societal prejudices and their own judgements into the algorithms that determine the AI’s actions. There are moral and ethical conundrums arising as technology becomes more sophisticated and ingrained in our daily lives.

Human decision makers are susceptible to many forms of prejudice and bias, such as those rooted in gender and racial stereotypes. Evidence in research as well as publicized news stories have found that machine learning systems can inadvertently discriminate against minorities, historically disadvantaged populations and other groups. One would hope that machine learning would overcome the bias, but, unfortunately, it learns from decisions that are made in the world.

Microsoft are devising interesting and innovation solutions to tackle bias. For example, Microsoft research have created a “fairness enforcer” which uses machine learning to create a process that yields a classification rule that is fair, according to the fairness definition while minimizing the error involved.

Microsoft are currently attempting to help companies by embedding a practical and considerate approach into their software. Last year, Microsoft released a statement titled Responsible Bots: 10 guidelines for developers of conversational AI (2018), where they outlined their ethos for responsibly creating and maintaining technology that people are most likely to use day-to-day for real-world, consequential tasks and problems. They also published Six principles for the responsible development of AI in a 2018 book, The Future Computed (2018). With Microsoft working on systematically and intentionally aiming to prioritise social responsibility and ethical decision-making as the most integral factors in their transformation as a company, and making their choices and actions public and transparent. 

While Microsoft are currently leading the way on implementing practical strategies, it is becoming more important that any company who is developing AI need to be aware of the risks and how to prevent and handle them.

Tay_bot_logoA strong example of how conversational AI can be used irresponsibly and become harmful is Microsoft’s ‘Tay’ experiment. Tay was a Microsoft AI chatbot who was launched on Twitter  in 2016. After less than 24 hours, Tay was shut down completely, as it had begun to generate a huge amount of inappropriate tweets filled with racist, sexist and anti-Semitic language. This was due to the fact that Tay learned from the interactions that it had with the public. The lack of responsibility on the developer’s part was a failing in itself – but Microsoft have learned this lesson and they have done the right thing in doing so. Miller et al state in their article ‘Why We Should Have Seen That Coming: Comments on Microsoft’s ‘Tay’ Experiment, and Wider Implications’, 

We contend that these incidents are symptoms of a deeper problem with the very nature of learning software (LS-a term that we will use to describe any software that changes its program in response to its interactions) that interacts directly with the public, the developer’s relationship with it, and the responsibility associated with it. We make the case that when LS interacts directly with people or indirectly via social media, the developer has additional ethical responsibilities beyond those of standard software. There is an additional burden of care. (Miller, Wolf and Grodzinsky, 2017)

Cases like this make it clear that developers need to re-think their approach to conversational AI, and begin to prioritise a more proactive ethical stance. Microsoft have suggested a number of ways for developers and companies to begin their journey to more responsible, practical AI practices.

It is important to have a ‘break out in case of emergency’ option present when conversational AI is interacting with the public. This allows the opportunity for conversations to be interrupted or shut down if they become inappropriate. In the case of companies and products using AI virtual helplines, it also presents the chance for the conversation to be flagged to the moderator if the customer expresses that they are dissatisfied or have a problem which goes beyond the AI’s capabilities.

One of the most interesting, but also most potentially harmful features of conversational AI is that it can take on a human-like persona, or appear to have a personality. This gives extra weight to the things that it says in conversation; it needs to interact appropriately with users, respect cultural norms, and be designed to avoid norms violations wherever possible. For example, a chatbot designed to help users navigate an online banking site should not have opinions on religion or politics, and should not engage at all with any questions that are not relevant to its purpose. It is also useful to have a ‘code of conduct’ for users where possible, explicitly prohibiting hate speech or any form of harassment. Techniques such as keyword filtering come in useful when designing a bot to pick up on inflammatory language and inappropriate subjects.

Data Relish have recently released a series on how to tackle prejudice in machine learning, and this is something that also needs to be considered in this case. It is essential that AI is programmed to treat every user fairly and equally. To help ensure this is the case, the development team should be diverse and home to a wide range of perspectives and experiences. Furthermore, data should be consistently assessed by humans – while bias detection tools can be helpful, they should not be relied on entirely.

toolsandweapons_3d_new-1-671x1024Overall, it is essential that the development of AI does not lead to us feeling as if their job is now their job only and is therefore out of our hands. Human intervention is essential throughout the entire process. For AI in more sensitive fields, such as health or law enforcement, experts from relevant fields should be brought in and encouraged to give their input. Customer service AI should be programmed to ask users for feedback, which should be fed back to colleagues at the business. To ensure the AI is accessible for all, disabled people should be invited to test the bots before they are released to the public. Ideally, AI should learn from diversity and effectively recognize and shut down issues.

At Data Relish, we are currently reading Brad Smith’s new book on ethics and AI, called Tools and Weapons. We will summarize in the future, and, in the meantime, we are glad to see that Microsoft are taking these issues seriously.

 

Miller, K.W; Wolf, Marty J; Grodzinsky, F.S. (2017). Why we should have seen that coming. ORBIT Journal, 1(2).

Future Decoded: Business opportunity and sustainability in the era of AI

CindyRosePeople don’t like change, and they don’t like to be changed. That’s what makes Digital Transformation so challenging.

Digital Transformation is one of the key topics at Future Decoded, where I’m working as an analyst this week. The Day 1 event is aimed to help visitors learn about the cutting-edge technologies that are shaping the modern workplace across industries. The event is intended to promote and provoke ideas for business opportunity and sustainability in the era of AI. This becomes increasingly important as UK companies risk falling behind foreign rivals unless they use more AI.  

Interestingly, the Financial sector leading the way in using AI. For example, NatWest is using AI to understand how financial markets behave. For people who are interested in applying AI to their organization, it is worth taking a look at the latest Microsoft’s AI report – Accelerating Competitive Advantage with AI

Planet, Profit and People – and AI

Organizations are taking environmental impact very seriously and, at Future Decoded, Microsoft cracks down on plastics at Future Decoded. At Future Decoded, the keynotes have had a clear emphasis on ethics being at the centre of working with technology, people and process. This emphasis also chimes with the new focus on Diversity and Inclusion at Future Decoded.

One key theme was the need for diversity and inclusion as a key part of the philosophy of a non-biased way of managing AI. Diverse teams, whether inadvertently or mindfully, do challenge each other, which means that people can also challenge themselves. In challenging themselves in an empathetic environment, the bar for success can be raised. In teams where everyone is similar, people don’t always question themselves or each other since there does not seem to be a need in these environments; the ‘like me’ impact means that the implication is that everyone is right, because they are ‘like me’ so they must be right, so therefore…. I am right. That’s not the way forward, however. If you want to raise the bar of success for your teams and your product, you need to include diversity and inclusion as part of the DNA of your organization in order to challenge ourselves and each other. It is these friendly challenges which raise the bar to success. You can’t just slap an AI label on a weak business proposition, and expect it to be successful.

In the field of AI in business, there are plenty of so-called ‘experts’ but it is important to understand how the Dunning–Kruger effect is involved in planning out projects, such as those projects that include AI. In case you haven’t heard of it, the Dunning-Kruger effect is a cognitive bias in which people mistakenly assess their cognitive ability as greater than it actually is. That’s why we need people around us, to challenge us, to encourage us, and to help us to see the ‘data’ to guide us towards making better decisions and having a sustainable impact on our business. Recognizing our cognitive biases, and working to overcome them, can help us to do better. Listening to the Future Decoded keynotes today was an interesting experience, because it was thoughtful about the impact of technology on people and planet. It was clear about how this practical ethics translates into authenticity in the behaviour of an organization, which, in turn, can  help an organization to scale up by increasing profits by focusing on using less impactful resources.

Aside from that issue, what else can you do in order to have a scale up attitude to AI?

  • Create a roadmap for 12 to 18 months, based on what the business values and perceives as key criteria for success.
  • Follow the value chain of your business. Think of the operations that are required to make AI succeed in the long term, and think about automation.
  • Think about sustainability for your organization as well as the environment. What is going to have a lasting impact for good for your business?

Digital Transformation is everywhere, but it is an ongoing process rather than a box to be ticked. Every company has to be a software company, whether they like it or not. Eventually, is every company is going to be a cloud and AI company? An analytics company?

Digital Transformation is about showing what is possible, and giving people a vision that they feel that they can be a part of, and a vision of the organization that they feel they belong to. This is an additional important theme, and it was good to hear that the stakeholders in the business are part of the Digital Transformation and AI stories.

The Future Decoded keynote ended on a note of recasting Big Data with a view to using it for quantum computing. The end user gets information and insights that they were never able to have before, which brings better choices and outcomes. The keynote is exciting because it allows for longer-term future results using Microsoft as a platform. It’s not science fiction, and quantum is helping us to solve problems that were never solvable before with classic computing.

Transformation will mean that every company is a software company, focused on what your organization does best. Trust is the currency of the new economy; it is a foundation for what makes a good partner.

For this reason, data is so important in earning trust. Data is a vague term, a catch-all which is skimmed over by organizations and people. Bad decisions are based on bad data; good decisions are down to expertise. Perhaps we all have a Dunning-Kruger impact? Data is the moments of our lives; our books, what we read, our photographs. Companies have taken the trust for granted and we suffer a techlash. With great power, comes great responsibility and the decisions need to be an ethical one, from the data right up to impact on the environment. In line with the thinking about ethics, the keynote ended with this thought; it is your data, and we must respect it. In doing so, it shows a respect for the people behind the data, and that should be a core pillar of any organization.

Future Decoded – see you there?

I’m excited to be attending Future Decoded in London this week. I’m attending in my role as an industry analyst, so I am not presenting. Instead, I will be interviewing Microsoft UK executives

What will be happening at Future Decoded? The event itself is aimed at business decision makers and leaders, rather than a technical event for IT Pros or developers. Due to that objective, Future Decoded features unique perspectives from industry leaders. I’m personally excited to meet astronauts Major Tim Peake CMG and Dr Helen Sharman CMG, OBE, HonFRSC and I’m interested to hear their perspective on the priorities for technology, given the challenges that the planet is currently facing. Additionally, Red Bull F1’s Team Principal Christian Horner OBE is delivering a keynote on technology and what it means for the F1 success.

I’m also excited to meet Michael Wignall, Azure Business Lead, Microsoft UK and I’ll be talking with Michael about the importance of AI as the fourth industrial revolution. Following on from that topic, I’ll be speaking with Paolo Costa, Microsoft’s Principal Researcher, Microsoft Research Cambridge and I’ll be learning more about the AI projects and innovation coming out of Cambridge.

I’m also super excited to be meeting Mitra Azizirad, Corporate Vice President, Microsoft AI again. Mitra leads Microsoft’s portfolio of AI innovation, and I’ll be asking her about Microsoft’s vision to democratise access to AI for ultimately empowering every individual to transform society with AI.

So I’m going to have a busy few days at the event. Check back in later this week for more news and deeper insights on these issues, and more!

 

 

 

 

Join me at Live! 360 Orlando to learn more about Applied Business Analytics

I’m delighted to be speaking at Live! 360 Orlando and I’m presenting a workshop on From Business Intelligence to Business Analytics with the Microsoft Data Platform. The conference is held on November 17-22, 2019 at the Royal Pacific Resort at Universal Orlando in Florida.

Data becomes relevant for decision making when we start to use it properly, so this workshop will demonstrate the use of analytics for real-life use cases. This means that you can make use of the knowledge you learn from the session when you go back to the office. In this workshop, we will make analytics relevant to your business so your organization will feel the benefit. If you’d like to register, please click here to register and please use code SPKLIVE87 and receive $400 off the standard pricing for both the 6-Day and 5-Day Packages.

Here are the topics we’ll cover:

  • Introduction to Analytics with the Microsoft Data Platform
  • Essential Business Statistics for Analytics Success – the important statistics that business users use often in business spheres, such as marketing and strategy.
  • Business Analytics for your CEO – what information does your CEO really care about, and how can you produce the analytics that she really wants? In this session, we will go through common calculations and discuss how these can be used for business strategy, along with their interpretation.
  • Analytics for Marketing – what numbers do they need, why, and what do they say? In this session, we will look at common marketing scenarios for analytics, and how they can be implemented with the Microsoft Data Platform.
  • Analytics for Sales – what numbers do they need on a sales dashboard, why, and what do they say? In this session, we will look at common sales scenarios for analytics such as forecasting and ‘what if’ scenarios, and how they can be implemented with the Microsoft Data Platform.
  • Analytics with Python – When you really have difficult data to crunch, Python is your secret Power tool.
  • Business Analytics with Big Data – let’s look at Big Data sources and how we can do Big Data Analytics with tools in Microsoft’s Data Platform.

Power BI and Marketing Data

What kind of applicable analytics will we cover? One important area in data is marketing. The primary aim of a Digital Marketing Analytics Report is to provide Digital Marketing Managers, Product Owners, and other relevant teams and stakeholders with a clear and easily-usable overview of the KPIs related to their campaigns. They should be designed in such a way that whoever is looking at them can interact with them in an easy, productive way. A Digital Marketing Analytics Report will generally have a wide range of visuals, of different levels of complexity, that display data in different ways.

The average attention span is only eight seconds. Therefore, it makes perfect sense to gather your data onto one canvas in a visually-compelling way, rather than relying on a lengthy PowerPoint or a cluttered Excel spreadsheet. Power BI is a hugely effective way of gathering and connecting to various data sources and using them to create a more engaging experience. 

When it comes to marketing specifically, there are some major advantages that come with processing your data in Power BI. Visualizing your data in a Power BI report lets you recognise patterns and trends that might have otherwise gone unnoticed if the data was in a text-based format. It is also an effective way to identify and create your company’s own best practices vs the common best practices of the industry; you can estimate traffic by creating a click-through-rate curve for your site, rather than just using the industry average.

Power BI can also make it easier to answer the questions that your clients and other stakeholders ask of you. Being able to explore and interact with live data means that, when you’re asked a question, rather than responding by saying “I don’t know, but I’ll get back to you” you can say “Let’s find out”. Not only does this mean that your client doesn’t have to wait for an answer, but it means that you are showing that you’re being honest and transparent about your data and how you analyse it.

You can also interact with your data in other ways — for example, it’s easy to isolate data from a specific time period in one visualization, and have this be reflected in the other visualizations on the Dashboard. You could select the spendings from a particular week, and have the KPIs from this week be highlighted, allowing you to better see the relationship between these two statistics.

Colour can also be used in a Dashboard to have certain effects, such as showcasing relationships clearly and drawing attention to the most important data, either overall or during user interaction. Colour can also be used to illustrate performance — for example, green statistics to represent above target, yellow for on target, and red for below target.

You can use a Power BI Dashboard to give an overview of impressions, campaign clicks, and average spend for a digital campaign. These statistics combined gives an overview of the effectiveness of and costs related to the campaign — seeing these statistics side by side can give a better idea of how they relate to each other.

Marketers are already learning how to use better tools for bigger datasets, and the environment is quickly evolving. Digital marketers working in eCommerce are experiencing over 45% increases year-on-year (1). People are becoming more digitally literate, and are learning how to search more effectively across more devices — often even beginning a purchase on one device and completing it on a different one. There are more search engines, more sites, and overall more options for users, and many users are responding to this by becoming better, more specific searchers. This means that, as marketers, instead of worrying about 1,000 keywords, you need to worry about 1,000,000. You need to have a much better idea of your marketing initiatives, click-throughs and engagements, and you need to convey this in a way that will be both understandable and engaging. 

If there are larger initiatives or recurring marketing programs that you need to track, it may be beneficial to setup specific reports, but in general, a Power BI dashboard is a great way to share your insights in a way that doesn’t end at a basic display of your data, but allows the data to be further investigated on-the-spot and when needed.

Join me in Live! 360 Orlando to learn more about Power BI and marketing, along with other topics.