Speech recognition takes the spoken word and converts it into data that can be processed – often by transcribing it into text. The spoken words can be in the form of a recorded voice in an audio file, or live audio from a microphone. Speech patterns are analyzed in the audio to determine recognizable patterns that are mapped to words. To accomplish this, the software typically uses multiple models, including:
An acoustic model that converts the audio signal into phonemes (representations of specific sounds).
A language model that maps phonemes to words, usually using a statistical algorithm that predicts the most probable sequence of words based on the phonemes.
The recognized words are typically converted to text, which you can use for various purposes, such as:
Providing closed captions for recorded or live videos
Creating a transcript of a phone call or meeting
Automated note dictation
Determining intended user input for further processing
Speech synthesis is concerned with vocalizing data, usually by converting text to speech. A speech synthesis solution typically requires the following information:
The text to be spoken
The voice to be used to vocalize the speech
To synthesize speech, the system typically tokenizes the text to break it down into individual words, and assigns phonetic sounds to each word. It then breaks the phonetic transcription into prosodic units (such as phrases, clauses, or sentences) to create phonemes that will be converted to audio format. These phonemes are then synthesized as audio and can be assigned a particular voice, speaking rate, pitch, and volume.
You can use the output of speech synthesis for many purposes, including:
Generating spoken responses to user input
Creating voice menus for phone systems
Reading email or text messages aloud in hands-free scenarios
Broadcasting announcements in public locations, such as railway stations or airports
The management infrastructure includes Azure resources and resource groups, subscriptions, and accounts. Understanding the hierarchical organization will help you plan your projects and products within Azure.
Azure resources and resource groups
A resource is the basic building block of Azure. Anything you create, provision, deploy, etc. is a resource. Virtual Machines (VMs), virtual networks, databases, cognitive services, etc. are all considered resources within Azure.
Resource groups are simply groupings of resources. When you create a resource, you’re required to place it into a resource group. While a resource group can contain many resources, a single resource can only be in one resource group at a time. Some resources may be moved between resource groups, but when you move a resource to a new group, it will no longer be associated with the former group. Additionally, resource groups can’t be nested, meaning you can’t put resource group B inside of resource group A.
Resource groups provide a convenient way to group resources together. When you apply an action to a resource group, that action will apply to all the resources within the resource group. If you delete a resource group, all the resources will be deleted. If you grant or deny access to a resource group, you’ve granted or denied access to all the resources within the resource group.
When you’re provisioning resources, it’s good to think about the resource group structure that best suits your needs.
For example, if you’re setting up a temporary dev environment, grouping all the resources together means you can deprovision all of the associated resources at once by deleting the resource group. If you’re provisioning compute resources that will need three different access schemas, it may be best to group resources based on the access schema, and then assign access at the resource group level.
There aren’t hard rules about how you use resource groups, so consider how to set up your resource groups to maximize their usefulness for you.
Azure subscriptions
In Azure, subscriptions are a unit of management, billing, and scale. Similar to how resource groups are a way to logically organize resources, subscriptions allow you to logically organize your resource groups and facilitate billing.
Using Azure requires an Azure subscription. A subscription provides you with authenticated and authorized access to Azure products and services. It also allows you to provision resources. An Azure subscription links to an Azure account, which is an identity in Microsoft Entra ID or in a directory that Microsoft Entra ID trusts.
An account can have multiple subscriptions, but it’s only required to have one. In a multi-subscription account, you can use the subscriptions to configure different billing models and apply different access-management policies. You can use Azure subscriptions to define boundaries around Azure products, services, and resources. There are two types of subscription boundaries that you can use:
Billing boundary: This subscription type determines how an Azure account is billed for using Azure. You can create multiple subscriptions for different types of billing requirements. Azure generates separate billing reports and invoices for each subscription so that you can organize and manage costs.
Access control boundary: Azure applies access-management policies at the subscription level, and you can create separate subscriptions to reflect different organizational structures. An example is that within a business, you have different departments to which you apply distinct Azure subscription policies. This billing model allows you to manage and control access to the resources that users provision with specific subscriptions.
Create additional Azure subscriptions
Similar to using resource groups to separate resources by function or access, you might want to create additional subscriptions for resource or billing management purposes. For example, you might choose to create additional subscriptions to separate:
Environments: You can choose to create subscriptions to set up separate environments for development and testing, security, or to isolate data for compliance reasons. This design is particularly useful because resource access control occurs at the subscription level.
Organizational structures: You can create subscriptions to reflect different organizational structures. For example, you could limit one team to lower-cost resources, while allowing the IT department a full range. This design allows you to manage and control access to the resources that users provision within each subscription.
Billing: You can create additional subscriptions for billing purposes. Because costs are first aggregated at the subscription level, you might want to create subscriptions to manage and track costs based on your needs. For instance, you might want to create one subscription for your production workloads and another subscription for your development and testing workloads.
Azure management groups
The final piece is the management group. Resources are gathered into resource groups, and resource groups are gathered into subscriptions. If you’re just starting in Azure that might seem like enough hierarchy to keep things organized. But imagine if you’re dealing with multiple applications, multiple development teams, in multiple geographies.
If you have many subscriptions, you might need a way to efficiently manage access, policies, and compliance for those subscriptions. Azure management groups provide a level of scope above subscriptions. You organize subscriptions into containers called management groups and apply governance conditions to the management groups. All subscriptions within a management group automatically inherit the conditions applied to the management group, the same way that resource groups inherit settings from subscriptions and resources inherit from resource groups. Management groups give you enterprise-grade management at a large scale, no matter what type of subscriptions you might have. Management groups can be nested.
Management group, subscriptions, and resource group hierarchy
You can build a flexible structure of management groups and subscriptions to organize your resources into a hierarchy for unified policy and access management. The following diagram shows an example of creating a hierarchy for governance by using management groups.
Some examples of how you could use management groups might be:
Create a hierarchy that applies a policy. You could limit VM locations to the US West Region in a group called Production. This policy will inherit onto all the subscriptions that are descendants of that management group and will apply to all VMs under those subscriptions. This security policy can’t be altered by the resource or subscription owner, which allows for improved governance.
Provide user access to multiple subscriptions. By moving multiple subscriptions under a management group, you can create one Azure role-based access control (Azure RBAC) assignment on the management group. Assigning Azure RBAC at the management group level means that all sub-management groups, subscriptions, resource groups, and resources underneath that management group would also inherit those permissions. One assignment on the management group can enable users to have access to everything they need instead of scripting Azure RBAC over different subscriptions.
Important facts about management groups:
10,000 management groups can be supported in a single directory.
A management group tree can support up to six levels of depth. This limit doesn’t include the root level or the subscription level.
Each management group and subscription can support only one parent.
Throughout your journey with Microsoft Azure, you’ll hear and use terms like Regions, Availability Zones, Resources, Subscriptions, and more. This module focuses on the core architectural components of Azure. The core architectural components of Azure may be broken down into two main groupings: the physical infrastructure, and the management infrastructure.
Physical infrastructure
The physical infrastructure for Azure starts with datacenters. Conceptually, the datacenters are the same as large corporate datacenters. They’re facilities with resources arranged in racks, with dedicated power, cooling, and networking infrastructure.
As a global cloud provider, Azure has datacenters around the world. However, these individual datacenters aren’t directly accessible. Datacenters are grouped into Azure Regions or Azure Availability Zones that are designed to help you achieve resiliency and reliability for your business-critical workloads.
The Global infrastructure site gives you a chance to interactively explore the underlying Azure infrastructure.
Regions
A region is a geographical area on the planet that contains at least one, but potentially multiple datacenters that are nearby and networked together with a low-latency network. Azure intelligently assigns and controls the resources within each region to ensure workloads are appropriately balanced.
When you deploy a resource in Azure, you’ll often need to choose the region where you want your resource deployed.
Note
Some services or virtual machine (VM) features are only available in certain regions, such as specific VM sizes or storage types. There are also some global Azure services that don’t require you to select a particular region, such as Microsoft Entra ID, Azure Traffic Manager, and Azure DNS.
Availability Zones
Availability zones are physically separate datacenters within an Azure region. Each availability zone is made up of one or more datacenters equipped with independent power, cooling, and networking. An availability zone is set up to be an isolation boundary. If one zone goes down, the other continues working. Availability zones are connected through high-speed, private fiber-optic networks.
Important
To ensure resiliency, a minimum of three separate availability zones are present in all availability zone-enabled regions. However, not all Azure Regions currently support availability zones.
Use availability zones in your apps
You want to ensure your services and data are redundant so you can protect your information in case of failure. When you host your infrastructure, setting up your own redundancy requires that you create duplicate hardware environments. Azure can help make your app highly available through availability zones.
You can use availability zones to run mission-critical applications and build high-availability into your application architecture by co-locating your compute, storage, networking, and data resources within an availability zone and replicating in other availability zones. Keep in mind that there could be a cost to duplicating your services and transferring data between availability zones.
Availability zones are primarily for VMs, managed disks, load balancers, and SQL databases. Azure services that support availability zones fall into three categories:
Zonal services: You pin the resource to a specific zone (for example, VMs, managed disks, IP addresses).
Zone-redundant services: The platform replicates automatically across zones (for example, zone-redundant storage, SQL Database).
Non-regional services: Services are always available from Azure geographies and are resilient to zone-wide outages as well as region-wide outages.
Even with the additional resiliency that availability zones provide, it’s possible that an event could be so large that it impacts multiple availability zones in a single region. To provide even further resilience, Azure has Region Pairs.
Region pairs
Most Azure regions are paired with another region within the same geography (such as US, Europe, or Asia) at least 300 miles away. This approach allows for the replication of resources across a geography that helps reduce the likelihood of interruptions because of events such as natural disasters, civil unrest, power outages, or physical network outages that affect an entire region. For example, if a region in a pair was affected by a natural disaster, services would automatically fail over to the other region in its region pair.
Important
Not all Azure services automatically replicate data or automatically fall back from a failed region to cross-replicate to another enabled region. In these scenarios, recovery and replication must be configured by the customer.
Examples of region pairs in Azure are West US paired with East US and South-East Asia paired with East Asia. Because the pair of regions are directly connected and far enough apart to be isolated from regional disasters, you can use them to provide reliable services and data redundancy.
Additional advantages of region pairs:
If an extensive Azure outage occurs, one region out of every pair is prioritized to make sure at least one is restored as quickly as possible for applications hosted in that region pair.
Planned Azure updates are rolled out to paired regions one region at a time to minimize downtime and risk of application outage.
Data continues to reside within the same geography as its pair (except for Brazil South) for tax- and law-enforcement jurisdiction purposes.
Important
Most regions are paired in two directions, meaning they are the backup for the region that provides a backup for them (West US and East US back each other up). However, some regions, such as West India and Brazil South, are paired in only one direction. In a one-direction pairing, the Primary region does not provide backup for its secondary region. So, even though West India’s secondary region is South India, South India does not rely on West India. West India’s secondary region is South India, but South India’s secondary region is Central India. Brazil South is unique because it’s paired with a region outside of its geography. Brazil South’s secondary region is South Central US. The secondary region of South Central US isn’t Brazil South.
Sovereign Regions
In addition to regular regions, Azure also has sovereign regions. Sovereign regions are instances of Azure that are isolated from the main instance of Azure. You may need to use a sovereign region for compliance or legal purposes.
Azure sovereign regions include:
US DoD Central, US Gov Virginia, US Gov Iowa and more: These regions are physical and logical network-isolated instances of Azure for U.S. government agencies and partners. These datacenters are operated by screened U.S. personnel and include additional compliance certifications.
China East, China North, and more: These regions are available through a unique partnership between Microsoft and 21Vianet, whereby Microsoft doesn’t directly maintain the datacenters.
To create and use Azure services, you need an Azure subscription. When you’re working with your own applications and business needs, you need to create an Azure account, and a subscription will be created for you. After you’ve created an Azure account, you’re free to create additional subscriptions. For example, your company might use a single Azure account for your business and separate subscriptions for development, marketing, and sales departments. After you’ve created an Azure subscription, you can start creating Azure resources within each subscription.
If you’re new to Azure, you can sign up for a free account on the Azure website to start exploring at no cost to you. When you’re ready, you can choose to upgrade your free account. You can also create a new subscription that enables you to start paying for Azure services you need beyond the limits of a free account.
Create an Azure account
You can purchase Azure access directly from Microsoft by signing up on the Azure website or through a Microsoft representative. You can also purchase Azure access through a Microsoft partner. Cloud Solution Provider partners offer a range of complete managed-cloud solutions for Azure.
Free access to popular Azure products for 12 months.
A credit to use for the first 30 days.
Access to more than 25 products that are always free.
The Azure free account is an excellent way for new users to get started and explore. To sign up, you need a phone number, a credit card, and a Microsoft or GitHub account. The credit card information is used for identity verification only. You won’t be charged for any services until you upgrade to a paid subscription.
What is the Azure free student account?
The Azure free student account offer includes:
Free access to certain Azure services for 12 months.
A credit to use in the first 12 months.
Free access to certain software developer tools.
The Azure free student account is an offer for students that gives $100 credit and free developer tools. Also, you can sign up without a credit card.
Most of the exercises in the Introduction to Azure learning paths and modules are bring your own subscription (BYOS). BYOS requires you to have a subscription to complete the exercise.
Each exercise has a clean up step at the end. It’s important to complete the clean up step in order to avoid unanticipated Azure costs.
Azure is a continually expanding set of cloud services that help you meet current and future business challenges. Azure gives you the freedom to build, manage, and deploy applications on a massive global network using your favorite tools and frameworks.
What does Azure offer?
Limitless innovation. Build intelligent apps and solutions with advanced technology, tools, and services to take your business to the next level. Seamlessly unify your technology to simplify platform management and to deliver innovations efficiently and securely on a trusted cloud.
Bring ideas to life: Build on a trusted platform to advance your organization with industry-leading AI and cloud services.
Seamlessly unify: Efficiently manage all your infrastructure, data, analytics, and AI solutions across an integrated platform.
Innovate on trust: Rely on trusted technology from a partner who’s dedicated to security and responsibility.
What can I do with Azure?
Azure provides more than 100 services that enable you to do everything from running your existing applications on virtual machines to exploring new software paradigms, such as intelligent bots and mixed reality.
Many teams start exploring the cloud by moving their existing applications to virtual machines (VMs) that run in Azure. Migrating your existing apps to VMs is a good start, but the cloud is much more than a different place to run your VMs.
For example, Azure provides artificial intelligence (AI) and machine-learning (ML) services that can naturally communicate with your users through vision, hearing, and speech. It also provides storage solutions that dynamically grow to accommodate massive amounts of data. Azure services enable solutions that aren’t feasible without the power of the cloud.
A data analyst is one of several critical roles in an organization, who help uncover and make sense of information to keep the company balanced and operating efficiently. Therefore, it’s vital that a data analyst clearly understands their responsibilities and the tasks that are performed on a near-daily basis. Data analysts are essential in helping organizations gain valuable insights into the expanse of data that they have, and they work closely with others in the organization to help reveal valuable information.
The following figure shows the five key areas that you’ll engage in during the data analysis process.
Prepare
As a data analyst, you’ll likely divide most of your time between the prepare and model tasks. Deficient or incorrect data can have a major impact that results in invalid reports, a loss of trust, and a negative effect on business decisions, which can lead to loss in revenue, a negative business impact, and more.
Before a report can be created, data must be prepared. Data preparation is the process of profiling, cleaning, and transforming your data to get it ready to model and visualize.
Data preparation is the process of taking raw data and turning it into information that is trusted and understandable. It involves, among other things, ensuring the integrity of the data, correcting wrong or inaccurate data, identifying missing data, converting data from one structure to another or from one type to another, or even a task as simple as making data more readable.
Data preparation also involves understanding how you’re going to get and connect to the data and the performance implications of the decisions. When connecting to data, you need to make decisions to ensure that models and reports meet, and perform to, acknowledged requirements and expectations.
Privacy and security assurances are also important. These assurances can include anonymizing data to avoid oversharing or preventing people from seeing personally identifiable information when it isn’t needed. Alternatively, helping to ensure privacy and security can involve removing that data completely if it doesn’t fit in with the story that you’re trying to shape.
Data preparation can often be a lengthy process. Data analysts follow a series of steps and methods to prepare data for placement into a proper context and state that eliminate poor data quality and allow it to be turned into valuable insights.
Model
When the data is in a proper state, it’s ready to be modeled. Data modeling is the process of determining how your tables are related to each other. This process is done by defining and creating relationships between the tables. From that point, you can enhance the model by defining metrics and adding custom calculations to enrich your data.
Creating an effective and proper semantic model is a critical step in helping organizations understand and gain valuable insights into the data. An effective semantic model makes reports more accurate, allows the data to be explored faster and efficiently, decreases time for the report writing process, and simplifies future report maintenance.
The model is another critical component that has a direct effect on the performance of your report and overall data analysis. A poorly designed model can have a drastically negative impact on the general accuracy and performance of your report. Conversely, a well-designed model with well-prepared data will ensure a properly efficient and trusted report. This notion is more prevalent when you are working with data at scale.
From a Power BI perspective, if your report is performing slowly, or your refreshes are taking a long time, you will likely need to revisit the data preparation and modeling tasks to optimize your report.
The process of preparing data and modeling data is an iterative process. Data preparation is the first task in data analysis. Understanding and preparing your data before you model it will make the modeling step much easier.
Visualize
The visualization task is where you get to bring your data to life. The ultimate goal of the visualize task is to solve business problems. A well-designed report should tell a compelling story about that data, which will enable business decision makers to quickly gain needed insights. By using appropriate visualizations and interactions, you can provide an effective report that guides the reader through the content quickly and efficiently, therefore allowing the reader to follow a narrative into the data.
The reports that are created during the visualization task help businesses and decision makers understand what that data means so that accurate and vital decisions can be made. Reports drive the overall actions, decisions, and behaviors of an organization that is trusting and relying on the information that is discovered in the data.
The business might communicate that they need all data points on a given report to help them make decisions. As a data analyst, you should take the time to fully understand the problem that the business is trying to solve. Determine whether all their data points are necessary because too much data can make detecting key points difficult. Having a small and concise data story can help find insights quickly.
With the built-in AI capabilities in Power BI, data analysts can build powerful reports, without writing any code, that enable users to get insights and answers and find actionable objectives. The AI capabilities in Power BI, such as the built-in AI visuals and Copilot, enable the discovering of data by asking questions, using the Quick Insights feature, or creating machine learning models directly within Power BI.
An important aspect of visualizing data is designing and creating reports for accessibility. As you build reports, it is important to think about people who will be accessing and reading the reports. Reports should be designed with accessibility in mind from the outset so that no special modifications are needed in the future.
Many components of your report will help with storytelling. From a color scheme that is complementary and accessible, to fonts and sizing, to picking the right visuals for what is being displayed, they all come together to tell that story.
Analyze
The analyze task is the important step of understanding and interpreting the information that is displayed on the report. In your role as a data analyst, you should understand the analytical capabilities of Power BI and use those capabilities to find insights, identify patterns and trends, predict outcomes, and then communicate those insights in a way that everyone can understand.
Advanced analytics enables businesses and organizations to ultimately drive better decisions throughout the business and create actionable insights and meaningful results. With advanced analytics, organizations can drill into the data to predict future patterns and trends, identify activities and behaviors, and enable businesses to ask the appropriate questions about their data.
Previously, analyzing data was a difficult and intricate process that was typically performed by data engineers or data scientists. Today, Power BI makes data analysis accessible, which simplifies the data analysis process. Users can quickly gain insights into their data by using visuals and metrics directly from their desktop and then publish those insights to dashboards so that others can find needed information.
This feature is another area where AI integrations within Power BI can take your analysis to the next level. Integrations with Azure Machine Learning, cognitive services, Copilot and built-in AI visuals will help to enrich your data and analysis.
Manage
Power BI consists of many components, including reports, dashboards, workspaces, semantic models, and more. As a data analyst, you are responsible for the management of these Power BI assets, overseeing the sharing and distribution of items, such as reports and dashboards, and ensuring the security of Power BI assets.
Apps can be a valuable distribution method for your content and allow easier management for large audiences. This feature also allows you to have custom navigation experiences and link to other assets within your organization to complement your reports.
The management of your content helps to foster collaboration between teams and individuals. Sharing and discovery of your content is important for the right people to get the answers that they need. It is also important to help ensure that items are secure. You want to make sure that the right people have access and that you are not leaking data past the correct stakeholders.
Proper management can also help reduce data silos within your organization. Data duplication can make managing and introducing data latency difficult when resources are overused. Power BI helps reduce data silos with the use of shared semantic models, and it allows you to reuse data that you have prepared and modeled. For key business data, endorsing a semantic model as certified can help to ensure trust in that data.
The management of Power BI assets helps reduce the duplication of efforts and helps ensure security of the data.
Telling a story with the data is a journey that usually doesn’t start with you. The data must come from somewhere. Getting that data into a place that is usable by you takes effort that is likely out of your scope, especially in consideration of the enterprise.
Today’s applications and projects can be large and intricate, often involving the use of skills and knowledge from numerous individuals. Each person brings a unique talent and expertise, sharing in the effort of working together and coordinating tasks and responsibilities to see a project through from concept to production.
In the recent past, roles such as business analysts and business intelligence developers were the standard for data processing and understanding. However, excessive expansion of the size and different types of data has caused these roles to evolve into more specialized sets of skills that modernize and streamline the processes of data engineering and analysis.
The following sections highlight these different roles in data and the specific responsibility in the overall spectrum of data discovery and understanding:
Business analyst
Data analyst
Data engineer
Analytics engineer
Data scientist
Business analyst
While some similarities exist between a data analyst and business analyst, the key differentiator between the two roles is what they do with data. A business analyst is closer to the business and is a specialist in interpreting the data that comes from the visualization. Often, the roles of data analyst and business analyst could be the responsibility of a single person.
Data analyst
A data analyst enables businesses to maximize the value of their data assets through visualization and reporting tools such as Microsoft Power BI. Data analysts are responsible for profiling, cleaning, and transforming data. Their responsibilities also include designing and building scalable and effective semantic models, and enabling and implementing the advanced analytics capabilities into reports for analysis. A data analyst works with the pertinent stakeholders to identify appropriate and necessary data and reporting requirements, and then they are tasked with turning raw data into relevant and meaningful insights.
A data analyst is also responsible for the management of Power BI assets, including reports, dashboards, workspaces, and the underlying semantic models that are used in the reports. They are tasked with implementing and configuring proper security procedures, in conjunction with stakeholder requirements, to ensure the safekeeping of all Power BI assets and their data.
Data engineer
Data engineers provision and set up data platform technologies that are on-premises and in the cloud. They manage and secure the flow of structured and unstructured data from multiple sources. The data platforms that they use can include relational databases, nonrelational databases, data streams, and file stores. Data engineers also ensure that data services securely and seamlessly integrate across data platforms.
Primary responsibilities of data engineers include the use of on-premises and cloud data services and tools to extract, transform, and load data from multiple sources. Data engineers collaborate with business stakeholders to identify and meet data requirements. They design and implement solutions.
While some alignment might exist in the tasks and responsibilities of a data engineer and a database administrator, a data engineer’s scope of work goes well beyond looking after a database and the server where it’s hosted and likely doesn’t include the overall operational data management.
A data engineer adds tremendous value to business intelligence and data science projects. When the data engineer brings data together, often described as data wrangling, projects move faster because data scientists can focus on their own areas of work.
As a data analyst, you would work closely with a data engineer in making sure that you can access the variety of structured and unstructured data sources because they will support you in optimizing semantic models, which are typically served from a modern data warehouse or data lake.
Analytics engineer
Analytics engineers bridge the gap between data engineering and analysis by curating data assets in data lakes or lakehouses, ensuring data quality, and enabling self-service analytics. They create semantic models in Power BI to prepare and serve data effectively. They may also prepare other data stores, such as data warehouses or lakehouses.
There is some overlap with the data engineer role, as both may use SQL as a query language in data warehouses and both focus on data quality. Data Engineers often build the foundational pipelines and infrastructure. Analytics Engineers sit closer to the business, focusing on modeling and making data usable for reporting/decision-making.
Data scientist
Data scientists perform advanced analytics to extract value from data. Their work can vary from descriptive analytics to predictive analytics. Descriptive analytics evaluate data through a process known as exploratory data analysis (EDA). Predictive analytics are used in machine learning to apply modeling techniques that can detect anomalies or patterns. These analytics are important parts of forecast models.
Descriptive and predictive analytics are only partial aspects of data scientists’ work. Some data scientists might work in the realm of deep learning, performing iterative experiments to solve a complex data problem by using customized algorithms.
Anecdotal evidence suggests that most of the work in a data science project is spent on data wrangling and feature engineering. Data scientists can speed up the experimentation process when data engineers use their skills to successfully wrangle data.
On the surface, it might seem that a data scientist and data analyst are far apart in the work that they do, but this conjecture is untrue. A data scientist looks at data to determine the questions that need answers and will often devise a hypothesis or an experiment and then turn to the data analyst to assist with the data visualization and reporting.
Before data can be used to tell a story, it must be run through a process that makes it usable in the story. Data analysis is the process of identifying, cleaning, transforming, and modeling data to discover meaningful and useful information. The data is then crafted into a story through reports for analysis to support the critical decision-making process.
As the world becomes more data-driven, storytelling through data analysis is becoming a vital component and aspect of large and small businesses. It is the reason that organizations continue to hire data analysts.
Data-driven businesses make decisions based on the story that their data tells, and in today’s data-driven world, data is not being used to its full potential, a challenge that most businesses face. Data analysis is, and should be, a critical aspect of all organizations to help determine the impact to their business, including evaluating customer sentiment, performing market and product research, and identifying trends or other data insights.
While the process of data analysis focuses on the tasks of cleaning, modeling, and visualizing data, the concept of data analysis and its importance to business should not be understated. To analyze data, core components of analytics are divided into the following categories:
Descriptive
Diagnostic
Predictive
Prescriptive
Artificial Intelligence (AI)
Descriptive analytics
Descriptive analytics help answer questions about what has happened based on historical data. Descriptive analytics techniques summarize large semantic models to describe outcomes to stakeholders.
By developing key performance indicators (KPIs), these strategies can help track the success or failure of key objectives. Metrics such as return on investment (ROI) are used in many industries, and specialized metrics are developed to track performance in specific industries.
An example of descriptive analytics is generating reports to provide a view of an organization’s sales and financial data.
Diagnostic analytics
Diagnostic analytics help answer questions about why events happened. Diagnostic analytics techniques supplement basic descriptive analytics, and they use the findings from descriptive analytics to discover the cause of these events. Then, performance indicators are further investigated to discover why these events improved or became worse. Generally, this process occurs in three steps:
Identify anomalies in the data. These anomalies might be unexpected changes in a metric or a particular market.
Collect data that’s related to these anomalies.
Use statistical techniques to discover relationships and trends that explain these anomalies.
Predictive analytics
Predictive analytics help answer questions about what will happen in the future. Predictive analytics techniques use historical data to identify trends and determine if they’re likely to recur. Predictive analytical tools provide valuable insight into what might happen in the future. Techniques include a variety of statistical and machine learning techniques such as neural networks, decision trees, and regression.
Prescriptive analytics
Prescriptive analytics help answer questions about which actions should be taken to achieve a goal or target. By using insights from prescriptive analytics, organizations can make data-driven decisions. This technique allows businesses to make informed decisions in the face of uncertainty. Prescriptive analytics techniques rely on machine learning as one of the strategies to find patterns in large semantic models. By analyzing past decisions and events, organizations can estimate the likelihood of different outcomes.
Artificial Intelligence
Artificial Intelligence (AI) helps answer questions about your data. AI refers to the simulation of human intelligence in machines that are programmed to think, learn, and adapt. In the context of analytics, AI enables systems to process vast amounts of data, recognize patterns, and deliver insights with minimal human intervention. It supports a wide range of applications, from natural language processing and image recognition to code generation and intelligent visualization suggestions.
Example
By enabling reporting and data visualizations, a retail business uses descriptive analytics to look at patterns of purchases from previous years to determine what products might be popular next year. The company might also look at supporting data to understand why a particular product was popular and if that trend is continuing, which will help them determine whether to continue stocking that product.
A business might determine that a certain product was popular over a specific timeframe. Then, they can use this analysis to determine whether certain marketing efforts or online social activities contributed to the sales increase.
An underlying facet of data analysis is that a business needs to trust its data. As a practice, the data analysis process will capture data from trusted sources and shape it into something that is consumable, meaningful, and easily understood to help with the decision-making process. Data analysis enables businesses to fully understand their data through data-driven processes and decisions, allowing them to be confident in their decisions.
As the amount of data grows, so does the need for data analysts. A data analyst knows how to organize information and distill it into something relevant and comprehensible. A data analyst knows how to gather the right data and what to do with it, in other words, making sense of the data in your data overload.
As your cloud usage grows, it’s increasingly important to stay organized. A good organization strategy helps you understand your cloud usage and can help you manage costs.
One way to organize related resources is to place them in their own subscriptions. You can also use resource groups to manage related resources. Resource tags are another way to organize resources. Tags provide extra information, or metadata, about your resources. This metadata is useful for:
Resource management Tags enable you to locate and act on resources that are associated with specific workloads, environments, business units, and owners.
Cost management and optimization Tags enable you to group resources so that you can report on costs, allocate internal cost centers, track budgets, and forecast estimated cost.
Operations management Tags enable you to group resources according to how critical their availability is to your business. This grouping helps you formulate service-level agreements (SLAs). An SLA is an uptime or performance guarantee between you and your users.
Security Tags enable you to classify data by its security level, such as public or confidential.
Governance and regulatory compliance Tags enable you to identify resources that align with governance or regulatory compliance requirements, such as ISO 27001. Tags can also be part of your standards enforcement efforts. For example, you might require that all resources be tagged with an owner or department name.
Workload optimization and automation Tags can help you visualize all of the resources that participate in complex deployments. For example, you might tag a resource with its associated workload or application name and use software such as Azure DevOps to perform automated tasks on those resources.
How do I manage resource tags?
You can add, modify, or delete resource tags through Windows PowerShell, the Azure CLI, Azure Resource Manager templates, the REST API, or the Azure portal.
You can use Azure Policy to enforce tagging rules and conventions. For example, you can require that certain tags be added to new resources as they’re provisioned. You can also define rules that reapply tags that have been removed. Resources don’t inherit tags from subscriptions and resource groups, meaning that you can apply tags at one level and not have those tags automatically show up at a different level, allowing you to create custom tagging schemas that change depending on the level (resource, resource group, subscription, and so on).
An example tagging structure
A resource tag consists of a name and a value. You can assign one or more tags to each Azure resource.
Name
Value
AppName
The name of the application that the resource is part of.
CostCenter
The internal cost center code.
Owner
The name of the business owner who’s responsible for the resource.
Environment
An environment name, such as “Prod,” “Dev,” or “Test.”
Impact
How important the resource is to business operations, such as “Mission-critical,” “High-impact,” or “Low-impact.”
Keep in mind that you don’t need to enforce that a specific tag is present on all of your resources. For example, you might decide that only mission-critical resources have the Impact tag. All non-tagged resources would then not be considered as mission-critical.
Microsoft Azure is a global cloud provider, meaning you can provision resources anywhere in the world. You can provision resources rapidly to meet a sudden demand, or to test out a new feature, or on accident. If you accidentally provision new resources, you may not be aware of them until it’s time for your invoice. Cost Management is a service that helps avoid those situations.
What is Cost Management?
Cost Management provides the ability to quickly check Azure resource costs, create alerts based on resource spend, and create budgets that can be used to automate management of resources.
Cost analysis is a subset of Cost Management that provides a quick visual for your Azure costs. Using cost analysis, you can quickly view the total cost in a variety of different ways, including by billing cycle, region, resource, and so on.
You use cost analysis to explore and analyze your organizational costs. You can view aggregated costs by organization to understand where costs are accrued and to identify spending trends. And you can see accumulated costs over time to estimate monthly, quarterly, or even yearly cost trends against a budget.
Cost alerts
Cost alerts provide a single location to quickly check on all of the different alert types that may show up in the Cost Management service. The three types of alerts that may show up are:
Budget alerts
Credit alerts
Department spending quota alerts.
Budget alerts
Budget alerts notify you when spending, based on usage or cost, reaches or exceeds the amount defined in the alert condition of the budget. Cost Management budgets are created using the Azure portal or the Azure Consumption API.
In the Azure portal, budgets are defined by cost. Budgets are defined by cost or by consumption usage when using the Azure Consumption API. Budget alerts support both cost-based and usage-based budgets. Budget alerts are generated automatically whenever the budget alert conditions are met. You can view all cost alerts in the Azure portal. Whenever an alert is generated, it appears in cost alerts. An alert email is also sent to the people in the alert recipients list of the budget.
Credit alerts
Credit alerts notify you when your Azure credit monetary commitments are consumed. Monetary commitments are for organizations with Enterprise Agreements (EAs). Credit alerts are generated automatically at 90% and at 100% of your Azure credit balance. Whenever an alert is generated, it’s reflected in cost alerts, and in the email sent to the account owners.
Department spending quota alerts
Department spending quota alerts notify you when department spending reaches a fixed threshold of the quota. Spending quotas are configured in the EA portal. Whenever a threshold is met, it generates an email to department owners, and appears in cost alerts. For example, 50 percent or 75 percent of the quota.
Budgets
A budget is where you set a spending limit for Azure. You can set budgets based on a subscription, resource group, service type, or other criteria. When you set a budget, you will also set a budget alert. When the budget hits the budget alert level, it will trigger a budget alert that shows up in the cost alerts area. If configured, budget alerts will also send an email notification that a budget alert threshold has been triggered.
A more advanced use of budgets enables budget conditions to trigger automation that suspends or otherwise modifies resources once the trigger condition has occurred.
