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Data sources supported in Azure Analysis Services

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Data sources and connectors shown in Get Data or Table Import Wizard in Visual Studio with Analysis Services projects are shown for both Azure Analysis Services and SQL Server Analysis Services. However, not all data sources and connectors shown are supported in Azure Analysis Services. The types of data sources you can connect to depend on many factors such as model compatibility level, available data connectors, authentication type, and On-premises data gateway support. The following tables describe supported data sources for Azure Analysis Services.

Azure data sources

1 - Tabular 1400 and higher models only. 2 - When specified as a provider data source in tabular 1200 and higher models, both in-memory and DirectQuery models require Microsoft OLE DB Driver for SQL Server MSOLEDBSQL (recommended) or .NET Framework Data Provider for SQL Server. 3 - Azure SQL Managed Instance is supported. Because SQL Managed Instance runs within Azure VNet with a private IP address, public endpoint must be enabled on the instance. If not enabled, an On-premises data gateway is required. 4 - Azure Databricks using the Spark connector is currently not supported. 5 - ADLS Gen2 connector is currently not supported, however, Azure Blob Storage connector can be used with an ADLS Gen2 data source.

Other data sources

Connecting to on-premises data sources from an Azure Analysis Services server require an On-premises gateway . When using a gateway, 64-bit providers are required.

Notes: 6 - Tabular 1400 and higher models only. 7 - When specified as a provider data source in tabular 1200 and higher models, specify Microsoft OLE DB Driver for SQL Server MSOLEDBSQL (recommended), SQL Server Native Client 11.0, or .NET Framework Data Provider for SQL Server. 8 - If specifying MSOLEDBSQL as the data provider, it may be necessary to download and install the Microsoft OLE DB Driver for SQL Server on the same computer as the On-premises data gateway. 9 - For tabular 1200 models, or as a provider data source in tabular 1400+ models, specify Oracle Data Provider for .NET. If specified as a structured data source, be sure to enable Oracle managed provider . 10 - For tabular 1200 models, or as a provider data source in tabular 1400+ models, specify Teradata Data Provider for .NET. 11 - Files in on-premises SharePoint aren't supported. 12 - Azure Analysis Services doesn't support direct connections to the Dynamics 365 Dataverse TDS endpoint . When connecting to this data source from Azure Analysis Services, you must use an On-premises Data Gateway and refresh the tokens manually. 13 - Azure Analysis Services doesn't support direct connections to MySQL databases. When connecting to this data source from Azure Analysis Services, you must use an On-premises Data Gateway and refresh the tokens manually.

Understanding providers

When creating tabular 1400 and higher model projects in Visual Studio, by default you don't specify a data provider when connecting to a data source by using Get Data. Tabular 1400 and higher models use Power Query connectors to manage connections, data queries, and mashups between the data source and Analysis Services. These are sometimes referred to as structured data source connections in that connection property settings are set for you. You can, however, enable legacy data sources for a model project in Visual Studio. When enabled, you can use Table Import Wizard to connect to certain data sources traditionally supported in tabular 1200 and lower models as legacy , or provider data sources. When specified as a provider data source, you can specify a particular data provider and other advanced connection properties. For example, you can connect to a SQL Server Data Warehouse instance or even an Azure SQL Database as a legacy data source. You can then select the OLE DB Driver for SQL Server MSOLEDBSQL data provider. In this case, selecting an OLE DB data provider may provide improved performance over the Power Query connector.

When using the Table Import Wizard in Visual Studio, connections to any data source require a data provider. A default data provider is selected for you. You can change the data provider if needed. The type of provider you choose might depend on performance, whether or not the model is using in-memory storage or DirectQuery, and which Analysis Services platform you deploy your model to.

Specify provider data sources in tabular 1400 and higher model projects

To enable provider data sources, in Visual Studio, click Tools > Options > Analysis Services Tabular > Data Import , select Enable legacy data sources .

Screenshot of Enable legacy data sources.

With legacy data sources enabled, in Tabular Model Explorer , right-click Data Sources > Import From Data Source (Legacy) .

Screenshot of Legacy data sources in Tabular Model Explorer.

Just like with tabular 1200 model projects, use Table Import Wizard to connect to a data source. On the connect page, click Advanced . Specify data provider and other connection settings in Set Advanced Properties .

Screenshot of Legacy data sources Advanced properties.

Impersonation

In some cases, it may be necessary to specify a different impersonation account. Impersonation account can be specified in Visual Studio or SQL Server Management Studio (SSMS).

For on-premises data sources:

  • If using SQL authentication, impersonation should be Service Account.
  • If using Windows authentication, set Windows user/password. For SQL Server, Windows authentication with a specific impersonation account is supported only for in-memory data models.

For cloud data sources:

OAuth credentials

For tabular models at the 1400 and higher compatibility level using in-memory mode, Azure SQL Database, Azure Synapse, Dynamics 365, and SharePoint List support OAuth credentials. To generate valid tokens, set credentials by using Power Query. Azure Analysis Services manages token refresh for OAuth data sources to avoid timeouts for long-running refresh operations.

Managed token refresh is not supported for data sources accessed through a gateway. For example, one or more mashup query data sources is accessed through a gateway, and/or the ASPaaS\AlwaysUseGateway property is set to true .

Direct Query mode is not supported with OAuth credentials.

Enable Oracle managed provider

In some cases, DAX queries to an Oracle data source may return unexpected results. This might be due to the provider being used for the data source connection.

As described in the Understanding providers section, tabular models connect to data sources as either a structured data source or a provider data source. For models with an Oracle data source specified as a provider data source, ensure the specified provider is Oracle Data Provider for .NET (Oracle.DataAccess.Client).

If the Oracle data source is specified as a structured data source, enable the MDataEngine\UseManagedOracleProvider server property. Setting this property ensures your model connects to the Oracle data source using the recommended Oracle Data Provider for .NET managed provider.

To enable Oracle managed provider:

In SQL Server Management Studio, connect to your server.

Create an XMLA query with the following script. Replace ServerName with the full server name, and then execute the query.

Restart the server.

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The SQL Analysis Services ODBC Driver is a powerful tool that allows you to connect with live data from SQL Analysis Services, directly from any applications that support ODBC connectivity.

Access Analysis Services report data like you would a database, through a standard ODBC Driver interface. Supports Direct Query and MDX query capabilities.

In this article

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Access SQL Analysis Services Data as a Remote Oracle Database

The Oracle Database Gateway for ODBC and Heterogeneous Services technology enable you to connect to ODBC data sources as remote Oracle databases. This article shows how to use the CData ODBC Driver for SQL Analysis Services to create a database link from SQL Analysis Services to Oracle and to query SQL Analysis Services data through the SQL*Plus tool. You can also create the database link and execute queries from SQL Developer.

Connect to SQL Analysis Services as an ODBC Data Source

Information for connecting to SQL Analysis Services follows, along with different instructions for configuring a DSN in Windows and Linux environments.

To connect, provide authentication and set the Url property to a valid SQL Server Analysis Services endpoint. You can connect to SQL Server Analysis Services instances hosted over HTTP with XMLA access. See the Microsoft documentation to configure HTTP access to SQL Server Analysis Services.

To secure connections and authenticate, set the corresponding connection properties, below. The data provider supports the major authentication schemes, including HTTP and Windows, as well as SSL/TLS.

Set AuthScheme to "Basic" or "Digest" and set User and Password. Specify other authentication values in CustomHeaders.

Set the Windows User and Password and set AuthScheme to "NTLM".

To authenticate with Kerberos, set AuthScheme to NEGOTIATE. To use Kerberos delegation, set AuthScheme to KERBEROSDELEGATION. If needed, provide the User, Password, and KerberosSPN. By default, the data provider attempts to communicate with the SPN at the specified Url.

By default, the data provider attempts to negotiate SSL/TLS by checking the server's certificate against the system's trusted certificate store. To specify another certificate, see the SSLServerCert property for the available formats.

You can then access any cube as a relational table: When you connect the data provider retrieves SSAS metadata and dynamically updates the table schemas. Instead of retrieving metadata every connection, you can set the CacheLocation property to automatically cache to a simple file-based store.

See the Getting Started section of the CData documentation, under Retrieving Analysis Services Data, to execute SQL-92 queries to the cubes.

If you have not already, first specify connection properties in an ODBC DSN (data source name). This is the last step of the driver installation. You can use the Microsoft ODBC Data Source Administrator to create and configure ODBC DSNs.

Note : If you need to modify the DSN or create other SQL Analysis Services DSNs, you must use a system DSN and the bitness of the DSN must match your Oracle system. You can access and create 32-bit DSNs on a 64-bit system by opening the 32-bit ODBC Data Source Administrator from C:\Windows\SysWOW64\odbcad32.exe.

If you are installing the CData ODBC Driver for SQL Analysis Services in a Linux environment, the driver installation predefines a system DSN. You can modify the DSN by editing the system data sources file ( /etc/odbc.ini ) and defining the required connection properties.

/etc/odbc.ini

For specific information on using these configuration files, please refer to the help documentation (installed and found online).

Set Connection Properties for Compatibility with Oracle

The driver provides several connection properties that streamline accessing SQL Analysis Services data just as you would an Oracle database. Set the following properties when working with SQL Analysis Services data in SQL*Plus and SQL Developer. For compatibility with Oracle, you will need to set the following connection properties, in addition to authentication and other required connection properties.

MapToWVarchar=False

Set this property to map string data types to SQL_VARCHAR instead of SQL_WVARCHAR. By default, the driver uses SQL_WVARCHAR to accommodate various international character sets. You can use this property to avoid the ORA-28528 Heterogeneous Services data type conversion error when the Unicode type is returned.

MaximumColumnSize=4000

Set this property to restrict the maximum column size to 4000 characters.

IncludeDualTable=True

Set this property to mock the Oracle DUAL table. SQL Developer uses this table to test the connection.

Linux Configuration

In Linux environments, Oracle uses UTF-8 to communicate with the unixODBC Driver manager, whereas the default driver encoding is UTF-16. To resolve this, open the file /opt/cdata/cdata-driver-for-ssas/lib/cdata.odbc.ssas.ini in a text editor and set the encoding.

cdata.odbc.ssas.ini

Configure the odbc gateway, oracle net, and oracle database.

Follow the procedure below to set up an ODBC gateway to SQL Analysis Services data that enables you to query live SQL Analysis Services data as an Oracle database.

Create the file initmyssasdb.ora in the folder oracle-home-directory /hs/admin and add the following setting:

initmyssasdb.ora

If you are using the Database Gateway for ODBC, your listener.ora needs to have a SID_LIST_LISTENER entry that resembles the following:

listener.ora

If you are using Heterogeneous Services, your listener.ora needs to have a SID_LIST_LISTENER entry that resembles the following:

Add the connect descriptor below in tnsnames.ora, located in oracle-home-directory /NETWORK/admin:

tnsnames.ora

Test the configuration with the following command:

Open SQL*Plus and create the database link with the command below:

You can now execute queries in SQL*Plus like the one below (note the double quotation marks around the table name):

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Leveraging Logging Analytics for Oracle Integration Cloud Logging and Monitoring - Part 1

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Oracle Integration Cloud is a fully managed, preconfigured environment that gives you the power to integrate your Oracle Cloud Infrastructure applications and services and on-premises applications. With Oracle Integration Cloud, you can:

  • Develop integrations to design, monitor, and manage connections between your applications.
  • Create process applications to automate and manage your business work flows.
  • Build custom web and mobile applications.
  • Store and retrieve files in Oracle Integration using the embedded SFTP-compliant file server.
  • Create integrations that use B2B e-commerce to extend business processes to reach trading partners.

As more customers are onboarded to Oracle Cloud Infrastructure (OCI) and run their critical integrations between OCI cloud services. Having a robust observability and monitoring solution for Oracle Integration Cloud (OIC) is pivotal for ensuring the efficiency, reliability and security of Oracle Integration solutions. It enables the organizations to maintain oversight over their integrations, diagnose issues promptly, and optimize performance. 

Out-of-the-box Oracle Integration Cloud monitoring dashboards

Oracle Integration Cloud offers out-of-the-box monitoring capabilities such as using the Oracle Integration dashboard to monitor and manage your integrations in the runtime environment. 

  • Runtime Health
  • System Health
  • Agent Health
  • Integrations Stats
  • OIC Scheduling
  • Design Time Metrics

You can also view information about how your integrations are performing. You can find more details of OIC native monitoring dashboard in OIC documentation.  

Figure 1. Oracle Integration Cloud Monitoring Dashboard

In the meantime, OCI Logging Analytics service takes the OIC logging and monitoring challenges further by delivering out-of-the-box OIC dashboards for all OCI customers. Oracle Logging Analytics is a cloud solution in Oracle Cloud Infrastructure that lets you index, enrich, aggregate, explore, search, analyze, correlate, visualize, and monitor all log data from your applications and system infrastructure.

OCI Logging Analytics takes the following OIC metrics and OIC activitiy stream logs as the telemetry sources for the out-of-the-box dashboards:

  • OIC Service Metrics – Monitoring namespace: Integration
  • OIC Activity Stream Logs – Logging Service Logs

Figure 2. Oracle Integration: Health Overview

Oracle Integration Cloud Design Time Audit Logs Use Cases

Oracle Integration Cloud also provides comprehensive record of changes and actions taken within the design-time environment of OIC. The Design Time Audit log data is instrumental for security, compliance, troubleshooting, and governance reasons. It provides a critical layer of visibility and control that is essential for managing the Oracle Integration Cloud instances.

  • Infrastructure operation teams can view and track the changes across the OIC integrations and configurations
  • Security and governance teams can monitor the accesses and changes in OIC to detect unauthorized or suspicious activities for operational transparency and collaboration
  • Design Time Audit Log can be a valuable resource for integration developers to identify and troubleshoot issues for faster problem resolution

Figure 5. Oracle Integration Cloud Design Time Audit Log Records

There are two ways of accessing Design Time Audit Log records, and Design Time Audit Log records are excluded from the OIC activity stream logs, therefore there is no out-of-the-box integration between OIC Audit Log records and OCI Logging service logs.

  • Using OIC console
  • Via OIC REST API endpoints

Next, we will walk you through the strategies to enable the OIC Design Time Audit Logs in OCI Logging Analytics, so that you can:

  • Enable short term reporting on the OIC Audit logs
  • Retain OIC Audit logs in long term archive respository

Oracle Cloud Integration Audit Logs Ingestion Strategies

We have three log ingestion strategies for OIC Audit logs (applicable to both OIC Gen2 and OIC Gen3), each strategy maps to specific monitoring use cases with the pros and cons. Based on our researches and testing, we recommend the push method via OIC Custom Integration for our customers to simplify the configuration complexity and reduce the overall operation overhead.

Recommended Option:

  • GET OIC Audit Logs via OIC REST API Endpoint into a stage file
  • Push OIC stage file to Logging Analytics via LA Log Upload REST API
  • (Optional) Use Logging Analytics Archival Storage Tier for long term OIC Audit Logs retention with lower cost
  • (Optional) Push OIC Audit Logs into Object Storage bucket and upload to Logging Analytics via Object Collection Rule
  • (Optional) Store the OIC Audit Logs in Object Storage for long term compliance and regulartory requirement
  • Highly customizable custom integration to orchestrate the OIC Audit Logs ingestion
  • Flexible long term OIC Audit Logs retention options
  • Additional workload impact on the OIC instances

Figure 6. OIC Design Time Audit Logs Ingestion Push Method

Other Options:

  • GET OIC Audit Logs via OIC REST API Endpoint
  • Merge OIC Audit logs into OIC Activity Stream Logs via Logger Action
  • (Optional) Use Logging Analytics Archival Storage Tier for long term OIC Audit Logs retention with lower cost
  • (Optional) Push OIC Audit Logs into Object Storage bucket via Service Connector Hub for long term retention
  • OIC Audit Logs will be part of the OIC Activity Stream service logs, no need to stage additional files
  • Single REST API call within the custom integration
  • OIC Audit Log records are XML format in the Activity Stream service elogs, additional parsing is required for the embedded XML format
  • Additional workload impact on the OIC instnaces
  • Via Management Agent Logging Analytics Plugin
  • Pull OIC Audit logs via API endpoint in Management Agent and forward OIC Audit Logs to Logging Analytics
  • No custom integration required for OIC instance
  • Native Logging Analytics REST API Log Ingestion via Management Agent
  • Need to maintain an additional VM
  • Logging Analytics Entity will be associated with the Management Agent VM, not with the OIC instance

Visualization and Dashboard

After successful ingestion of OIC Audit logs into Logging Analytics, we can query and visualize the OIC Audit logs and create widgets for Oracle Integration Cloud Audit Analysis dashboard.

Figure 6. Oracle Integration: Audit Analysis Sample Dashboard

Incorporating OIC Design Time Audit Logs into OCI Logging Analytics represents a strategic approach to maximizing the operational intelligence and security posture of cloud integration environments. By ingesting these detailed change records into OCI Logging Analytics, organizations unlock the potential to transform raw data into actionable insights to foster a more secure, efficient, and compliant integration ecosystem. Furthermore, the aggregation of OIC Audit Logs in Logging Analytics facilitates a more robust compliance framework, offering an aggregated view of activities across the integration landscape that is invaluable for audit trails and regulatory adherence. 

Further Reading

  • Monitor Integrations OIC GEN2 
  • Monitor Integrations OIC GEN3 
  • Review the OIC RESTful API for monitoring integration
  • Surfacing OIC Design Time Audit blog
  • OIC and OCI Logging Analytics
  • Collect Logs from OCI Object Storage bucket
  • Logging Analytics REST API Log Ingestion

Acknowledgements

  • Contributor: Nolan Trouvé

Principal Database and O&M Solution Architect

Royce Fu is the Principal Database Solution Architect of the North America Cloud Technology and Engineering Team. Royce's area of specialty is core Database Technology and OCI O&M especially in Database Platform Engineering, Architecture, and Integration. He started his career as Java software engineer and spent over a decade in database engineering and architecture.

Royce Fu is the Principal Database Solution Architect of the North America Cloud Technology and Engineering Team. Royce's area of specialty is core Database Technology and OCI O&M especially in Database Platform Engineering, Architecture, and Integration. He started his career as Java software engineer and spent over a decade in database engineering and architecture.

Oracle Integration Cloud is a fully managed, preconfigured environment that gives you the power to integrate your Oracle Cloud Infrastructure applications and services and on-premises applications. \nWith Oracle Integration Cloud, you can:

As more customers are onboarded to Oracle Cloud Infrastructure (OCI) and run their critical integrations between OCI cloud services. Having a robust observability and monitoring solution for Oracle Integration Cloud (OIC) is pivotal for ensuring the efficiency, reliability and security of Oracle Integration solutions. It enables the organizations to maintain oversight over their integrations, diagnose issues promptly, and optimize performance. 

Oracle Integration Cloud offers out-of-the-box monitoring capabilities such as using the Oracle Integration dashboard to monitor and manage your integrations in the runtime environment. 

You can also view information about how your integrations are performing. You can find more details of OIC native monitoring dashboard in OIC documentation. \n 

  • OIC Service Metrics – Monitoring namespace: Integration
  • OIC Activity Stream Logs – Logging Service Logs

Oracle Integration Cloud also provides comprehensive record of changes and actions taken within the design-time environment of OIC. The Design Time Audit log data is instrumental for security, compliance, troubleshooting, and governance reasons. It provides a critical layer of visibility and control that is essential for managing the Oracle Integration Cloud instances.

  • (Optional) Use Logging Analytics Archival Storage Tier for long term OIC Audit Logs retention with lower cost
  • Logging Analytics Entity will be associated with the Management Agent VM, not with the OIC instance

Incorporating OIC Design Time Audit Logs into OCI Logging Analytics represents a strategic approach to maximizing the operational intelligence and security posture of cloud integration environments. By ingesting these detailed change records into OCI Logging Analytics, organizations unlock the potential to transform raw data into actionable insights to foster a more secure, efficient, and compliant integration ecosystem. Furthermore, the aggregation of OIC Audit Logs in Logging Analytics facilitates a more robust compliance framework, offering an aggregated view of activities across the integration landscape that is invaluable for audit trails and regulatory adherence. 

  • Monitor Integrations OIC GEN2 
  • Monitor Integrations OIC GEN3 
  • Contributor: Nolan Trouvé

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MoSCoW Prioritization

What is moscow prioritization.

MoSCoW prioritization, also known as the MoSCoW method or MoSCoW analysis, is a popular prioritization technique for managing requirements. 

  The acronym MoSCoW represents four categories of initiatives: must-have, should-have, could-have, and won’t-have, or will not have right now. Some companies also use the “W” in MoSCoW to mean “wish.”

What is the History of the MoSCoW Method?

Software development expert Dai Clegg created the MoSCoW method while working at Oracle. He designed the framework to help his team prioritize tasks during development work on product releases.

You can find a detailed account of using MoSCoW prioritization in the Dynamic System Development Method (DSDM) handbook . But because MoSCoW can prioritize tasks within any time-boxed project, teams have adapted the method for a broad range of uses.

How Does MoSCoW Prioritization Work?

Before running a MoSCoW analysis, a few things need to happen. First, key stakeholders and the product team need to get aligned on objectives and prioritization factors. Then, all participants must agree on which initiatives to prioritize.

At this point, your team should also discuss how they will settle any disagreements in prioritization. If you can establish how to resolve disputes before they come up, you can help prevent those disagreements from holding up progress.

Finally, you’ll also want to reach a consensus on what percentage of resources you’d like to allocate to each category.

With the groundwork complete, you may begin determining which category is most appropriate for each initiative. But, first, let’s further break down each category in the MoSCoW method.

Start prioritizing your roadmap

Moscow prioritization categories.

Moscow

1. Must-have initiatives

As the name suggests, this category consists of initiatives that are “musts” for your team. They represent non-negotiable needs for the project, product, or release in question. For example, if you’re releasing a healthcare application, a must-have initiative may be security functionalities that help maintain compliance.

The “must-have” category requires the team to complete a mandatory task. If you’re unsure about whether something belongs in this category, ask yourself the following.

moscow-initiatives

If the product won’t work without an initiative, or the release becomes useless without it, the initiative is most likely a “must-have.”

2. Should-have initiatives

Should-have initiatives are just a step below must-haves. They are essential to the product, project, or release, but they are not vital. If left out, the product or project still functions. However, the initiatives may add significant value.

“Should-have” initiatives are different from “must-have” initiatives in that they can get scheduled for a future release without impacting the current one. For example, performance improvements, minor bug fixes, or new functionality may be “should-have” initiatives. Without them, the product still works.

3. Could-have initiatives

Another way of describing “could-have” initiatives is nice-to-haves. “Could-have” initiatives are not necessary to the core function of the product. However, compared with “should-have” initiatives, they have a much smaller impact on the outcome if left out.

So, initiatives placed in the “could-have” category are often the first to be deprioritized if a project in the “should-have” or “must-have” category ends up larger than expected.

4. Will not have (this time)

One benefit of the MoSCoW method is that it places several initiatives in the “will-not-have” category. The category can manage expectations about what the team will not include in a specific release (or another timeframe you’re prioritizing).

Placing initiatives in the “will-not-have” category is one way to help prevent scope creep . If initiatives are in this category, the team knows they are not a priority for this specific time frame. 

Some initiatives in the “will-not-have” group will be prioritized in the future, while others are not likely to happen. Some teams decide to differentiate between those by creating a subcategory within this group.

How Can Development Teams Use MoSCoW?

  Although Dai Clegg developed the approach to help prioritize tasks around his team’s limited time, the MoSCoW method also works when a development team faces limitations other than time. For example: 

Prioritize based on budgetary constraints.

What if a development team’s limiting factor is not a deadline but a tight budget imposed by the company? Working with the product managers, the team can use MoSCoW first to decide on the initiatives that represent must-haves and the should-haves. Then, using the development department’s budget as the guide, the team can figure out which items they can complete. 

Prioritize based on the team’s skillsets.

A cross-functional product team might also find itself constrained by the experience and expertise of its developers. If the product roadmap calls for functionality the team does not have the skills to build, this limiting factor will play into scoring those items in their MoSCoW analysis.

Prioritize based on competing needs at the company.

Cross-functional teams can also find themselves constrained by other company priorities. The team wants to make progress on a new product release, but the executive staff has created tight deadlines for further releases in the same timeframe. In this case, the team can use MoSCoW to determine which aspects of their desired release represent must-haves and temporarily backlog everything else.

What Are the Drawbacks of MoSCoW Prioritization?

  Although many product and development teams have prioritized MoSCoW, the approach has potential pitfalls. Here are a few examples.

1. An inconsistent scoring process can lead to tasks placed in the wrong categories.

  One common criticism against MoSCoW is that it does not include an objective methodology for ranking initiatives against each other. Your team will need to bring this methodology to your analysis. The MoSCoW approach works only to ensure that your team applies a consistent scoring system for all initiatives.

Pro tip: One proven method is weighted scoring, where your team measures each initiative on your backlog against a standard set of cost and benefit criteria. You can use the weighted scoring approach in ProductPlan’s roadmap app .

2. Not including all relevant stakeholders can lead to items placed in the wrong categories.

To know which of your team’s initiatives represent must-haves for your product and which are merely should-haves, you will need as much context as possible.

For example, you might need someone from your sales team to let you know how important (or unimportant) prospective buyers view a proposed new feature.

One pitfall of the MoSCoW method is that you could make poor decisions about where to slot each initiative unless your team receives input from all relevant stakeholders. 

3. Team bias for (or against) initiatives can undermine MoSCoW’s effectiveness.

Because MoSCoW does not include an objective scoring method, your team members can fall victim to their own opinions about certain initiatives. 

One risk of using MoSCoW prioritization is that a team can mistakenly think MoSCoW itself represents an objective way of measuring the items on their list. They discuss an initiative, agree that it is a “should have,” and move on to the next.

But your team will also need an objective and consistent framework for ranking all initiatives. That is the only way to minimize your team’s biases in favor of items or against them.

When Do You Use the MoSCoW Method for Prioritization?

MoSCoW prioritization is effective for teams that want to include representatives from the whole organization in their process. You can capture a broader perspective by involving participants from various functional departments.

Another reason you may want to use MoSCoW prioritization is it allows your team to determine how much effort goes into each category. Therefore, you can ensure you’re delivering a good variety of initiatives in each release.

What Are Best Practices for Using MoSCoW Prioritization?

If you’re considering giving MoSCoW prioritization a try, here are a few steps to keep in mind. Incorporating these into your process will help your team gain more value from the MoSCoW method.

1. Choose an objective ranking or scoring system.

Remember, MoSCoW helps your team group items into the appropriate buckets—from must-have items down to your longer-term wish list. But MoSCoW itself doesn’t help you determine which item belongs in which category.

You will need a separate ranking methodology. You can choose from many, such as:

  • Weighted scoring
  • Value vs. complexity
  • Buy-a-feature
  • Opportunity scoring

For help finding the best scoring methodology for your team, check out ProductPlan’s article: 7 strategies to choose the best features for your product .

2. Seek input from all key stakeholders.

To make sure you’re placing each initiative into the right bucket—must-have, should-have, could-have, or won’t-have—your team needs context. 

At the beginning of your MoSCoW method, your team should consider which stakeholders can provide valuable context and insights. Sales? Customer success? The executive staff? Product managers in another area of your business? Include them in your initiative scoring process if you think they can help you see opportunities or threats your team might miss. 

3. Share your MoSCoW process across your organization.

MoSCoW gives your team a tangible way to show your organization prioritizing initiatives for your products or projects. 

The method can help you build company-wide consensus for your work, or at least help you show stakeholders why you made the decisions you did.

Communicating your team’s prioritization strategy also helps you set expectations across the business. When they see your methodology for choosing one initiative over another, stakeholders in other departments will understand that your team has thought through and weighed all decisions you’ve made. 

If any stakeholders have an issue with one of your decisions, they will understand that they can’t simply complain—they’ll need to present you with evidence to alter your course of action.  

Related Terms

2×2 prioritization matrix / Eisenhower matrix / DACI decision-making framework / ICE scoring model / RICE scoring model

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What is a MoSCoW Analysis? Definition, Use Guide, and Analysis

By Paul VanZandt

Published on: July 26, 2023

MoSCoW Analysis

Table of Contents

What is a Moscow Analysis?

Moscow analysis use guide, how to do a moscow analysis.

Prioritization and organization are two essential elements in creating a successful project and are also things that are inherently harder to achieve online. While prioritizing elements can be hard online, it doesn’t have to be. The Moscow analysis is a great tool for teams to collaborate on through online whiteboards and has takeaways that are applicable to a variety of different projects and teams.

In this article, we will define the Moscow analysis and talk about what makes it so helpful to teams everywhere. If you are interested in reading some of our other template guides, you can check out our most recent guides on design thinking and using a business model canvas here.

A Moscow analysis , also known as Moscow prioritization, is defined as an organizational framework that helps clarify and prioritize features or requirements for a given project. By creating boundaries for the priorities, teams are able to narrow their focus and create direct and achievable goals.

Moscow is an acronym that stands for the four categories that various features can be sorted into. These categories are: Must have, Should have, Could have, and Won’t have These four categories determine the prioritization of the corresponding features and are a marker of their importance to the overall success and continuity of the project.

  • Must-haves: These are the essential requirements that must be included in the project or product. If any of these requirements are not met, the project or product cannot be considered successful.
  • Should-haves: These are important requirements that should be included if possible. They are not absolutely critical but add significant value to the project or product.
  • Could-haves: These are requirements that are nice to have, but they are not critical. They can be considered if time and resources allow but can be deferred if necessary.
  • Won’t-haves: These are requirements that are explicitly out of scope for the current project or product. These requirements are not currently under consideration.

Moscow short

While the Moscow analysis is most often used to organize a project and its required elements, it can also be used in other scenarios . For example, Moscow prioritization can be applied to better align a team with its values and expectations. It can also be used to prioritize takeaways and next steps from an important meeting. Its main goal is the help visualize the prioritization of the tasks at hand.

These use cases demonstrate the flexibility of the Moscow prioritization to break down important requirements into simple prioritized areas, whether it be for team expectations or a project sprint.

As previously stated, the Moscow analysis consists of four major elements. These categories are explained below alongside some questions to guide what should be included in each category. For the sake of simplicity, we will use a project prioritization for reference.

This section is where you think about the core features that are necessary to the success of the project. Must have features are things that, if absent, would compromise the project as a whole. Without these features, the project would have an entirely different function and wouldn’t serve the intended purpose.

Must have features, while being the most important things to consider, should not account for every detail that will be present in the final version. The features in must have, should have, and could have should all be major considerations to be included in the project, so try and be very specific with the features you add in each section.

Some prompting questions to ask in this section could be:

  • What features are absolutely essential and cannot be replaced?
  • If removed, would the project achieve the same purpose?
  • Will the delivery of the project be a success without this feature?

Should Have

Should have is where the project begins to become more nuanced in its prioritization. Should have features include those that are supplemental to the must have features, things customers have vocalized interest in, and other features that would make meaningful additions to the project.

Should have features should be thought of as just a step below must have. These features, while important, could be pushed to a later release while the must have features are absolutely essential. Without these things, the project will still work, but it will be better with them.

Some prompting questions to ask in this section could be;

  • How does this feature compare to the must have features? What about the could have features?
  • What is a helpful but not required feature?
  • How would the project function if this feature is omitted?

Priority list

Could have features are often misunderstood and get lumped with random possible additions. This section is meant to highlight features that you want to include but aren’t sure if they will be possible.

Could have features are even a step lower on the prioritization of should have features due to either time or substantive restraints. These are features that would be nice additions, but might not directly impact the core function of the product.

  • What would be a useful tool to add that isn’t a priority?
  • What is something that you’d like to add in the future?
  • How would this feature impact the overall product?

Won’t have is one of the most important sections in the analysis. It defines all of the features and points that specifically will not be included in the project release. This section is critical because it narrows the scope of the project greatly and helps define the boundaries that must be followed to achieve a successful project.

In order to have a helpful won’t have section, you need to plan not only the project you’re working on but future projects and parallel endeavors as well. By thinking about what comes in the future and what exists outside of the current release, you are able to narrow the scope of the current project.

  • What features will be purposefully left out of this project?
  • What is being avoided or postponed for a future release?
  • What features fall outside of this releases specific scope?

Learn more: SWOT Analysis Framework

MOSCOW analysis helps teams make informed decisions about what to prioritize and what can be deferred or excluded, leading to more effective project or product development. Here’s how to perform a MOSCOW analysis:

  • Identify Stakeholders: Gather the key stakeholders and decision-makers involved in the project or product development. It’s essential to have a clear understanding of their needs and expectations.
  • List Requirements or Features: Make a comprehensive list of all the potential requirements or features that have been proposed for the project or product. This list can come from user stories, feature requests, or other sources.
  • Categorize Requirements: For each requirement or feature, categorize it into one of the four MOSCOW categories (Must-have, Should-have, Could-have, or Won’t-have). You can do this collaboratively with the stakeholders, using their input to make informed decisions.
  • Prioritize Must-Haves: Focus on the “Must-have” category and ensure that these requirements are prioritized above all else. These are the non-negotiable elements of the project.
  • Prioritize Should-Haves: Once the Must-haves are defined, move on to the “Should-have” category and prioritize these based on their relative importance and impact on the project or product.
  • Consider Could-Haves: Evaluate the “Could-have” category and decide which of these features or requirements are feasible to include, given the available resources and time.
  • Exclude Won’t-Haves: Ensure that the “Won’t-have” category is clearly communicated and understood. These are the features or requirements that will not be addressed in the current project or product.
  • Document the Analysis: Record the results of the MOSCOW analysis in a document or spreadsheet so that all stakeholders have a clear understanding of the prioritization decisions.
  • Review and Iterate: Periodically review and update the MOSCOW analysis as the project or product evolves. Changes in scope or stakeholder priorities may necessitate adjustments.

Learn more: What is PESTEL Analysis?

Using a Moscow analysis is one of the best ways to improve the alignment of a team and understand the prioritization of the project at hand. While these templates are mainly used for product management, they are extremely versatile and can be applied to many different scenarios .

Hopefully, this guide has been helpful, and if so make sure to check out our other posts around online whiteboards and visual collaboration if you want to learn more about how to interact and collaborate online.

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How to do a MoSCoW Analysis and prioritise requirements in a complex environment?

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How to do a MoSCoW Analysis and prioritise requirements effectively in a complex environment?

As a Business Analyst, the question of how to prioritise requirements may seem like an easy question to answer but it can also be wrought with a variety of complications and interesting complexities. Once you have overcome these potential complexities which can come with requirements prioritisation, the most relevant Business Analysis technique to apply is what is known as the MoSCoW Analysis.

This blog article will cover both how to apply the MoSCoW Analysis for requirements prioritisation as well as the considerations and complexities for a Business Analyst to understand about their environment before attempting to prioritise requirements.

So let’s start by talking about some of these complexities that can face a Business Analyst when it comes to requirements prioritisation.

#1: Different perspectives on what is important

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#2: Lack of leadership

This factor walks hand in hand with the previous factor (and is most likely the cause of it!) where people have different perspectives on what takes priority. A lack of leadership in the project or initiative team causes confusion around what is important and this is when people will end up providing their own perspective around priorities rather than following business priorities or guidelines. This causes problems in various ways and can put the Business Analyst in a very awkward position. Sometimes this lack of leadership can mean that a stronger or more senior stakeholder might get the requirements prioritised according to his/her team’s preferences due to his/her position and level of influence in the organisation rather than it being the true priorities for the good of the organisation. This leads to requirement priorities which is not necessarily being implemented in the most valuable or efficient manner and consequently reflects badly on the project as a whole.

It is imperative for a Business Analyst to receive clear direction from their project manager or project steering committee about what are the clear business objectives (with their relative priorities outlined) that requirements must deliver against so that the Business Analyst can use these business objective priorities to guide the conversations when requirement prioritisation activities take place.

#3: Not prioritising requirements

In some organisations or projects there is simply no formal and explicit effort undertaken to prioritise requirements at all. This doesn’t mean requirements are not in some sort of priority, it simply means that the requirements are not prioritised in a structured and collaborative way. This type of approach can cause problems when expectations are not managed about what will be delivered by when but it can also be that prioritising the requirements are very clear cut in a particular type of project and hence this informal way works in those circumstances. So although the Business Analyst must be very careful when choosing to not formally go through a requirements prioritisation activity, it can be the most logical and suitable approach for certain types of projects.

#4: Priority levels are not well defined

The last complexity or consideration for the Business Analyst to pay careful attention to before embarking on requirements prioritisation activities are simply the definition of the priority levels and what each priority means. Many organisations have adopted a method or set of priority levels which they are used to using without it necessarily being the most effective way to prioritise.

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So now that we have discussed some of the common complexities in projects and organisations that make requirements prioritisation somewhat tricky for the Business Analyst lets now look at the MoSCoW Analysis technique and how best it can be applied.

The MoSCoW Analysis Technique

The MoSCoW Analysis is a very common and effective requirements prioritisation technique because it allows not only for three clear priority levels but also covers the requirements that will end up not being included in the currently delivery or project at all. This works very well because it allows people to explicitly agree the different priorities including the requirements, which will be excluded or referred to a future release.

Let’s have a look at how this prioritization technique works:

MoSCoW is an acronym.

M = Must ‘Must’ level requirements are those requirements which will definitely be included to be delivered. There is no negotiation around whether they will be delivered and are considered mandatory requirements.

S = Should ‘Should’ level requirements are those requirements which should be included if at all possible. If the project have capacity and time and it will not jeopardise any of the “Must” requirements, then these requirements should be delivered or included in whatever the prioritisation is done for.

C = Could The ‘Could’ level requirements are the requirements which could be included if it doesn’t have any impact on any of the ‘Should’ or ‘Must’ requirements.

W = Won’t The ‘Won’t’ level requirements tend to be the requirements which will not be included to be delivered or implemented this time but are requirements that would be favoured for a future delivery or implementation.

In Conclusion

As a final point to make, it is important that although the Business Analyst uses a best practice requirements technique , the outlined complexities listed here should be addressed as much as possible prior to embarking on a requirements prioritization activity to ensure a successful and accurate outcome.

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  23. What is MoSCoW Prioritization?

    MoSCoW prioritization, also known as the MoSCoW method or MoSCoW analysis, is a popular prioritization technique for managing requirements. The acronym MoSCoW represents four categories of initiatives: must-have, should-have, could-have, and won't-have, or will not have right now. Some companies also use the "W" in MoSCoW to mean "wish.".

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