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Assess your organization’s data maturity and see how to accelerate digital transformation with Azure data services.

Get key insights on your organization's data maturity

Answer these questions to get insights on your organization's data maturity, as defined in the Rethinking the Enterprise white paper. See recommended next steps for your organization and explore curated resources to accelerate your digital transformation.

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1. Do you have a data platform for aggregating data across teams?
2. Are you able to process and run analytics on data in real time (as opposed to batch processing data)?
3. Do you use APIs or other systematic methods to automatically share data externally?
4. Do you have a centralised source of documentation for internal APIs/data-sharing between teams?
5. Do you have systems that automatically check new data sources to ensure they meet quality and/or format requirements?
6. Are you able to track the life cycle or lineage of data as it is transformed and used by models or reports?
7. Do you have a sandbox environment that enables you to test different features and models, and optimise their performance?
8. Does your organisation use advanced machine learning techniques, such as deep learning or reinforcement learning?
9. Are you able to deploy machine learning models automatically without human intervention?
10. Do you automatically archive ML artifacts, enabling future auditing of ML models?
11. Do you use performance reviews or other management tools to enforce compliance with machine learning practices?

Your result: Platform

Your organisation has successfully digitally transformed and is now a leader when it comes to tech intensity. Your organisation most likely has an integrated foundation of data, software and artificial intelligence that supports a mature innovation process, as well as a strong culture of growth and measurement, that empowers employees to collaborate extensively and make individual decisions that are aligned with organisational strategy. Get more information and insights for platform organisations.

Your result: Hub

Your organisation has already taken significant steps towards digital transformation and is now poised to successfully make use of all your organisational assets. At this point, your organisation is most likely looking to improve your processes rather than your technical foundations, and you’re also able to focus on developing and improving the use of analytics and machine learning to drive business performance and transforming your business culture so that your employees can effectively use the new data and analytics tools at their disposal. Get more information and insights for hub organisations.

Your result: Bridge

Your organisation has already made some first steps towards digital transformation. As you continue to establish your data platform, your organisation may face challenges in finding ways to keep building on your initial successes and in determining and prioritising next steps for your data platform. Get more information and insights for bridge organisations.

Your result: Traditional

Your organisation is still in the early stages of its digital transformation and may face challenges in fostering collaboration across organisational boundaries, sharing data and making effective use of your data. Get more information and insights for traditional organisations.

Get key insights for enabling successful data-driven transformations

The Culture of Data Leaders

Explore the role that data culture plays in enabling and reinforcing successful digital transformations.

Read the white paper

Navigating change in your digital transformation journey

Explore key strategies for successful change management during digital transformation.

Read the white paper

Take advantage of limitless scale, limitless performance, and limitless possibilities for your digital transformation—all with Azure data services.

Limitless scale

Scale a single database to hundreds of terabytes, and enable tens of thousands of users to gain real-time insights at petabyte scale.

Learn how the NBA uses data and AI to transform billions of data points into insights that enhance the fan experience.

See how Walgreens achieved 3x analytics performance at 1/3 the cost with Azure.

Limitless performance

Build cloud-native apps with real-time personalization and ultra-low latency. Enjoy higher performance analytics at a lower cost compared with competitors.1

See how P&G uses data and analytics to improve the resilience of its supply chain.

Explore how Coca-Cola uses Azure Cosmos DB to turn petabytes of disparate data into critical insights.

Limitless possibilities

Improve customer experiences, transform products, optimize operations, and enable employees of all skill levels to apply AI to their data responsibly with Azure data and AI services.

Learn how Land O’Lakes is using Azure AI solutions to pioneer agricultural innovations.


See how BNY Mellon uses Azure data services to help their clients make better investment decisions.

Get more value from your data at a lower cost


Azure Analytics up to 14-times faster decisions than Google BigQuery1


Azure Analytics up to 94% less expensive than Google BigQuery1

Explore Azure data services

Azure managed databases

Build cloud-native applications or modernise existing applications with fully managed, flexible databases.

Cloud-scale analytics

Build transformative and secure analytics solutions and turn your data into timely insights at enterprise scale.

Azure AI

Build mission-critical solutions with proven, secure and responsible AI capabilities.

1Performance, TCO, and price-performance claims based on data from a study commissioned by Microsoft and conducted by GigaOm in March 2021 for the Cloud Analytics Platform Total Cost of Ownership report. Analytics in Azure costs up to 59 percent less than other cloud providers according to the Cloud Analytics Platform Total Cost of Ownership report. Data is taken from Test-DS derived queries, and is based on query execution performance testing of 103 queries per vendor, conducted by GigaOm in March 2021; testing commissioned by Microsoft. The primary metric used was the aggregate total of the best execution times for each query. Three power runs were completed. Each of the 103 queries (99 plus part 2 for 4 queries) was executed three times in order (1, 2, 3, … 98, 99) against each vendor cloud platform, and the overall fastest of the three times was used as the performance metric. These best times were then added together to obtain the total aggregate execution time for the entire workload. Prices are based on publicly available US pricing as of March 2021. Actual performance and prices may vary. Learn more about the GigaOm TCO study.

Price-performance claims based on data from a study commissioned by Microsoft and conducted by GigaOm in August 2019. The study compared price performance between a single 80 vCore Gen 5 Azure SQL Database on the business-critical service tier and the db.r4.16x large offering for Amazon Web Services Relational Database Service (AWS RDS) on SQL Server. Benchmark data is taken from a GigaOm Analytic Field Test derived from a recognised industry standard, TPC Benchmark™ E (TPC-E) and is based on a mixture of read-only and update intensive transactions that simulate activities found in complex OLTP application environments. Price-performance is calculated by GigaOm as the cost of running the cloud platform continuously for three years divided by transactions per second throughput. Prices are based on publicly available US pricing in East US for Azure SQL Database and US East (Ohio) for AWS RDS as of August 2019. Price-performance results are based upon the configurations detailed in the GigaOm Analytic Field Test. Actual results and prices may vary based on configuration and region.

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