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

Get key insights on your organisation's data maturity

Answer these questions to get insights on your organisation's data maturity, as defined in the Rethinking the Enterprise white paper. See recommended next steps for your organisation 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 lifecycle 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 to 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 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

Analytics Lessons Learnt: How Four Companies Drove Business Agility with Analytics

See real-world examples of companies using data analysis to make responsive, informed, and timely decisions.

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

GigaOm - Forrester Total Economic Impact™ of Azure Machine Learning

Read about the real impact and benefits businesses are experiencing by building AI solutions using Azure Machine Learning.

Read the white paper

GigaOm - Delivering on the Vision of MLOp

Learn how to implement MLOps and address the impact of ML across the development cycle.

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.

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

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

Limitless performance

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

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

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

Limitless possibilities

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

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

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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


Analytics in Azure is up to 380% faster than other cloud providers1


Analytics in Azure is up to 59% less expensive than other cloud providers1


Azure Machine Learning is up to 64% less expensive than Google Vertex AI2

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.

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Get personalised help planning and implementing your Azure data services

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.

2Total Cost of Ownership, time-to-value and enterprise capability readiness claims based on data from a study commissioned by Microsoft and conducted by GigaOm in July 2021 for the Enterprise Readiness of Cloud MLOps report. Prices are based on publicly available US pricing as of July 2021. Actual performance and prices may vary. Learn more about the GigaOm study.

Gartner, Magic Quadrant for Cloud Database Management Systems, 23 November 2020, Donald Feinberg | Merv Adrian | Rick Greenwald | Adam Ronthal | Henry Cook

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organisation and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

GARTNER and Magic Quadrant are registered trademarks and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.

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