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Get more value from your data at a lower cost

14x

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

94%

Azure Analytics up to 94% less expensive than Google BigQuery1

86%

Azure SQL up to 86% less expensive than Amazon Relational Database Service for SQL workloads.2

Learn how leading companies are innovating with Azure data solutions

Walgreens achieved triple the analytics performance at a third of the cost with Azure.

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Nestlé prevents cybersecurity threats around the globe with Azure.

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The BBC uses Azure AI to improve engagement with their global audience.

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GE Aviation improves aircraft fuel efficiency and lowers costs with Azure.

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

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

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Get key insights for enabling successful data-driven transformation

Take the data maturity model assessment

Gauge how effective your data capabilities are at bringing actionable insights to everyone across your organisation. Answer these questions to find out what phase of data maturity your organisation has reached, and access recommended resources that are catered to your organisation’s needs.

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1. Do you have a data platform for aggregating and accessing data across functional and regional silos?
2. Does your team have a “data steward” or designated role which is responsible for ensuring the quality and usability of over 80 per cent of your team’s data?
3. Do you have a single, centralised system – at least at a departmental level – to grant permissions and access to data?
4. Is there a centralised source of documentation for existing interdepartmental APIs or other systematic methods for sharing data with other teams in your organisation?
5. Do you use APIs or other systematic methods to automatically share data externally?
6. When you add a new data source, do you have systems that automatically check that it meets your team’s data quality or format requirements?
7. Are you able to track the life cycle or lineage of data as it is transformed and used by models or reports?
8. Do you have a data catalogue which enables data scientists, BI analysts and others to discover and access data in a self-service way?
9. Is your data platform built on a microservices architecture that enables modular and repeatable use of components?
10. Are there rules or standards in place for systematically sharing data between teams (e.g., via API), so that teams can quickly and easily interpret other teams’ data?
11. Do you use performance reviews or other management tools to enforce compliance with data practices?
12. Are you able to process and run analytics on data in real time (as opposed to batch processing)?
13. Do you have a sandbox environment that enables you to test different features and models, and optimise their performance?
14. Does your organisation develop machine learning models?
15. Does your organisation use advanced machine learning techniques, such as deep learning or reinforcement learning?
16. Are you able to deploy machine learning models automatically without human intervention?
17. Do you enable future auditing of machine learning models by automatically archiving any artifacts that they generate?
18. Has your organisation defined ways to address fairness or bias concerns in machine learning models?
19. Do you use performance reviews or other management tools to enforce compliance with machine learning practices?
20. Have technologies developed by internal, independent and cross-functional teams been successfully rolled out across the organisation?

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.

Explore resources 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.

Explore resources 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.

Explore resources 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.

Explore resources for traditional organisations

Rethinking the enterprise

Learn more about the data maturity model and explore all the phases of digital transformation.

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The culture of data leaders

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

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1Azure Synapse (formerly Azure SQL Data Warehouse) outperforms Google BigQuery in all Test-H and Test-DS* benchmark queries from GigaOm. Azure Synapse consistently demonstrated better price performance compared with BigQuery, and costs up to 94 per cent less when measured against Azure Synapse clusters running Test-H* benchmark queries. GigaOm field tests also revealed that Azure Synapse (formerly Azure SQL Data Warehouse) outperforms Amazon Redshift in 86 per cent of all the Test-H* benchmark queries. Azure Synapse Analytics also consistently demonstrated better price performance compared with Redshift and costs up to 46 per cent less when measured against Azure Synapse Analytics clusters running Test-DS* benchmark queries.

*Performance and price-performance claims based on data from a study commissioned by Microsoft and conducted by GigaOm in January 2019 for the GigaOm Analytics Field Test-H benchmark report and March 2019 for the GigaOm Analytics Field Test -DS benchmark report. Analytics in Azure is up to 14 times faster and costs 94 per cent less, according to the GigaOm Analytics Field Test-H benchmark, and is up to 12 times faster and costs 73 per cent less, according to the GigaOm Analytics Field Test-DS benchmark. Benchmark data is derived from recognised industry standards, TPC Benchmark™ H (TPC-H) and TPC Benchmark™ DS (TPC-DS). The GigaOm Analytics Field Test results are based on query execution performance testing of 66 TPC-H-like queries for TPC-H and 309 TPC-DS-like, conducted by GigaOm in January 2019 and March 2019, respectively; testing commissioned by Microsoft. Price performance is calculated by GigaOm as the GigaOm Analytics Field Test-H/ GigaOm Analytics Field Test-DS metric of cost of ownership divided by composite query. Prices are based on publicly available US pricing as of January 2019 for the GigaOm Analytics Field Test-H queries and March 2019 for the GigaOm Analytics Field Test-DS queries. Actual performance and prices may vary. Both the GigaOm Analytics Field Test-H and Test-DS is derived from the TPC-H and TPC-DS benchmarks and as such is not comparable to published TPC-H or TPC-DS benchmarks results, as the GigaOm Analytics Field Test-H and GigaOm Analytics Field Test-DS does not fully comply with the TPC-H or TPC-DS benchmark.

See here for more information | Read the full GigaOm report.

2Price-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.

See here for more information | Read the full report.