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We’re excited to share the announcement of our partnership with Avanade to enable enterprise clients to scale their Azure Data and artificial intelligence (AI) investments and to generate positive business results. In addition to the hundreds of trained Microsoft Azure Databricks specialists, Avanade has a set of solution accelerators to help operationalize data engineering, data science and machine learning on top of Azure cloud solutions. The combination of expertise from both companies, especially on Azure, will make it easier for our joint customers to modernize and implement advanced analytics using Azure Databricks.

This partnership builds upon the work Avanade and Databricks have already done together for several years to deliver client solutions that embed AI throughout various business processes and experiences. We’ve seen that despite 88% of global decision makers investing in machine learning, only 8% of these companies are engaging in core practices that support AI adoption at scale. Most companies are applying AI to just a single part of their business or running ad-hoc data science pilot programs, missing opportunities to generate new sources of revenue and engage with customers. Avanade and Databricks are working together to help data teams address these gaps and move towards a competitive advantage in their industry.

Focus on Modernization and Scalability

Avanade and Databricks have worked together on a number of solutions and projects. Talking to Luke Pritchard, the Global Data Lead for Avanade, he said “This partnership builds upon the work we have already done together for several years to deliver solutions that help our clients scale their Azure Data and AI investments to generate business results.” Here are a few key areas of our partnership to highlight:

1. Modernization and cloud migration

Limitations with on-premise data systems, like Hadoop, are pushing data teams to explore new cloud-computing alternatives. However, planning and migrating business applications from one environment to another is no easy feat. It takes a lot of time and technical expertise to develop a proper migration plan, refactor the data architecture, and validate outcomes with the desired results. This is where Avanade and Databricks enable a smoother migration path from legacy data systems to modern data architectures.

2. Production ready machine learning

Every enterprise has opportunities to accelerate innovation by building data science and machine learning into their business. When it comes time to automate and govern the preparation of large datasets for analytics, and establish processes and automation for moving models from development to production, the extent of what is needed becomes clear. Streamline the full machine learning lifecycle with a repository of industry-specific ML models and pipeline templates to automate data preparation and promote reuse of data transformation scripts.

3. Data science at scale

The power of bringing data together across business units and systems gives organizations a competitive edge, but often takes months of infrastructure and DevOps work. It also requires multiple handoffs between data engineering and data science, which is error prone and increases risk. Develop an enterprise analytics strategy that is specific to your industry and organization, bridge the talent gap in deep advanced analytics, and ensure scalability and sustainability through built-in security and maintenance.

Industry Case Studies

Avanade and Databricks have helped customers across industries leverage open-source software, big data analytics, machine learning and AI to modernize their data platforms and engage customers. Here are a few examples:

Hadoop Migration for Global Pharma

A global pharmaceutical company wanted to operationalize and expand their data science capabilities when Avanade helped them move from their on-premise system to Azure. By building a data platform that leveraged Azure and Azure Databricks, the company’s data scientists were able to automate the majority of their data preparation, experiment with their models, and train algorithms faster. As a result, the reduction in repeat work and improvement of data science capabilities allowed the company to reduce costs and uncover new revenue opportunities.

Industrial Supply Chain Optimization

When thyssenkrupp, an industrial engineering and steel production company, wanted to optimize their delivery network to address rising supply and transportation costs, they immediately thought of AI in the cloud. Together with Avanade and Databricks, thyssenkrupp built the cloud-based platform alfred.simOne to automatically analyze and run simulations. The completed simulations led to optimized operations, increased cost savings, and reduced emissions. Internally, thyssenkrupp was better able to bring their data engineering and data science teams together for improved collaboration and development of innovative solutions that had a real impact on the way they do business.

Financial Services Customer Personalization

One financial services company repeatedly saw that customers were abandoning credit card applications. They chose to work with Avanade after realizing they needed to scale their data science initiatives to maximize insights and create a more personalized customer experience. Avanade helped implement a real-time data platform using Azure Databricks with a unified view into each customer across various time intervals. The solution made it easier for their marketing team to segment customers by type, serve them a relevant application, and ultimately reduce abandonment and churn.

Learn More

To learn more, please see the Databricks page on the Avanade website, or Contact Us.

Be sure to also check out the Avanade session at the Spark and AI Summit on June 25, 2020 at 11:00-11:30 am PT. In this session, you’ll learn how to scale the use of data science and artificial intelligence (AI) for accelerated business results. You will also gain insights into high impact use cases and learn why a design led approach helps you achieve a higher success rate to accelerate value enterprise wide.