Today, we’re excited to announce Databricks’ partnership with Accenture to provide high-value Databricks services and reusable components to enterprise clients globally. Specializing in data strategy and design, data platform modernization and AI, the Accenture data and artificial intelligence (AI) team leverages Databricks’ Unified Data Analytics Platform to streamline proven methodologies for large-scale machine learning deployments. Together, we enable enterprises to break down silos, create more agile and adaptive processes, and power data-driven decision making to solve business problems and identify new opportunities.
This global partnership builds upon the work Accenture and Databricks have already done together to create joint solution accelerators and client solutions that originate and deliver opportunities across industry verticals. Notably, Accenture recently won the Databricks’ Consulting and System Integrator Innovation Award at the Data + AI Summit 2020 for their transformative data science, data engineering, and analytical frameworks on Databricks. By combining nearly 20 solution accelerators and assets, strong investments in training and marketing, and a dedicated innovation center, Accenture and Databricks enable customers to realize a much faster time to deployment.
Joint offerings and Solution Accelerators
Organizations leverage AI and ML to identify new ways to improve business productivity and outcomes. In fact, businesses that can scale their AI efforts effectively are 3x more likely to see a return on investment. Accenture and Databricks have created nearly 20 assets and accelerators to help clients prepare large data sets, establish data pipelines, and move models from development to production in no time.
Industrialized machine learning
While ML is lauded for its ability to learn patterns of data, subsequently improving performance and outcomes based on experience, the barriers to scaling it are numerous and varied. The rapid evolution of skills and technologies required, and the potential incompatibility of traditional operating models and business processes–especially in IT–both pose hurdles in moving ML applications from the pilot stage into production. This is why Accenture and Databricks have partnered to help clients operationalize ML at scale. By combining Accenture’s industrialized machine learning best practices and technical components with Databricks, organizations see value delivered from their Databricks applications that is both accelerated and aligned with their organizations’ values for governance and responsibility.
Scalable artificial intelligence
The opportunities of AI are expanding and crucial to any competitive strategy. As businesses pursue AI at the enterprise-level, they must deal with greater volumes, velocity, and varieties of data. AIP+ is Accenture Applied Intelligence’s collection of modular, pre-integrated AI services and capabilities and makes it significantly easier to adopt AI throughout the business at a lower total cost of ownership. Without needing to replace current platforms, AIP+ helps you maximize the value of your existing infrastructure. Leveraging our open architecture, AIP+ incorporates Databricks for data ingestion and analysis across a range of use cases. Through the incorporation of Databricks, AIP+ boosts data capabilities by simplifying data transformation and automating many labor-intensive tasks.
Intelligent and automated data foundation
There’s no denying that real-time access to contextually relevant data is critical for making effective business decisions. Supported by the right data insights, these decisions improve business performance across the board—from driving growth to improving employee productivity to delivering better customer experiences. To help organizations derive value from their data in the digital era, traditional Master Data Management (MDM) is evolving towards a new model. The new “Digital Master” mode, powered by Databricks, allows for near real-time, analytical processes that are aligned around desired business outcomes. This expansion is facilitated by multi-dimensional data technologies that turn data lakes into bodies of contextual knowledge, harnessing machine learning to drive ground-breaking business outcomes.
Industry case studies
Accenture and Databricks have worked together on 100+ client engagements with top enterprise companies in financial services, energy and utilities, retail and consumer goods, and more. Here are some ways customers leverage our joint solution to drive significant business value:
Navy Federal Credit Union is the largest natural member credit union in the world , both in asset size and in membership. Hoping to deliver a superior experience and provide members with personalized insights to help them save for their financial future, they set forth on a data transformation journey. However, they came across technical challenges, including data wrangling, feature engineering, code silos, and productionalizing data science at scale.
Focusing on being member-centric and data-driven, Navy Federal Credit Union deployed AI and modern technology by partnering Accenture’s services and Industrialized Machine Learning accelerator with Databricks. As a result, Navy Federal Credit Union was able to reduce the amount of data science work occupied by data wrangling or feature engineering from 80% to 20% and was able to address their goal of determining when members were most likely to begin saving.
A large pharmaceutical retailer in the U.S. was struggling to engage with its 80-million-plus member base through offers made with its loyalty program. It needed a way to increase uplift, but apart from manual processes, there were no systems in place to build a reliable, uniform and reproducible ML pipeline to evaluate billions of combinations of offers on a continual basis.
Accenture developed and delivered a personalization engine with the Databricks platform to build, train, test, validate and deploy models at scale–across tens of millions of customers, billions of offers, and tens of thousands of products. They also deployed an automated ML model deployment process and modernized AI pipeline. The result was substantially reduced DevOps time and effort in deploying models, and the business was able to achieve an estimated 20% higher margin for pilot retail locations.
A top American insurance company was looking to improve claims processing and pricing analytics, but was having difficulty due to on-premises legacy architecture, data lake duplication issues, and lack of performance and agility with their current application stack. Concerned about their ability to stay competitive in personal and business insurance, they worked with Accenture on their data strategy and development, implementing Databricks as their data architecture.
The key result was the company’s ability to now process at scale, meaning high throughput and 6X performance improvement over their existing code. In addition, they realized millions of dollars in cost savings across productivity improvement, infrastructure savings, and business impact across claims and risk.
To learn more, check out Accenture’s and Databricks’ joint webinar on Industrialized ML for Governed, Responsible and Explainable AI. In this session, you’ll learn how Accenture’s industrialized machine learning best practices, combined with Databricks, enables organizations like Navy Federal Credit Union to efficiently develop an industrialized, end-to-end ML model. Through this model, organizations are able to minimize redundancies, improve standardization, and accelerate model deployment.
For additional questions, please contact Jim Gregg, Director, Strategic System Integrators, Databricks, at email@example.com.
- Operationalizing machine learning at scale with Databricks and Accenture blog
- Industrialized ML for Governed, Responsible and Explainable AI
- The Killer Feature Store: Orchestrating Spark ML Pipelines and MLflow for Production
- Accenture Adds Databricks to AIP-IQ to Scale Federal AI