Skip to main content
Data AI

"This initiative to modernize our data infrastructure, which includes a multi-year agreement with Databricks to unlock data at scale, will further enhance our analytical capabilities and deliver richer insights – driving better customer experiences and enabling colleagues to collaborate with more agility across the Bank." - Bharat Masrani, CEO of TD Bank 3/3/22

"We also recently launched Marketplace Workbench in partnership with Databricks, allowing clients access to a modern cloud-based platform for big data testing and analysis…Congratulations to all those involved in creating a site that uses unique technology to simplify our clients' ability to identify, access, evaluate and utilize unique data and solutions." - Doug Peterson, CEO of S&P Global 7/29/21

A decade ago, Capital and Scale were arguably the two most critical assets that allowed CEOs to compete. Capital, in simple terms, represents the ability of a company to not only fund day-to-day operations but to fund its future growth. Scale is what is afforded by the capital. It represents the tangible and intangible assets a company obtains through those investments (factories, stores, salespeople, etc). The interplay between these two assets helped to secure competitive advantage and to define a generation of successful companies.

Today, Capital and Scale alone don't cut it. Innovative and category-winning companies are built by adding Data and People to the equation. Data is the fuel that powers innovation: new products and services, better customer and partner relationships, and ultimately, higher share prices. People represent the employees, both technical and non-technical, that are enabled to fully harness the power of the data at a company's disposal. If Capital and Scale are the bedrock of companies, Data and People are modern materials and talent that CEOs need to take their companies to new heights.

The downside? Traditional data architectures weren't built to support these evolving needs and assets. Even in my own conversations with executives across the globe, it's become clear that even the most resource-rich companies can't obtain competitive advantage if they're relying on last decade's technology. Those that do find themselves drowning in unreliable data, disjointed "tacked on" architecture, and complex solutions that further silo data teams.

Choosing a technology platform that unifies Data and People – across all organizations – becomes one of the most important strategic decisions CEOs must make. At its core, the right technology must democratize data across the organization and empower people to make smarter decisions. This modern Data + AI platform must fully support the major "data megatrends" that are shaping the enterprise landscape:

  1. Data Explosion: In all aspects of the 5 Vs (volume, value, variety, velocity, and veracity), data is growing at an unprecedented pace. Scale isn't just a buzzword – your data stack must support massive amounts of unstructured and structured data and make it reliable and accessible to the right teams.
  2. AI is no longer "nice to have." AI capabilities must be seamlessly incorporated into products and services. This means data-driven insights that are predictive and not merely descriptive. For example, would you still take an Uber if your information was merely limited to where your driver currently is and if the app couldn't predict your fare or time of arrival? Productizing AI into your everyday products and services is the new bedrock of a successful business.
  3. Multi-cloud is here. Cloud is table stakes. Now, it's about developing a multi-cloud strategy that enables interoperability between clouds for both resilience and risk management. Multi-cloud gives companies negotiating leverage with cloud vendors as well as strategic optionality for acquisitive CEOs that must quickly execute technology integration after M&A.
  4. The future is open. Vendor lock-in and proprietary data formats slow down innovation. Let's face it – a single company cannot out-innovate a global community of innovators. Even the most regulated industries are realizing that open source is the best way to foster innovation, recruit and retain the best talent, and future-proof a technology platform.

These trends are just the surface of where the industry is headed. The uncomfortable reality is, any long-established incumbent can be out-competed if they're using the wrong technology platform. And we're already seeing this today– look at what Tesla did to the automobile industry.

Databricks is designed to support the data needs of today (and tomorrow), and do so cost-effectively and with high performance. Here's how the Databricks Lakehouse Platform addresses the challenges listed above:

Infinitely scalable and cost-effective.

The Databricks Lakehouse works off of data stored in cheap and scalable cloud data storage provided by the three major cloud vendors. This means Databricks can handle all types of data (structured, semi-structured and unstructured). It can also handle everything from AI to BI. In simple terms, Databricks can be your data lake as well as your data warehouse. In addition, the hyper-optimized Databricks engine brings massive computing power to your data, enabling faster computations that lead to cost savings over cloud data warehouses and over native tools provided by the cloud vendors. Databricks operates under a consumption model. In other words, your costs are tied to usage and value from the platform. If you're not getting value from Databricks, you're not paying us. For a CEO, scalability and cost-effectiveness can mean higher margins, ROEs, and transparency on the benefits of technology spend.

Get to AI faster.

Delivering AI at enterprise scale is hard. The Databricks Lakehouse makes that easier by bringing all your data together with all the personas that use data on one platform. This means Databricks is secure because you now have one governance model and one security model for your data science, data engineering and AI use-cases. The collaboration features and optimized software for managing machine learning life cycle within Databricks means you can get the most out of your data and people for all business use-cases. AI means going from using data to measure your business to using data to impact it. For a CEO, AI can mean higher NPS scores (happier customers) and higher growth.

Databricks is multi-cloud.

Databricks is not only available on Google, Azure and AWS but it is the first software company in history (to our knowledge) that has received investment from all three cloud vendors. This means a seamless experience across clouds but also that the models and Intellectual Property (IP) built by your teams within Databricks are portable across clouds. Data sharing capabilities inherent within Databricks means you can now share any data asset across any cloud or any tool or any system in an auditable and governed way. For a CEO, multi-cloud can mean superior business resilience, business continuity and negotiating power.

Databricks is Open.

The open-source technologies that underpin Databricks such as Delta Lake, MLflow and Apache Spark are downloaded more than 30 million times a month around the world. This means there is a rich ecosystem of innovation to leverage as well as a rich pool of talent that knows how to leverage the technology. Your data stays in your own cloud accounts in an open format. This means there is no vendor lock-in. On the other hand, if you go with a cloud data warehouse vendor, they will take your data and put it in their proprietary formats. For a CEO, open means attracting and retaining the best and brightest tech talent that want to work on the latest open-source tools.

Databricks bring together your Data and People on one secure, open, and build-for-cloud platform. The Lakehouse architecture vastly simplifies your data architectures and gives you the tools to win against the competition and also to attract and retain the best technology talent to your organization. We prevent vendor lock-in because we never put your data into proprietary vendor-specific formats. That's why over 6,000 customers including over 40% of the Fortune 500 rely on Databricks. Simply put, we make it easier for CEOs to make the right technology decisions to unleash the power of your data and your people and to set your organization to win the race to AI.

Learn more about Databricks Lakehouse for Financial Services at or read the recent Databricks Symposium highlights.

Try Databricks for free

Related posts

Company blog

How Digital Natives Can Transform Messy Data into Business Success

March 29, 2022 by Elly Juniper in Company Blog
Elly Juniper, Media & Entertainment and Digital Native Business Sales Leader for Databricks Australia and New Zealand, is in a front-row seat observing...
Data AI

Top Four Characteristics of Successful Data and AI-driven Companies

At Databricks, we have had the opportunity to help thousands of organizations modernize their data architectures to be cloud-first and extract value from...
Company blog

Accenture and Databricks Lakehouse Accelerate Digital Transformation

November 17, 2021 by Stephen Ryan and Matt Arellano in Company Blog
This is a collaborative post from Accenture and Databricks. We thank Matt Arellano, Managing Director, Global Data & AI Ecosystem Lead -- Accenture...
See all Data Strategy posts