Last month, Dataiku had announced a $400 million investment increasing their valuation to $4.6 billion. Sid Bhatia, regional vice president, Middle East & Turkey, Dataiku, talks to Channel Post MEA on their recent investments and market outlook.
Elaborate on Dataiku’s recent funding.
In early August we announced $400M in Series E investment led by Tiger Global, with participation from several existing investors, including ICONIQ Growth, CapitalG, FirstMark Capital, Battery Ventures, Snowflake Ventures, and Dawn Capital, as well as new investors, including Insight Partners, Eurazeo, Lightrock and Datadog CEO Olivier Pomel. This capital, which brings the company’s valuation to $4.6B, will power Dataiku’s mission to systemize the use of data for exceptional business results.
We are on a mission to enable Middle East organizations to use data by removing friction surrounding data access, cleaning, modeling and deployment. This funding is further validation of our position as one of the world’s leading AI and machine learning platforms, and will allow us to continue supporting agility in organizations’ data efforts via collaborative, elastic, and responsible AI, all at enterprise scale
In addition to focusing on empowering regional organizations — particularly in the Financial Services, Healthcare, Manufacturing, Telcos & Public Sector — to have success with their Machine Learning and Artificial Intelligence initiatives, Dataiku will also continue to invest in strong partnerships in the region to ensure coverage & to capture market share. Another major priority for the company will be on building out the regional team to better support local customers.
What are some of the top-notch solutions provided by Dataiku to enterprise customers?
Dataiku provides one simple UI for data wrangling, mining, visualization, machine learning, and deployment based on a collaborative and team-based user interface, accessible to anyone on a data team — from data scientist to beginner analyst — and therefore appeals to all organizations across a myriad of industries. Dataiku allows enterprises to create value with their data in a human-centered way while breaking down silos and encouraging collaboration. One of the most unique characteristics of our product, Data Science Studio (DSS), is the breadth of its scope and the fact that it caters both to technical and non-technical users. Through DSS, we aim democratize data science and empower people through data.
Dataiku believes that any company can win in their market through enterprise AI, kindly explain.
At its core, Dataiku believes that to stay relevant in today’s changing world, companies need to harness Enterprise AI as a widespread organizational asset instead of siloing it into a specific team or role.
To make this vision of Enterprise AI a reality, Dataiku is the only platform on the market that provides one simple UI for the entire data pipeline, from data preparation and exploration to machine learning model building, deployment, monitoring, and everything in between.
Dataiku was built from the ground up to support usability in every step of the data pipeline and across all profiles — from data scientist and cloud architect to an analyst. Point-and-click features allow those on the business side and other non-coders to explore data and apply AutoML in a visual interface. At the same time, robust coding features — including interactive Python, R, and SQL notebooks, the ability to create reusable components and environments, and much more — make data scientists and other coders first-class citizens as well.
As each company’s path to Enterprise AI looks different, Dataiku supports the creation of a spectrum of applications, whether that means building out a self-serve analytics platform or fully operationalized AI integrated with business processes.
What are some of the key challenges facing enterprises when it comes to deploying AI/ML platforms?
The challenges for teams that don’t have a collaborative data science platform are:
- Access to systems: Whether accessing the various data sources or the computational capabilities, doing so in a remote setting can be challenging.
- Collaboration within teams: Without physical in-office proximity, individuals can become siloed in the execution of their data projects.
- Collaboration across teams: Data projects require buy-in and validation from business teams and also require data engineering and other teams to help with operationalization.
- Reuse over time: Capitalising on past projects is key to maintaining productivity and reducing duplicate work. The lack of in-person discussions can limit this ability.
More & more organizations see the need to provide a centralized, controlled, and elastic environment to support the exponential growth in the amount of data, the number of AI projects, and the number of people contributing to such projects.
What advice would you give organizations that are looking to invest in data science platforms?
In 2021, one of the biggest questions is: how could organizations even think right now about investing in AI? The reality is that lots of businesses get easily disrupted by big change (recent example case-in-point), and arming the business to deal with these kinds of changes and to face the challenges ahead via Enterprise AI makes sense.
AI is no longer a nice-to-have or something that mad scientists experiment within isolated teams; it’s a fundamental organizational asset that’s necessary to reboot business.
I have the following 3 recommendations for prospective customers when they’re looking for their data platform:
Democratization of Data Science across the organization is a must
Today, the democratization of data science across the enterprise and tools that put data into the hands of the many and not just the elite few (like data scientists or even analysts) means that companies are using more data in more ways than ever before. Data democratization is the path forward to eventually enabling AI services.
Collaboration is the key to Scaling AI
Collaboration is about making AI more widespread and relevant through access to a wider population within the enterprise. Part of the reason that collaboration is used a lot (but also why there is some lack of specificity around its exact definition) is that it has two distinct parts:
- Horizontal collaboration refers to people working together with others who have roughly the same skills, toolsets, training, and day-to-day responsibilities.
- Vertical collaboration refers to people from across teams working together who might have vastly different responsibilities, viewpoints, and who ― importantly ― use very different tools, usually.
Responsible use of AI through a Platform Approach
The use of data across roles and industries has become (and will continue to become) increasingly restricted. But that doesn’t have to mean a pause or paralyzation in data use; it simply means tighter processes around the use of data in the enterprise and a sense of responsibility down to the individual.