Sid Bhatia, the Regional Vice President for the Middle East at Dataiku, speaks to Future Tech about their offerings, their presence in the region, their channel strategies for 2021, and more
Can you briefly touch on Dataiku’s growth in the region over the last couple of years?
Dataiku was founded on the principle that in order to succeed in the world’s rapidly evolving ecosystem, companies — no matter what their industry or size — must use data to continuously innovate. Dataiku continues to grow & expand with offices around the world, including New York, Paris, London, Munich, Sydney, and Singapore.
Dataiku opened up the Middle East Headquarters, based out of Dubai, UAE in 2020. I was appointed as the Regional Vice President for the Middle East region and in just one year, I have built a strong direct sales/technical team and also onboarded several key partners.
Dataiku’s customer-centric engagements powered by a comprehensive solution struck a chord with businesses around the Middle East, resulting in rapid growth since the launch of regional operations. Today, many large organizations, both in the private and public sector, use Dataiku on a daily basis to build AI solutions.
Tell us about some of the big wins you have had in the region.
In 2020, we saw tremendous growth in the Middle East region, increasing client references to 15+ large organizations, ranging from some of the largest airlines in the region to some of the top organizations in the FSI and manufacturing sectors as well as strategically important public sector entities that solve industry-wide problems like fraud, churn, supply chain optimization, predictive maintenance, and much more.
Globally, more than 300 customers across retail, healthcare, finance, transportation, public sector, manufacturing, and pharmaceuticals use Dataiku to underpin their essential business operations and ensure they stay relevant in a changing world. Dataiku is positioned as one of the world’s leading AI and machine learning platforms, supporting agility in organizations’ data efforts via collaborative, elastic, and responsible AI, all at enterprise scale.
What sort of key trends have you seen in the regional market and what are the key challenges being faced by enterprises in the region?
In 2020, the significant drift observed was a result of the global health crisis. As a result, the new year is bound to include organizations using MLOps to put more structure in place around drift monitoring so that models can be more agile and accurate. And organizations won’t stop there. Aside from using MLOps for the short-term to address model drift during events like the COVID crisis, teams will also likely look to implement MLOps practices for the long term in an effort to more effectively scale their machine learning efforts.
In 2021, the use of AI for sustained resilience will be underscored, particularly with regard to empowering every team and employee to work with data to improve their business output. These challenges we observed in 2020 will remain in 2021 for teams that don’t have a collaborative data science platform that includes:
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 the 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: Capitalizing on past projects is key to maintaining productivity and reducing duplicate work. The lack of in-person discussions can limit this ability.
In 2021, we believe we’ll see more organizations put this research and work into practice. There’s no longer a need to convince people that this is the way to go, as they’ve already gotten there. Now, it’s going to be a matter of bringing organizations the expertise to implement the ethical use of AI across their existing and future use cases. Embracing these AI trends will not only accelerate organizations’ post-COVID recovery but the adoption of enterprise-wide AI as well.
What sort of value proposition do you offer for enterprises in the region?
Dataiku is one of the world’s leading AI and machine learning platforms, supporting agility in organizations’ data efforts via collaborative, elastic, and responsible AI, all at enterprise scale. At its core, Dataiku believes that in order 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 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.
The commitment to openness and flexibility in Dataiku doesn’t stop there. Because 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.
No matter what the underlying changes in architecture or advancements in technology, Dataiku remains at the center — the cornerstone of data governance and responsible AI. Dataiku continuously integrates the most recent technologies in its stack in order to lower the barrier to integration for the companies themselves. These include computation, storage, programming languages, machine learning technologies, and more.
Dataiku’s centralized, controlled, and elastic environment fuels exponential growth in the amount of data, the number of AI projects, and the number of people contributing to such projects. The platform was built to scale as businesses strive to go from a handful of models in production to hundreds (or thousands). The bottom line is that Dataiku is built for every industry, every use case, and also for everyone.
Finally, I would like to add that Dataiku’s focus on project and customer success is the real difference. We are committed to ensuring that companies achieve positive business outcomes through their engagement and interaction with our product, and our customer success team does this by interacting regularly with and understanding each individual enterprise’s unique needs. We want to highlight the incredible work of Dataiku’s Customer Success team and show what role they play in the day-to-day data science efforts of our customers.
Can you elaborate on the key solutions, verticals, and markets that you will be focusing on in the region?
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 to democratize data science and empower people through data.
The key industries that we have seen great traction in the Middle East include FSI, Healthcare, Manufacturing, Telcos & Public Sector. As we grow in the region & onboard new customers, our core focus remains on making our clients extremely successful so that they get the maximum value out of their adoption of our solution.
We have always appealed to organizations that have an Enterprise AI strategy to get a competitive edge. Today, businesses are handling increasingly more (and increasingly more complex — think videos, images, sound, etc.) data, which will require more complex algorithms. More complex algorithms are able to learn hidden patterns from the data by themselves, which is why they are useful — they can deal with problems that a human brain could not understand. And that’s where Enterprise AI brings an edge.
Please tell us about your channel strategy for the region?
Channels and partnerships enable solution vendors to accelerate their growth and go-to-market strategy. We have seen an amazing response from partners across the Middle East, thanks to our partner-driven strategy and a very friendly and comprehensive partner program.
When considering potential partners, we evaluate what each company gets from the partnership, and how these fit within each other’s overall strategy, capabilities, resources, and offerings. In the region, we are working with partners ranging from large system integrators, global consulting companies, and technology vendors to boutique houses focused on solution & industry expertise.
We firmly believe that in order for the partnerships to remain effective and productive on a consistent basis, management on both sides must continuously invest in the partnership. This can be in the form of adding more resources to the partnership, developing more solutions together, pursuing new markets jointly, or rotating in top talent into the partnerships.