By Derek Zanutto, General Partner at CapitalG

Originally posted on February 5, 2020

Big data processing has become a necessity for survival

The digital universe will create 40 trillion gigabytes of data by the end of 2020. And as the volume of data grows, so does our desire to derive value from it. Despite some early successes, most companies are just getting started:

70% of CIOs report “digital transformation” as their top priority, spending north of $2 trillion against this goal.

The acceleration of data creation, paired with intelligent processing & analysis, perpetuates opportunities for leading enterprises to strengthen their competitive moats with data-driven decision-making. Unprecedented compute power, cheaper storage costs, and the rapid deployment of IoT devices will continue to drive a doubling of data volumes every 3 years, transforming big data processing from a beneficial practice to a necessity for survival.

A universal big data challenge: Talent Gaps

While the promise of data-driven insights is immense, the reality has fallen far short for many well-intentioned companies. This shortcoming is largely rooted in talent gaps. Data scientists are in high demand and short supply, a net deficit that gets worse every year. Most enterprises, even technology-forward companies, struggle to hire and retain the data science talent required to turn data into insights.

Over the years, a large number of point solutions have emerged as “band-aids” to augment the capabilities of small data science teams, costing enterprises an aggregate of $7 billion. While these tools help, they are not solving the problem; only 10–20% of potential value creation has been captured to date.

Unlocking value with “democratized” data science

In our conversations with executives running digital transformation initiatives, it’s clear the challenges they face are more people-focused than technology-based challenges. It’s less about having the right point solution to process data and more about having a platform that can empower people throughout the organization to sustainably succeed as data citizens.

In our view, to unlock the full potential of data, companies need solutions that act as “force multipliers”, empowering a broad base of employees to collaborate on data-driven decision-making.

That’s why we’re excited to partner with Dataiku, a leading end-to-end platform, making data science accessible to both data science specialists & functional business analysts alike.

Before Dataiku, implementing data science within an organization typically meant involving a select few specialists to build models on a small portion of the data being collected, using a host of point solutions or self-built tools to manage an unruly data structure. With Dataiku, teams across organizations can access previously siloed data, stored on-premise or in the cloud, and leverage the platform to do everything from ingesting and manipulating data to running full-scale data science models in production.

Customers utilize Dataiku across a wide range of industries and use cases, from entertainment companies dynamically measuring marketing campaign ROI, to utilities analyzing sensor data on core infrastructure. Customers consistently compliment Dataiku’s easy-to-use interface, its powerful featureset, and its collaborative workflow functionality, which enables individuals across functions and business units to work together to generate actionable insights. In addition, our conversations with Dataiku customers indicate one of the best customer satisfaction scores that we’ve seen in the enterprise space.

CapitalG’s investment in Dataiku

We at CapitalG, Alphabet’s growth equity investment fund, are excited to partner with Florian Douetteau and the rest of the Dataiku team. We look forward to bringing our resources to Dataiku and supporting the team as they continue to democratize data science for companies around the world, helping them on their path to Enterprise AI. You can read more about what Dataiku is building here

Special thanks to Pia Mishra, Associate at CapitalG and James Luo, VP at CapitalG for their collaboration on this article.