Data Executives Raise the Bar for a Data-Driven Culture

Not every organization has a Chief Data Officer (CDO) specifically, but nearly every organization has a data leader (or a few) who champion data initiatives and work to increase AI maturity. A VP of Data, Chief Analytics Officer, CEO, or even Chief Marketing Officer can be a data leader if they drive data responsibility and usage at their organization.

Top Priorities for Data Leaders

Data leaders must work to improve access to the technologies and education that will equip their organization with the tools to succeed plus foster a collaborative culture where data is trusted and valued. Even the best models won’t succeed if no one uses them. On the other hand, even if everyone in an organization wants to use data, they can’t if it’s too cumbersome to access and process.

The top priorities  for data leaders to drive home data transformation are: 

• Align data collection and strategy with business goals – data projects, especially early ones, must be selected strategically, as they will demonstrate the ways that data can drive value for the whole organization. Facilitating communication between technical and analytic teams on their pain points and business stakeholders on their strategic goals is critical to success.
• Democratize data analysis – data can provide value to every team, from R&D to sales and marketing, so confining it to only technical personas is a huge missed opportunity. With the proper tooling and support, analysts and others can distribute the workload of data projects plus increase the organization’s overall data literacy and ability to leverage data to make day-to-day decisions.

• Establish trust in data-driven insights – understanding data value is the first step toward a data-driven culture, but organizational silos, lack of visibility into what’s driving machine learning models, or lack of understanding of data itself can quickly diminish trust. Introducing transparency and education, to name a few examples, can go a long way to getting company-wide buy-in.
• Foster an environment where data is driving real decisions – when teams have access to robust analytics on relevant, timely data, it will drive better decision making; it’s that simple. However, if analytics and business teams need to jump through hoops to access data, – or worse, the analytics they can access are based on stale data – decisions will come down to gut instinct every time.

• Instill a culture of constant iteration – even after the completion of a successful data project, teams must review the results to ensure that the accuracy of models doesn’t drift over time and that they are still providing the value expected. 

Dataiku Supports Data Leaders

Dataiku is the platform democratizing access to data and enabling enterprises to build their own path to AI. By making AI accessible to a wider population within the enterprise, facilitating and accelerating the design of machine learning models, and by providing a centralized, controlled, and governable environment, Dataiku allows businesses to massively scale AI efforts.

Dataiku enables:

• Data project management and monitoring, with the ability to track large-scale projects from data management, to prototyped pipelines, to operationalized results. From there, the entire project (or parts of it) can be quickly duplicated for future projects in just a few clicks.
• Cross-organization collaboration, with robust coding and code-free features that allow data leaders to capitalize on a larger array of talents & expertise to achieve enterprise-wide AI goals, quickly.
• Team growth, with extensive knowledge sharing, contribution, and data project sharing features to ease new talent onboarding and existing talent growth.