The day-to-day headaches that enterprise analytics and IT leaders are faced with are not for the faint of heart. We’re talking about frustrations that cloud resources are not being used more efficiently, concerns over the amount of ungoverned analytics work being done on desktops, isolated and disparate workflows (not to mention the shocking number of tools that these various workflows take place in), and — last but absolutely not least — the juxtaposition of seeing the value in Generative AI implementation, but struggling with actioning on and managing the tech at scale.
In our report with Cognizant based on a survey of 200 senior analytics and IT leaders, we highlight all of these challenges and provide a path to solving them. When we asked respondents about their main challenge with data infrastructure, 45% cited data quality and usability. It’s a tale as old as time and, especially in the age of Generative AI, carries more weight than ever.
Poor data quality can result in large language models (LLMs) learning from incorrect, biased, or incomplete information, which subsequently generates flawed or biased outputs. This can undermine the credibility and effectiveness of Generative AI applications, affecting their adoption and trust among users. So, we’ve established that organizations need to address data quality in a way that allows analytics and AI to flourish. But what are the other data infrastructure concerns keeping analytics and IT leaders up at night? Keep reading for never-before-seen insights that didn’t make it into our final report.
Concerns Persist With Security, Compute Scalability, and Data Access, Too
With regard to those who answered that security was their main data infrastructure challenge, the greatest security-specific challenge was ensuring no proprietary data (things like trade secrets, intellectual property, and customer information) gets outside of their company, cited by half (50%) of those respondents.
Implementing a data governance framework can help to oversee data initiatives, including roles, responsibilities, and processes for data management. Dataiku’s visual flow gives full traceability of data from source to final data product. Teams can build audit trails and data lineage throughout the entire lifecycle, ensuring data is compliant with internal controls and external regulations. Plus, with role-based access control, user groups can be granted multiple levels of access and fine-grained permissions operate at the user, connection, project, compute, and global levels.
Next, with regard to those who answered that compute scalability was their main data infrastructure challenge, nearly half (45%) of those respondents said their greatest barrier is that work is done in the cloud but is not configured in a scalable way.
With the rise of cloud data sources and warehouses, many teams can’t access data, connections are very slow, or they need serious SQL skills that they don’t have. With a modern, end-to-end platform like Dataiku, teams can seamlessly connect to every data source in one place, regardless of size, shape, or location.
Next, with regard to those who answered that data access was their main data infrastructure challenge, nearly three-quarters (69%) said that their biggest data access challenge is that data is siloed across disparate systems.
This is no surprise, as one of the primary obstacles to effective data access is the presence of data silos within organizations — especially amidst the “data deluge” of modern times. Departments often operate in isolation, leading to fragmented datasets scattered across various systems and platforms. This fragmentation impedes collaboration and decision-making, as accessing relevant data becomes a cumbersome and time-consuming process.
Dataiku makes data access more efficient by providing an infrastructure-agnostic, centralized platform with features to ease the burden on IT: a unified access point with pre-built connectors to streamline the connection process, enhanced security so IT can easily manage permissions, and streamlined workflows via the data catalog.
Putting It All Together
With the rise of Generative AI, the maturity of cloud data, and the constantly-increasing scale of data availability, the pace of change is greater than ever before.
However, despite the breakneck pace of change in the analytics and AI landscape, a few harsh realities persist:
- Teams still struggle to access the data they need, creating bottlenecks and frustration (for both the practitioners and IT!).
- People and projects are siloed, slowing work to a crawl. Teams are still using ungoverned spreadsheets on their desktops, copying and pasting data and formulas.
- The proliferation of tools and technology only pushes IT leaders farther and farther away from analytics modernization. Legacy applications aren’t future proof, don’t support distributed teams, and are limited in their ability to help teams apply new techniques like Generative AI.
- Lack of central visibility and governance over analytics projects opens analytics and IT leaders up to operational risk.
The time is now for organizations of all sizes — with analytics and IT leaders at the helm — to improve their performance and agility, reduce risk, and gain more insights from their data. With Dataiku, teams work in a safe and governed environment and leverage investments in the latest cloud data, cloud computing, and Generative AI technology to drive more projects and value.