In the not-so-distant past, businesses heavily invested in self-service analytics and desktop applications for data preparation, striving to harness the power of their data and empower their teams. Fast forward to today, and the data analytics landscape has radically transformed thanks to the meteoric rise of cloud data warehouses and cloud computing. This paradigm shift has ushered in an era where modern, cloud-native analytics platforms are revolutionizing how companies approach data analysis and decision-making.
Does your team need an analytics upgrade? Making your case has never been easier. Here are seven compelling reasons to make the switch.
1. Prepare for Increased Scrutiny and Regulation With a Governance Layer
To access data and build analytics, many business departments took advantage of desktop applications that could be quickly installed on user laptops for data preparation and analysis without the knowledge of central IT teams. These shadow IT projects created risks for the organization as users accessed data and created reports and applications without proper oversight and governance.
One Dataiku customer, a large health insurance company, said, “One of the most important success factors delivering value to the business is transparency through the entire pipeline, from data ingestion to modeling and the interface with front-end systems.” With robust governance and oversight in Dataiku, IT executives (for whom reducing shadow IT to better manage IT landscapes over time) can ensure that analysts and other users can operate in a governed environment for their analytics (and eventually AI).
2. Easily Access the Cloud Data You Need
Flat files and spreadsheets used to be the only way that business teams could access data, and legacy analytics and desktop apps were designed for these limited data types. 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. Instead of letting the multiplicity of sources be a blocker, let it be a competitive advantage for driving data-backed insights and predictions.
3. Leave Inefficient Resource Consumption in the Past
As data has grown and companies have moved their infrastructure to more cost-effective and scalable cloud environments, many teams find that legacy tools cannot utilize these new and powerful resources. This is particularly painful when companies have budgets for cloud computing, and business teams cannot benefit from them.
A large health insurance company invested in Google Cloud Platform (GCP) but found that their legacy desktop data prep tool was slow to access cloud data and computing, making it more difficult for users. They replaced the desktop tool with a modern data preparation and analytics environment from Dataiku and users can now easily access data stored in GCP and seamlessly run analytics workloads on the cloud.
Dataiku’s end-to-end platform and cloud agnosticism make it easy for organizations to use the cloud providers and their available services while simultaneously allowing users of all levels to quickly go from data exploration and preparation to fully built out analytics & AI applications, without siloing that work to exclusively technical experts.
4. Accelerate Project Time to Production
Desktop tools can seriously limit the production and automation options for data projects, making data and analytics projects more difficult to operationalize and maintain. For example, when data processes run on end-user laptops, a process can break down, and nobody else can fix it. Projects built by multiple people can also be challenging to bring together because the data, resources, and environments used to create the project differ from those in production.
At a leading financial services company, the deployment team has decreased the hours needed to put projects into production by 8x using Dataiku. Another financial services company noted that they have improved the quality of their data product outputs, so their business users have higher confidence in their decisions.
5. Move Beyond Descriptive Analytics, Under the Same Roof
Legacy reporting platforms allowed business teams to access data and build descriptive analytics reports, enabling them to understand historical trends. However, this did not allow teams to predict or forecast future values. With the rise of automated machine learning (AutoML) techniques, predictive analytics was suddenly within reach of many more people in the organization. Unfortunately, for many teams and users, this meant learning new tools and integrating data preparation and datasets between tools, creating brittle projects that took more work to maintain.
A Dataiku customer in the financial services space noted that it was more than 10x faster to onboard and upskill a data analyst on Dataiku, enabling them to work autonomously on advanced analytics projects in one day instead of a few weeks.
6. Keep Pace With New Expectations & Ways of Working
The global pandemic had a massive impact on the nature of work - gone are the days when teams could rely on in-person collaboration and work in desktop applications. Today, teams must connect from anywhere and seamlessly collaborate to deliver value. This is especially true for cross-functional teams, including business stakeholders, data teams, and IT. Having a way for everyone to collaborate in real time and share projects increases productivity, delivers projects faster, and drives more sophisticated “moonshot” projects that can create additional value.
One Dataiku customer - a leading pharmaceutical company - relies on cross-functional collaboration in Dataiku for complex projects. Another customer noted that once they started working in Dataiku, they easily collaborated with a team they had never worked with before.
7. Capitalize on Generative AI Momentum
Over the past year, Generative AI (particularly large language models) has breathed new life into the analytics and AI space. Although 45% of senior AI professionals are already experimenting with the technology, many executives are still figuring out how to tow the line of turning applications of Generative AI use cases into reality (fast) while safeguarding against risk.
Legacy analytics platforms are not built to integrate and operate with AI services and models, leaving business and IT teams on their own to build these projects from scratch in code-only environments. In Dataiku, they can use their preferred analytics platform and build these services into their projects with pre-built connectors - making AI services a fully integrated part of the experience. AI services like those from the cloud providers (i.e., computer vision) and Generative AI providers are part of the modern ecosystem and more than just advanced users want to (and should be able to!) take advantage of them for a variety of projects.
It’s Time to Transition to a Modern Analytics Platform
So, will you upgrade from legacy platforms to modern, cloud-native analytics? If you weren’t convinced already, let me give you a speed round of even MORE reasons to jump: easier admin, lower maintenance costs, reduced operational, legal, and regulatory risk (that's three in one), happier teams, more sophisticated projects, better business decisions, and more satisfied customers because your business is running like a well-oiled machine.
Dataiku is a cloud-native modern analytics platform that gives teams a single, shared environment for the entire lifecycle of data, analytics, and AI projects. 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. To learn more, be sure to come back for part two of this blog to in depth on how Dataiku solves each of these challenges in practice.
Dan Darnell is Senior Director of Product Marketing at Dataiku.
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