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In today’s digital landscape, data is the lifeblood of business operations. Whether companies rely on monolithic platforms or integrated solutions, success hinges on how quickly data moves through the organization. Yet, for many, the data science lifecycle still takes months — delaying critical decisions and stalling progress.
To compete in real-time markets, companies need more than faster data — they need clean, actionable insights delivered instantly. When business users can access reliable data within minutes, they can make smarter decisions, respond to trends, and drive growth.
Certainly, every company wants faster, cleaner data, but traditional processes can be complex and bog down the lifecycle. Accelerating data science happens when companies embrace automation, unified data, end-to-end visibility, and tap into real-time insight.
Slow and Siloed: The Traditional Data Science Lifecycle
For most companies, the typical data science lifecycle can take up to half a year or more to complete, as data flows through a complex, multi-step process. The data funnels through multiple departments and steps; a traditional process includes:
Data science creation — At first, PhDs on the data science team develop algorithms based on business needs. For instance, a fashion retailer may want to improve how their merchandising team optimizes product pricing, and the data scientists first build the algorithm.
Solution development — Next, coders translate algorithms into solutions. In the case of the fashion retailer, a solution that optimizes pricing.
User testing — Here, end users take the developed solution and test the models, dashboards, and results. The merchandising team at the retailer tests the pricing optimization tool, taking notes on the intuitiveness of the dashboard or how the results are aggregated.
Iteration — Lastly, if the model falls short during testing, the cycle starts again. Data scientists go back to retooling the model.
Depending on how many iterations a model goes through, a traditional data science lifecycle can go for a very long time. The back-and-forth between teams can take months. For the fashion retailer, that’s long enough that seasonal opportunities can be missed.
In healthcare, the stakes are even higher. Patient data must be processed, cleaned, and analyzed quickly to support timely care. Whether it's pricing, patient outcomes, or supply chain decisions, months-long data cycles no longer meet business needs.
How to Shorten the Data Lifecycle: Key Steps
To shrink the data lifecycle from all-important months to minutes, companies need to rethink their approach and focus on cleaning and automating data to attain real-time insights. Business leaders have been achieving these massive time savings by following these key steps:
Unifying data — Creating one single source of truth for data is paramount. To control the data lifecycle, companies first need to ensure all their data — inside and outside the company — funnels to one centralized location. This creates a single source of truth, where data scientists and business users ultimately work with the best data available.
Cleaning data with AI — As data gets funneled to a centralized location, AI can clean the data, properly formatting data and standardizing external data for seamless integration.
Automating the processing — Loading data into solutions automatically can process data faster and make it immediately available for action.
Governing the data — It’s not enough to let AI clean data. Companies must educate business users on best practices when it comes to handling data, especially unified data living inside a central location. For instance, business users should never remove data from the system and re-upload it. The data instantly becomes unclean, and clean data fuels AI. Poor data undermines it.
Implementing end-to-end visibility — Lastly, as data flows across business users and within different solutions, it’s important that all stakeholders, including C-suite executives, can see how the data flows through the company’s platform.
As the data lifecycle continues to update in real time at an automated and accelerated pace, the data needs to be monitored and seen by all stakeholders. This process greatly enhances how companies leverage data, develop solutions, and optimize their businesses. In the case of the fashion retailer, rather than wait for a pricing optimization solution to go through various iterations, the merchandising business user can access the visible pricing data and see immediate impacts on the business.
Core Questions Around the Data Science Lifecycle
Editor's note: Here are two important questions to ask about the data lifecycle:
How can companies accelerate the data science lifecycle?
Companies can accelerate it by unifying data into a single source of truth, using AI to clean and standardize data, automating workflows and implementing end-to-end visibility across teams and systems.
What are the biggest roadblocks to speeding up your lifecycle?
The biggest roadblocks include siloed data across departments, manual data cleaning processes, lack of automation and poor visibility into how data moves through the organization.