Nearly every early-stage life sciences organization recognizes the necessity of getting their technology and informatics landscape right. If done well, this can help you accelerate growth and enhance progress through the development lifecycle.
Yet too many early-stage companies struggle with the perceived number of options when building their technology and data infrastructure. This often results in analysis paralysis, leaving leaders cycling through multiple tools and architectural designs and reworking solutions for little measurable benefit, all while spending precious time and resources.
Get started with best practices in mind
Executives are often paralyzed by the perception of choice when in reality there’s only one fundamental decision to make: Will you use AWS or Microsoft? Based on that choice, it’s best practice to use 80% of those solutions as is (out of the box). Only the remaining 20% should be considered for custom configuration. Following this rule-of-thumb removes the false sense of choice and desire to customize a “perfect” solution, allowing you to get to work faster.
Other basic principles to keep in mind if you’re early in your data and informatics journey:
- Don’t forget the fundamentals, like data governance. Companies are 2.5X more likely to be successful when they have a data governance strategy.
- Avoid moving too quickly on data management, which often leads to technical debt and rework.
- Don’t rush the important step of preparing your data. 60% of data scientists spend their time looking for and cleaning data versus analyzing it.
Using Design Patterns for Life Sciences
Data and software engineers use design patterns to describe repeatable ways to solve recurring problems and use them to guide solutions. This helps life sciences organizations understand where that 20% customization potential exists.
Here are a few things we advise customers to do first:
1
Identify design patterns with infrastructure and SaaS solutions that use the FAIR framework to choose from. Using these in combination removes a lot of work, especially with JSON native repositories.
2
Use a best-practices framework and design pattern to advance your analytics. This will remove the headache of “fixing” your data before hiring talent and focusing on developing your analytics and data insights strategy.
3
Adhere to a design pattern early. This results in cleaner data earlier so it’ll be easier to add business intelligence tools later. Every company must clean data before implementing advanced analytics, machine learning, and AI. Start building the muscle internally today before analytics begin.
Using standard design patterns and practices, you can eliminate most of the technical debt that leads to unnecessary added costs. The tradeoff is investing in seemingly more expensive technologies upfront. But the benefits far outweigh the perceived drawbacks – helping you iron out issues, get everyone on the same page, and grow the right solutions from the start. The alternative is switching approaches later as you get closer to IND filing or first-in-human trials (FIHT).
Spending more time upfront prevents the inefficiencies that result from piecemeal IT implementation. Budgets may be tight now, but asking for more money down the road due to technology and human resource inefficiency impacts your ability to get to market quickly.
Standard Best Practice Architecture
We have detailed blueprints available to help customers focus their efforts and maximize the out of-the-box functionality of best-in-class platforms.
And here’s a simplified blueprint that helps narrow down options for time and energy-intensive customizations.
80% Accepting your Fate, 20% Innovating
Regardless of the perceived menu of choices, we most often see startup life sciences organizations choose between two major paths, AWS or Microsoft.
We recommend choosing your fork in the road, then getting started with the basic blueprint for the path you choose. The result will be 80% identical to your peers with the same platform – and for good reason – the platforms are thoughtfully designed to support the fundamentals. The other 20% can be customized based on your unique business needs. Focusing your time and teams on the 20% that will help you achieve better ROI and faster implementation.
Avoid the Paradox of Choice
Don’t fall into the paradox of choice! Remember:
- There are really only two strong vendors to choose between.
- Regardless of the vendor you choose, 80% of your IT needs can be met with out-of-the-box features.
- After you’ve committed to a platform, then it’s time to think about how your unique business needs can drive high-priority customization efforts.
Creating and implementing your data and informatics strategy should be as simple as possible. Our team has helped numerous early-stage life science organizations conquer the paradox of choice. Contact our life sciences and analytics experts if you’re considering a data and informatics strategy.
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