If you’re in charge of your organization’s R&D IT efforts and expecting different results from the same initiatives, it’s time to reassess your Bio-IT roadmap.

Start here with this list of best practices curated from almost 30 years of experience exclusively focused on scientific computing within the Life Sciences:

• Don’t make good decisions with bad data. As Bio-IT professionals, we’re often asked to support a wide range of data-related projects. After all, data is currency in the life sciences—the more you have, the more you can do. But even petabytes of data don’t do you any good if you can’t retrieve or make sense of it. While delivering big on a high-value project (analytics is the Holy Grail) may be on your team’s radar, good intentions alone won’t get you results; your plan and your processes are critical. Which leads me to my next point.
• Start from where you are, rather than where you want to be.  Would you ever consider building a house on a foundation you weren’t sure to be solid?  Probably not. However, in the high-stakes and often-demanding environment of R&D IT, the tendency to move toward solutioning before fully exposing and diagnosing the full extent of the issue or opportunity is all too common.  However, this approach is not only backward, it’s also costly. Only when you know where it is that you’re starting from, can you accurately identify the right strategy or tools to get you where you want to go.
• Listen (a lot).  This one may seem like a no brainer, but human nature primes us to ready a response while we should actually still be listening. In the field of scientific computing, listening should take place as part of a constructive engagement between I.T. and the business with the goal to understand each side’s particular challenges and needs.
• Clarify risk. In a highly regulated environment, the concept of mitigating risk is ubiquitous, and for good reason. When the integrity of data or processes drives outcomes that can actually mean life or death for living things, accuracy is not only everything it’s the only thing. But different projects, business units, and even companies have varying appetites for risk. Before assuming what will and won’t work because of the perceived risk involved, be sure to clearly understand where and what may be influencing a particular way of thinking.
• Let the shape of the solution fit the shape of the problem. Square peg, round hole—you know the outcome. Though variables may be consistent based on the parameters of your organization and what’s worked well in the past, viewing each challenge as unique affords you the opportunity (or luxury) to leverage best-of-breed design patterns and technologies to answer your needs. Resist the urge to jam a prefab solution into framework not equipped to support it and opt for a more strategic (and tailored) approach.
• Leverage small projects to build momentum for big initiatives.  Ever try to get your kids to clean their room? Psychology 101 has taught us that humans are programmed to respond more favorably when our big goals are broken into a series of smaller, more attainable steps. The same applies when affecting change within a company.  If you want buy-in to tackle a big initiative—like converting to the cloud, for example—start first on smaller elements of the effort. Agree on success then build upon that platform.
• Plan for the life of the solution. Once a solution is delivered—no matter what type it may be—it’s only the beginning. Solutions have to be maintained, oftentimes upgraded, and eventually retired.  Future-proof your systems when architecting them by thinking about your operations today AND years from today.
• Build agility into your team.  Often, the efficiency and flexibility of the team implementing your compute solutions are just as critical as the solutions themselves. Knowing how to structure your team and workflows—like when and why to introduce a DevOp or a DevSecOps model, for example—can help you bridge the gaps that often impede innovation.
• Always have a Plan B.  No IT or Bio-IT operation should move forward without a fall-back—or rather, a “back-out”— plan. Being able to pivot quickly and respond appropriately in the event of an unforeseen challenge (or opportunity if you’re an optimist) can mean the difference between the ability to try again, or not.
• Trust the process.  This is a phrase infinitely more famous for what it means to Philadelphia 76er fans, but unequivocally applicable in scientific computing.  Often, the strategy that leads to many effective scientific and technical computing initiatives within an R&D IT framework is different from a traditional enterprise IT model. And that’s ok because just as often the goals are different as well.  Leverage the expertise of R&D IT professionals highly specialized and experienced in this niche space, and trust the process.

Finally, I’ll leave you with this:  Never substitute experience.  The value in solutioning is a mix of skill, dedication, resources, time, and experience.  

Skill can be developed (often quickly) when needed

Dedication is generally a factor of personality but can be fostered

Resources are often fixed as a factor of the company

And with only 24 hours in each day, time is beyond our control; to be successful, you must use is wisely. 

Experience on the other hand, takes time to develop. It’s a product of resources and dedication applied to build skills.  It’s not simply knowing what solutions work or don’t, but rather understanding the types of solutions or solution paths that are optimal for a particular goal, because you’ve tried it before. It’s having the ability to project potential outcomes, in order to influence priorities and workflows. And ultimately, it’s knowing how to find the best design patterns.  

After nearly three decades of support for Bio-IT initiatives in the life sciences and healthcare,  I can say with certainty, experience is the single constant in a world otherwise fueled by innovation and change.