Azzur Blog

6 Ways You Can Secure Executive Buy-In for Proactive Data Management



We've all seen the recent headlines about the nation's leading companies falling victim to data "malfunction". What not everyone realizes is that the key to avoiding the headlines was in front of their noses the entire time. Since the '90s, Good Documentation Practice (GDP) has, or should have, ruled the roost to ensure quality data governance measures. A major obstacle for implementing GDP is the fact that the people feeling the pain aren't necessarily the ones making the decisions. Gaining executive buy-in for GDP structure is the make or break for any enterprise hoping to climb the data maturity ladder.

While it's an expensive and arduous process to implement, harnessing buy-in from the c-suite for proactive data management is about more than securing a budget approval.

You'll get better results when you take initiative to fully understand and illustrate all circumstances surrounding the change. In doing so, you can convince decision-makers to back your plan to achieve a higher level of data governance - or at least consider next steps in the right direction.

But as anyone who's ever tried securing top-level buy-in for an ultra-specialized initiative can tell you, it isn't easy.

Sometimes you need some simple benchmarks to help you on your way.

So, here are six important tips for fast-tracking your path to executive buy-in to proactive data management.


 

#1 - Know the players.

Understand all of the key decision-makers, as well as those you know will be doing the legwork. Most often, the C-suite is the last to approve the initiative and the furthest away from those doing the actual work. To gain their buy-in, spend time with those who WILL be doing the work to understand how the process will fit into their daily workflow.

How to do this? If appropriate for your organization's culture and structure, schedule half-hour meetings with mid-level management of the most impacted departments, and work with them to identify THEIR pain points in the current data management environment  (or lack thereof). Then, work with them to realize how more mature data management would optimize their workflow.

Not only will you better understand the day-to-day implementation of the data maturity model, but you'll have others on your side who will be able to speak to the potential success of the program.
 

#2 - Know the landscape.

When presenting any shift in thinking or procedure to leadership - specifically in a specialized environment of data integrity and security - it's imperative to know the ins and out of the situation--including adversaries and competing initiatives.

Since you've already gotten to know the players, you've likely identified those who are on your side versus those in need of a bit more campaigning. Ideally, in outlining departments' pain points, you've also uncovered your adversaries and mapped out next steps for peer-level buy-in.

#3 - Know the results.

To prove that you're not wasting their time with the next ultra-specialized IT fad, identify a similar project success either from within your company or network, or a competitor, that reflects a similar starting point and highlights the positive end results.

For example, in 2017, after experiencing 10-fold growth, a leading U.S. biotechnology manufacturer faced the challenge of bringing the ITQ (Information Technology Quality) team up to speed with the stringent compliance and risk mitigation profile required of such a dynamic organization.

The solution was a quality and compliance audit and future state roadmap from the experts at Azzur IT Advisory Services (ITAS), which outlined insights and strategy for organizational change management, as well as delivery of the structure and processes for computer system validation, risk assessment, IT quality management, change control, and training.

#4 - Know the risks.

Just like knowing the results of success, it's crucial to understand the potential risks of NOT implementing the improved data management strategy. From dirty data to security breaches to patient well-being, reactive-only data management can leave your company open for disaster.

Whether it's showcasing an existing threat within your own enterprise, or that of a competitor, use concrete examples of how NOT enforcing proactive data management can be harmful to the executives teams' bottom line, or worse, patient outcomes.

Looking for a good place to start? 483's of course. FDA keeps public record of 483's issued each year. Here, you'll find a database of more than 5,000 warnings issued in 2017. 

#5 - Know the Numbers

While we know that executive buy-in to the data maturity model depends on more than just the bottom line, the numbers are very important. You must understand the dollars, people, and time necessary in order to achieve the desired future state, as well as any of the same figures for maintaining the future state once reached.

Your figures will vary depending on your enterprise; however, keep this in mind: According to IBM, each year bad data costs $3.1 trillion in the United States alone. That's one number that executives will listen to.

While a costly upfront investment - both in time and resources, reaching your ideal state of data maturity will ensure that your enterprise runs at a more efficient and cost-effective pace than your current state, while avoiding potentially catastrophic circumstances surrounding poor data governance (see 483's above).

#6 - Know how.

Last, but not least, you must know the ins and outs of how to reach the company's future state based on a strategic roadmap. Whether it's acting as project manager and proactively working across various departments such as IT and quality, or engaging a third-party consultant to advise on the optimal path forward, having a plan for implementation is crucial to taming the IT monster.

Have you been through this process before? Is there a tip I didn't mention? Connect with me on Linkedin and send me a message!
Kevin Martin
Author   

Kevin has nearly 40 years of FDA regulated industry experience that includes management positions at Wyeth and J&J/McNeil Pharmaceutical. Kevin's experience spans projects conducted within QA, IT/IM, Manufacturing / Operations, Clinical and R&D. Kevin is a former member of the PhRMA Computer Systems Validation Committee, a former chair of the ISPE DVC CSV Sub-Committee, a former Core Team member for the PDA Part 11 Task Group, and past Chair of GAMP Americas Steering Committee, past Co-Chair of GAMP Global, and former Sponsor to the GAMP Risk Management Special Interest Group. Kevin has a Bachelor Degree in Chemistry from Delaware Valley College of Science and Agriculture and a Master of Engineering in Manufacturing Systems from Penn State University.
 

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