Big Data Solutions: Tips For Selection And Implementation

By Sven Junkergård, Chief Technology Officer, Zephyr Health

The health care marketplace is growing at an ever-increasing pace. It’s projected that total global spending on medicines will increase by 30 percent and top $1.3T by 2018¹ and that more than 400 Life Sciences products will be launched over the next three years².

Despite massive spending and a growing range of treatment options, the industry still will experience missed opportunities. Of the 400 new products launched, it’s expected that only half will meet their sales forecasts.³

There is a better way. In many cases, the cause of these missed projections and failed projects will be due to not having the right information that would optimize the process of getting the therapy, diagnostic or medical device into the hands of the right healthcare provider to help the patient in need.

Over the past few decades, there has been an explosion in the amount of data available. So much so that accessing the right information has become a bigger issue than its availability. As the number of data sources increases, data strategy necessarily becomes more complicated. That is where the right technology can hone processes for managing key sources to ensure a focused view of the market for informed outreach and decision-making.

We are at an exciting point of transformation as the Life Sciences industry finally begins to implement Big Data solutions to find these linkages that facilitate getting the right therapy, diagnostic or medical device into the hands of the right healthcare provider to ultimately help the patient.

These solutions are powerful. The data aggregation and linkage they offer provide a much deeper and more accurate picture of the market than ever before. This, in turn, elevates business decision-making to new levels, both from a strategic vantage point and from the day-to-day decisions of field representatives calling on healthcare professionals. However, these systems are large and require substantial financial and human investment.

With that in mind, here are a few thoughts to consider if your company is considering implementing a Big Data solution.

1. Start with the goal in mind. Data solutions require significant financial and time investment. Take time to understand what your objectives are for a data management solution so that you are able to measure your return and make the investment worthwhile.
2. Make sure the right people are on board. As with any major investment, you can expect that it is important to get buy-in from senior management. However, buy-in from frontline users is equally important. It may be surprising to learn that employees are tied into old ways of doing things, and it’s important to understand why, for example, they believe that manually integrating data is critical. It’s also important to build a case for change and to show employees how the time invested in learning a new way of doing things will make them more effective at their work. Companies who gain the most from integrated data management systems understand the crucial role of frontline team members in maintaining internal databases with up-to-date and accurate details.
3. Additionally, it’s critical to have conversations with legal, regulatory and compliance before investing in a system to be sure that the system can be used in the way all of the stakeholders are envisioning. The industry landscape is evolving rapidly with new regulations and added limitations to market access.
4. Don’t throw out the baby with the bath water. It’s easy to get excited about what a data management system can do and the variety of features that can help employees do their jobs better. It’s less exciting but just as critical for the system to work seamlessly with other programs and applications that will be used on an ongoing basis and to be able to incorporate the intellectual capital existing in current and legacy systems.
5. The internal integration process may involve aligning data across departments that are collecting different data points via different products or systems. With the right tools and processes, monitoring multiple data streams can be entirely manageable, and the benefits derived in merging multiple data sources can offer incredible insights.
6. Leave room to grow. When you are choosing a system, make sure that it has flexibility, both in terms of scale and in the ability to integrate new sources or systems that might not make sense for your company now but could make sense in the future. The more you can utilize the data management system flexibly, the greater your return on investment and the easier it will be to maintain employee effectiveness and support.
7. Meet people where they are. In this era of mobility, the ability to access information from a smartphone can make your data solution an inseparable part of your field users’ toolbox, equipping them to have better, more productive conversations with better results.

Taking time with both internal and external audiences prior to purchase is critical. By extensively interviewing internal audiences, you will understand their information needs and workflows, and by spending time with potential vendors and customers, you will learn how they can support you both in the implementation/transition phase as well as in the long-term.

Although it may seem early, it’s also wise to think about implementation prior to purchasing the system. Here are a few thoughts on implementation to consider:

1. Factor training costs into your budget. Data intelligence systems are complex structures that take time to learn to use to their full capability. In order for users to maximize the power of the system, you can count needing to offer more extensive and ongoing training sessions. It’s helpful to have in-person sessions available and also to make information accessible online or in video format. Making the financial investment in a system only reaps benefit when users can access that information easily.
2. Take your time. Just as you are taking time to make a thoughtful purchase decision, it’s important to roll out features of the system in phases. Some features of the data intelligence system may require new skill sets, and you want people to feel comfortable as they build on those skills for additional features. The prioritization phases should tie back to your business goals and which data points are most critical in addressing the challenges your company is facing.
3. Use it, don’t lose it. Data intelligence systems are dynamic, two-way structures that output important data points and also collect important information. Using the information collected is critical to sharpening your business intelligence and continuing to hone the accuracy of your decision-making. Your system will be most powerful when it is learning from external and internal sources.

It’s exciting to be at this unique cross-section in the healthcare industry when we are realizing the power of data. In the end, it benefits everyone as we create a world where we can get the right therapy, diagnostic or medical device into the hands of the right healthcare provider to help the right patient.