Reproducibility in research is a challenge, and it's one your data analytics team needs to address to ensure consistency. An inability to reproduce pharma market insights in the UK can call into question the reliability of the results. Such doubts could crumble the foundation of your pharma data analytics insights used in decision-making.

However, there are ways to improve reproducibility. This post will outline the challenges, how they impact data analysts and data scientists, and solutions.

What Is Reproducibility, and Why Is It So Hard to Deliver?

Reproducibility is the practice of obtaining consistent results using the same data and structure as the original study. The definition itself infers the difficulty. You have to be careful in every phase of the analysis. The burden is heaviest for the data analyst and data scientist.

The Impact of Reproducibility on Data Analysts

Replication is often elusive, and a data analyst has the burden of recreating the format to bring success. Data analysts realise that an inability to reproduce results impacts the initial result's credibility and interpretation. Since they often create business intelligence reports, they need complete trust in their findings. That trust fractures when reproducibility isn't achievable.

While data analysts can't manage every part of the process, they can improve experimental design. They also have ownership over gathering external datasets and ensuring they are usable for multiple rounds of reproduction.

Why Is Reproducibility So Important to Data Scientists?

Data scientists are keenly aware of many elements outside their control in research. This group cannot state with 100% confidence that findings are reproducible. Much of that comes back to data collection and data relevance to the question. Their data engineer counterparts typically manage this. It's a collaborative effort that sometimes falls short.

By creating a standard way to reproduce findings, data scientists can spend less time duplicating efforts because repeatability checks the work. This allows for a greater focus on new questions to answer. There are always new queries to tackle regarding pharma insights — measuring performance on new drugs, looking at factors that cause boosts in prescribing, and more.

Repeatability vs Reproducibility: What's the Difference?

In looking at the landscape of research in pharma, reproducibility has a distinct process and objective. Many often group it with repeatability. Often people interchange these terms. However, they aren't the same. Reproducibility uses the same data. Replicability involves producing consistent results using new data or methods to answer the same questions. Both are important to a pharma data analytics team.

Reproducibility is critical because you want to be sure of what you find. After all, it impacts how you'll market and position your products. Repeatability enables you to add new data to the study to discern if additional factors change your findings. It's essential to differentiate these two and have procedures for both. While they both seek to answer the same question, they have separate uses for your data analytics team.

The Biggest Challenges with Reproducibility

The issues with reproducibility need resolutions so significantly the UK Reproducibility Network (UKRN) Steering Committee exists. It studies how to increase reproducibility to improve research quality. The body promotes partnerships, training, and effective research practices. In other words, they stand for making research accountable. They published a blueprint for reproducibility. In creating one for your pharma market analytics, you have to consider the biggest challenges.

While bias plays a role in reproducibility, it's not the only factor. And the impact of this lowers the efficiency outputs, making the time to insight much slower. Here's why you struggle with reproducibility.

• Lack of access to raw data and research materials: If you're going to reproduce an analysis properly, you'll require the same datasets. Depending on how you obtain, use, and process data, this can be a significant barrier.

• Using misidentified data: When you tap into different data streams outside of your business for UK pharma data, its hygiene and accuracy are critical. Raw, unstructured data can cause challenges here, especially for the data analyst. That's because their main goal is to explore the data, seeking answers to your questions.

• Inability to manage complex datasets: This problem is in the data scientist's bucket. They are responsible for sophisticated analysis to support better decision-making. Thus, they need a powerful platform to manage datasets. They also need to use an open data source already optimised and versioned.

• Poor research practices: This problem is foundational. In assembling your data analytics team, your success hinges on the parameters you define for research as part of your analytics strategy. This missing piece can cause reproducibility to fail.

Best Practices for Achieving Pharma Analytics Reproducibility

Your data analytics team can overcome challenges with the right solutions. In looking at the biggest challenges, much of this has to do with standardisation and data integrity. To improve upon the ability to reproduce, keep these things in mind. They all support the scientific method as well.

• Document and follow the shared process of how to explore data and ask the most valuable questions. This falls under the purview of data analysts.

• Employ a repeatable method for everything to eliminate manual data editing or insertions by leveraging technology that annotates, saves, and shares.

• Create workflows that are simple and easy to interpret into business intelligence.

• Include detailed descriptions of the data, its source, and how you're using it.

• Plan out statistical analysis before collecting data regarding sample size calculations. This will ensure you're not on a path to generating less reliable results.

• Establish a data management plan, which defines where you'll store the data after the initial project. You'll need easy access to it for future reproductions.

UK Pharma Market Analytics Solutions for Reproducibility

Outside of the best practices above, you'll need open and optimised prescribing data from NHS England. While there are several ways to access it, they aren't equal in what they provide. Our prescribing datasets enable you to uncover UK pharma market insights. The data helps identify prescription patterns and cost information. Explore the options and how these datasets can improve your reproducibility initiatives.

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Blog hero image by Hans Reniers on Unsplash.