As the world of scientific R&D embraces new digital technologies, we are gaining ever more value from big data analytics. The volume of scientific data is now doubling every 2 years and companies are increasingly aware of the huge potential to leverage this data for machine learning and artificial intelligence (AI). Recent years have seen an explosion of investment in this space, and with the broadening scope of digital technologies, companies that don't invest risk falling far behind their competitors.

However, investment in digital technology may seem like a risky business. The success rate of these ambitious AI projects is low in scientific R&D—estimated at only 15 percent. So, how can you make sure that your company is one of the few successes and gain a competitive edge? The secret is a robust data management infrastructure. Here we explain how you can achieve this and highlight some common pitfalls to avoid.

The challenge of scientific data for AI

Though the scope of AI is continually increasing, its use on scientific data raises unique challenges. With most experimental data dispersed between different databases and stored in multiple formats, it can be difficult to consolidate this complex data into an organized structure upon which AI can be implemented. A major reason for the high failure rate is that organizations embark on over-ambitious AI projects without building the solid data management infrastructure that is critical for success.

Even with a robust data management infrastructure, you can still run into problems. Many AI tools can easily find patterns in simple image and text data, but scientific data is unique in its complexity. As such, successful implementation of AI with scientific data often requires a large and robust training set of high-quality scientific data, which many companies don't have. The success of any AI system depends on the data input, so developing advanced systems trained on high-quality data is essential.

Combined, these challenges mean that organizations often struggle to use digital technology to maximize the value of their scientific data, putting significant investment into AI programs that are destined to fail from the start. How can you overcome these pitfalls and get ahead of the curve in R&D?

Investing in the data management journey

The key to success with digital technologies is to build a solid foundation for the management of high-quality scientific data. Investment at this stage will enable you to move from a reliance on structured data for retrospective reporting and descriptive analytics, to a self-organizing data lake, where machine learning algorithms are used for cognitive analytics and automated decision-making.

By first optimizing your systems for data management—an investment that may seem like a step backward—the competitive benefits can be reaped for years to come. Having a robust infrastructure for data management allows you to develop a future-facing architecture. The goal is to build a system with the capability to support current digital technology, and also the flexibility and scalability to meet your future needs and incorporate new innovations. By investing in robust data management, you not only get maximum value from your data, but also improve the efficiency of your processes, ultimately saving your company time and money.

For a competitive edge, look to CAS

To progress toward data maturity, you'll need to review your current infrastructure and implement potentially fundamental changes. This is a major undertaking, and while the field of data management consultancy is well established, it can be difficult to find the depth of experience needed to solve challenges specific to scientific data.

With more than 110 years of experience in consolidating scientific data, CAS is uniquely positioned to provide informed and specialized data management support. Our unparalleled understanding of the pain points involved in the journey is complemented by deep in-house scientific expertise, so we can actively navigate the challenges of scientific data.

For example, at the content management stage, CAS can help define your data management strategy and implement the appropriate data models, augment your data with CAS or other external content and curate new training datasets for you AI applications. At every stage of the data management journey, CAS can help move your system toward a self-organizing data lake, so you can fully realize the benefits from digital technologies such as AI.

In the highly competitive scientific R&D market, the organizations that succeed will be those that move through the full journey to data maturity. Learn how CAS can help.