Overcoming the Challenges of Analytical Data Digitalisation

While digital transformation of many types of scientific data is a challenge, analytical data brings its own, particular complexities. Moving your data from paper printouts to a computer screen is not digitalisation. The data itself has not changed; it has simply been encoded in a digital format. Digitalisation is defined by data that can be leveraged by digital information technology to help transform R&D.

The main difficulties of analytical data digitalisation are: analytical data is diverse -multiple techniques are often used in combination to provide answers; it is disparate -each technique and instrument vendor has its own proprietary format; and it’s pervasive in R&D workflows - how to tackle them all?

Many organisations have invested in data standardisation initiatives to tackle the first two problems. Unfortunately, while standard data ontologies and a standard data output for all analytical hardware is the ideal scenario, such initiatives have yet to yield a complete and accepted standard. That some of these initiatives are decades-old is also a clear indicator that this is not a trivial task and a goal unlikely to be achieved in the near future. The ubiquity of analytical data adds to the challenge because it is required by different users for different purposes at various stages of research, development, and manufacturing.

For more than 25 years ACD/Labs has been helping R&D organisations store, manage, and digitalise analytical data. The Spectrus platform enables the normalisation of all major analytical data types and formats. ACD/Labs makes your data easier for researchers to find and extract answers from, and available for new insights. The company have consulted on and helped clients deploy successful digitalisation of analytical workflows. The right time is now, and  ACD/Labs will help you every step of the way.