Close this search box.
Close this search box.

January 4, 2023

Automate data transformation for healthcare and pharma

Automating data transformation draws actionable insights quicker to improve patient outcomes and drug development. Read to learn more.
automate data transformation

“Big data,” as a buzzword, offered industries the promise of a new, info-driven way to make decisions. With big data, decision-makers would be infallible. Or so we thought. As with all buzzwords, the buzz outpaced reality. The tools we needed to gather data – the internet of things, ubiquitous wifi, advanced mobile technology, and wearable devices – were ready for us before we were ready for them. Organizations were suddenly enabled to collect oceans of structured data, but it wasn’t organized. To harness the power of big data, companies needed to transform information into a helpful format and understand how to automate data transformation for maximum efficiency.

What is data transformation?

Data transformation simply means converting data from one format into another. The process can involve converting a raw data source into a cleaned, validated, and ready-to-use format. It’s crucial to correct data management processes like data integration, data migration, data warehousing, and data preparation.

  • Key steps in data transformation include:
  • Identifying data sources and types
  • Determining the structure of transformations that must occur
  • Defining how fields will be changed or aggregated
  • Extracting data from its original source
  • Transforming data and sending to the target destination

Automating data transformation reduces time to insight and helps handle the ever-increasing volume of data most businesses deal with. Other reasons to automate this process are:

  • Ensure records are up to date
  • Enable data scientists to focus on top priorities
  • Drive more accurate decision-making
  • Prepare data for artificial intelligence applications
  • Increase cost-effectiveness

There are various ways that life science companies can approach data transformation and render datasets more useful, but whatever the approach, each data transformation process should be performed according to an established process. A key part of any process is determining the order of operations based on performing the most time-consuming parts first.

What is data transformation automation?

The healthcare industry generates an enormous amount of data. Ironically, the sheer volume of data available for use in developing new treatments is a hindrance in and of itself; without the ability to extract insights from this ocean of information, it’s ultimately not all that useful to data scientists. Automating data transformation makes getting actionable information from medical datasets easier.

“Healthcare organizations and biopharmaceutical companies are caught in an inefficient process of spending more time cleaning datasets than extracting value from them.” – Vial

Why should life science companies automate data transformation?

Data plays an increasingly important role across the spectrum of healthcare, from drug discovery and development to direct patient care. Drug companies, in particular, use data to help them solve daunting challenges, like bringing more drugs to market safely and economically under tight regulatory scrutiny.

Pharmaceutical companies are dealing with the realities of a multi-channel world. Data sources are proliferating, which is good and bad news – the good part is the availability of rich information, and the bad part is the need to clean and analyze it. But this isn’t a matter of man-hours; rather, it’s a matter of having the right technology and people who know how to use it. “Pharma once feared automation,” reports Dataversity, “But now, companies are turning to technology for solutions…automation will not eliminate jobs. Instead, it will enable them.”

Pharma companies are coming around to the idea that technology will transform many functions in the industry in the years ahead. In a 2020 Deloitte survey, 52% of biomedical and pharmaceutical executives said that transforming functions using digital technologies will be a top priority through 2025. Reliance on manual and paper-based processes significantly slows processes from supply chain operations and manufacturing to research and development and, worse, adds notable compliance risks.

In other words, technology will be essential to helping humans understand large amounts of information and use them to develop better, more effective drugs for patients worldwide. And this is good news for life science companies, who can refocus resources on value-added work rather than repetitive tasks.

Tools to automate data transformation and create actionable insights

There are several technological solutions to automating data transformation, including business intelligence, data integration platforms, data visualization tools, and insights management platforms. Many of these tools help prepare data for the application of artificial intelligence – an increasingly important factor for most major pharma companies.

In the pharma space, business intelligence platforms are used in new drug development and trials to achieve breakthroughs before competitors. Data visualization tools can help pharma companies drive insights to conduct more efficient clinical trials or hybrid clinical trials, stay ahead of the competition, and collaborate more easily. And data integration platforms bring together real-world data from multiple sources to provide accurate datasets for analysis.

Insights management platforms apply AI to unstructured data from conversations to uncover directional insight. An insights management platform for life science organizations can collect observations and other data obtained during the drug or device development process and is also less technologically disruptive than business intelligence. Whereas implementing a BI platform may require overhauling multiple processes, pharma teams typically work with an insights management technology vendor to set goals, plan engagements, and formulate next steps.

In the year ahead, more pharma companies will prioritize insights management as a key part of business strategy. Read our blog series to learn how the industry uses insights management.

Matillion. Automating data transformation: 5 reasons to do it.
Vial. What is automated data transformation and how can it accelerate healthcare insights?
Vizlib. What can data visualization do for pharma and life sciences?


Related Posts:

common clinical trial enrollment challenges

Common clinical trial patient enrollment challenges

life science industry updates

Life science trend tracker #9

How to engage stakeholders in healthcare

More insights, direct to your inbox.