Data modeling: A fundamental pillar of your future AI technology
Just as a high-quality data foundation is important for success in the realm of digital technologies, so too is the underlying structure that connects your organization's data assets. For artificial intelligence, machine learning and other digital business applications to transform your R&D organization, starting with high-quality data and an effective data model is essential.
How your data is organized and stored, and the data relationships within, is defined by a data model. An effective model allows users across your organization to easily understand how the business operates. It is the linchpin of almost every high-value business solution, with its greatest value realized when applied beyond the boundaries of individual lines of businesses (LOBs) or operations within an organization. This data model is a strategic pillar for information management, upon which the success of future business-critical projects depend.
However, modern data has become so intricate and diverse that the task of creating a data model is now rarely straightforward. For R&D companies, this can be particularly tricky as scientific data is uniquely complex and often disconnected. Furthermore, as the amount of data produced increases exponentially, creating an effective data model as the foundation for successful implementation of new digital technologies has never been more important.
In our latest whitepaper, we explore the issues associated with this rapidly changing digital landscape and examine how to prepare your data foundation accordingly. This blog post illustrates the benefits of data modeling, outlines common pitfalls and gives an R&D example of what an effective data model can look like.
How your R&D organization can benefit from data modeling
A data model will give you a standard mechanism for defining and analyzing data within your organization. But it can go further. In fact, a good data model, in combination with a well-designed data architecture, enables any staff member in your organization to efficiently access and consume information (that perhaps they didn’t even realize was being collected!) to your strategic advantage.
More so, with an effective enterprise data model, you can integrate your organization's existing information systems, as many large R&D businesses have siloed data in a variety of systems that do not communicate with each other. By modeling the data in each of these systems, you can see relationships and redundancies, resolve discrepancies, and integrate disparate systems so they can work together. This integrated enterprise data model provides consistent contextualized information, data lineage and a single version of truth for data queries and reporting.
Finally, a well thought-out data model can give you deeper insights into your business area, as the modeling process requires your team to define the data that drives it. The data and relationships represented in a data model provide a foundation on which to build an understanding of business processes. It enables seamless onboarding and blending of external data with your organization's in-house counterpart, enriching the value of analytics and predictions that will help your business navigate challenges and opportunities.
Tips to avoid common data modeling pitfalls
First, make sure you have specific goals. If the business use case for developing the model is not well-defined, the data model will not deliver its optimal value. So, think carefully about what you are hoping to achieve with your data-modeling program, and focus around a particular business need or process improvement.
Once a consensus has been reached, determine whether a top-down, bottom-up or hybrid approach is best to take. Matching the right factors with the correct modeling approach will dramatically increase the chance of having a successful model. Select the data modeling approach based on the business use case and underlying technology, as each kind of data model has its own set of strengths and uses.
When designing your model, aim to make it as simple and close to real life as possible. Avoid speculative content—a data model must fully address the requirements, but not greatly over-engineer them. Seamless and open communication between all teams and stakeholders is extremely important for successful data modeling, ensuring that all data elements are incorporated and have the same meaning and interpretation across the organization.
Your data model should not be designed to solve a single problem, but a variety of problems by taking a more holistic view of all data element in the ecosystem. With this approach, an efficient data model will be able to solve well-defined problems, as well as those not yet described.
Finally, realize that your data model is a living artifact that requires updating and maintenance, e.g., making sure that changes made to any level of the data model are reflected in other levels as well. Although most data models require very little maintenance, a formal process for keeping the model up-to-date is critical.
Real-world applications of data modeling in R&D
So, how would designing a data model work in practice? As an example, consider a global animal health company that recently launched a line of livestock and pet foods. To create their data model, all relevant data elements must be accounted for, such as text content, chemical structures, drug/target relationships, taxonomic animal names (kingdom, phylum, genus, etc.), financial values, along with schematics, graphs and charts, and many others. With these multifaceted data sets, it's easy to understand the complexity of data modeling.
In complex R&D cases like this, it is important to strike a balance between a data model that is detailed enough to be useful, but also straightforward so it can be easily understood. A further consideration is that the data model should allow for flexibility, as changing processes may require it to be reworked over time.
Considering these needs, a theoretical data model for this animal nutrition example could look something like this:
This model defines data elements and relationships in a standardized way so that the organization's data is interconnected. It is particularly important that your model includes business and master data to support overarching enterprise processes and reporting. If you'd like to take a deeper dive into this data model example, read our whitepaper for further details and considerations.
Preparing you for future digital developments
The digital revolution is in full swing and data modeling has become more influential than ever before. As such, R&D organizations should aim to fully leverage the value of their data assets by making data modeling a priority. All the hard work of setting up your data model will eventually pay off when you have a robust data foundation for building solutions that give your organization a competitive advantage.
If you are looking to build an enterprise data hub that accurately classifies your data, and would like more information, download our whitepaper on the digital opportunities of the future. In it, we discuss the importance of data modeling, as well as the current state of digitalization in R&D in more depth.
CAS, a division of the American Chemical Society, partners with R&D organizations globally to provide actionable scientific insights that help them plan, innovate, protect their innovations, and predict how new markets and opportunities will evolve. Leverage our unparalleled content, specialized technology, and unmatched human expertise to customize solutions that will give your organization an information advantage.