Most information professionals within organizations expect to be working in a group with other info pros and librarians. I recently spoke with one information scientist at an international pharmaceutical firm who leads a team of information professionals who reside within a data science group. As he explained, “there are five groups within the data science organization, and we all exist within the research arm of the company. Our role is to bring augmented data science capabilities to all the project teams within the discovery space.”
When asked about the specific role of the information science team, he described it as being focused on supporting colleagues within the data science groups by enabling access to external information and data and in developing knowledge graphs and deeper machine-learning analysis to support the researchers’ activities. In addition, his group looks at how scientists search for and discover critical information, in order to create new curation, discovery and analysis tools for their day-to-day searches.
One challenge of information centers within enterprises is in finding a way to become integrated into the workflow of research teams. During an internal reorganization, this information scientist saw the greatest potential for his group would be to work much more closely with the data science organization. That has enabled his team to bring their core expertise—library and information science—into each project.
In fact, he noted that “our profession has always been focused on how we can contribute, where we fit in a project lifecycle. In that sense, nothing has really changed from 20 years ago. Yes, the technology space has evolved and our skill sets have evolved, but the fundamental value we offer is our unique expertise in information science. The data scientists respect us as the experts in external data and information just as we respect them as the experts in their domain. Whenever they begin to map out a project, they bring an information scientist from our team in to ask us what kinds of resources we could provide and to spell out their information requirements. I think it is critical that we are always offered a seat at the table; we don’t have to fight to be brought into a project at the beginning.”
Establishing a Better Working Relationship
I asked him what advice he would offer to information professionals who have not yet established these kinds of collaborative relationships with the data scientists within their organization. His suggestions included:
- Make sure you elevate your capabilities in your organization’s core domain. Watch for new resources and tools, and share what you learn with the data scientists. Look for opportunities to bring your expertise to the forefront and highlight your familiarity with external information sources and licensing issues.
- Often the data scientists are new to the organization—sometimes, they are right out of university—and they may not be aware of the role you can play. Your messaging around the value of information science and what information scientists can bring to the table is critical.
- Be open about the fact that you’re not a data scientist, and you are not in competition with them. Sometimes you will be brought into projects in areas with which you are not familiar and much of the discussion at team meetings might go completely above you. But that’s OK—by building relationships around openness, trust and respect, everyone appreciates the information scientists for the expertise they bring to the table.
- Keep in mind that, while information scientists and data scientists both live in a world of data, the data scientists have a far deeper set of technical capabilities. They understand how to interrogate data, how to manipulate and apply machine learning to the data; they have a technical level of expertise that information professionals don’t have. On the other hand, they may not fully appreciate your expertise in identifying resources, negotiating contracts, or working collaboratively to leverage the value of content.
- Embrace change and take advantage of the opportunities you see. You either sit back and don’t innovate and become irrelevant, or you embrace the change and create your own opportunities. You need to constantly reassess what it is you deliver as an information scientist.
Related to that last point, he noted that, if you had asked him 18 months ago whether he would be working on projects involving artificial intelligence, he would have recoiled in horror. Today, he is managing four AI-focused projects. When I asked how his team reacted to these changes, he described his approach:
“I brought my team together and asked them to list on Post-It notes all the skills that they bring to the table as information scientists. What they didn’t know was that, in another room, I had already created a wall with Post-It notes listing all the skills I knew would be needed to run the AI project. I brought the team into the room and asked them to see how much overlap there was between the skills they have and the skills the project required. We all decided to embrace the change, have some fun and use our strong information science skill sets in these new areas.”
By identifying the ways in which information scientists can best collaborate with data scientists, this information scientist ensures that the organization gets the most benefit from its information staff and resources.
This is the first in a three-part series from Mary Ellen Bates around Info Pros in a Data Driven Enterprise. Subscribe to CCC’s Velocity of Content blog to receive the next installments directly in your inbox.