Information professionals spend a lot of energy wondering where they belong. Are we IT? Are we business? But here's what I've learned: that question is a distraction. The more useful question is simpler and more grounding: What problem are we actually solving, and for whom?
Know Your Purpose First
No sector or domain is confined to itself anymore. Everything is becoming interlinked and cross-functional, and the relationship between IT and the line of business is no different. Rather than getting caught up in organizational divisions, information professionals should anchor themselves in purpose.
What does that mean in practice? Like any solution or service, we deliver IT and information management solutions for a customer, a user, a stakeholder. My advice to information professionals working at this confluence of IT and business is to start with three questions: Why do we do what we do? Why do we need this solution? And what's the simplest way to get there?
When you lead with purpose, you stop thinking of yourself as "IT" or "business" and start thinking of yourself as someone solving a real problem for a real person.
Keep It Simple
Information professionals are techies at heart. We love to solve problems, and we love to solve complex problems. But my second piece of advice is to keep it simple.
If you don't need an algorithm and a rule works, then why not use the rule? If you can create a straightforward automation, then why reach for a probabilistic AI solution? For information professionals, keeping solutions simple, understandable, and relatable to the business is essential. Complexity for its own sake doesn't serve anyone.
Think About Scalability and Fit
It's also important for information professionals to understand how our solutions will scale within the business. We often end up choosing technologies, tools, or infrastructure that isn't compatible with where the business is going. We select data or information behind our solutions that doesn't align with regulations or business needs.
Here's a concrete example. Take a marketing response model that you're building to send campaigns to customers. Whatever data you choose behind that response model typically needs to have regulatory alignment. In a marketing response model, you cannot use certain geographic elements. Unless you're aware that this information cannot be used, you will always end up using it because it gives you the best statistical model response.
But that's not what we're after. As information professionals, we need to provide solutions that are relevant, fit for purpose, and actually deployable. The most elegant technical solution means nothing if it can't be put into practice.
Be the Bridge Through Business Analysis
When I started my career as a data analytics professional, we were taught a very simple equation: Take a business problem, convert it into an analytical problem, create an analytical solution, and convert it back to a business solution.
Information professionals play a critical role in that conversion process, both directions. We translate business needs into technical requirements, and we translate technical outputs back into business value.
This is why I believe business analysis is a key skill for information management professionals. We sit at the confluence of engineering and business. What we really do is create the context around information. We create the meaning around data.
To understand what a typical solution looks like, and to demystify problems from business parlance to technology parlance and back again, business analysis skills are essential. It's not about being purely technical or purely business-focused. It's about being the bridge, and doing that well starts with understanding purpose.
The views expressed by Subhadra Dutta are her own and do not necessarily reflect the views of her employer.
This blog post is based on an original AIIM OnAir podcast. When recording podcasts, AIIM uses AI-enabled transcription in Zoom. We then use that transcription as part of a prompt with Claude Pro, Anthropic’s AI assistant. AIIM staff (aka humans) then edit the output from Claude for accuracy, completeness, and tone. In this way, we use AI to increase the accessibility of our podcast and extend the value of great content.
