Alterman Inc.

Deborah Reinagel

Business Intelligence Innovator

Create More Room for Intentional Thinking

Working with data strategy has opened my mind to see what’s possible.

When I see manual processing, repetitive tasks or an accountant or executive struggling to find data, my first thought is that there’s an easier and more efficient way.  Manual systems almost always guarantee mistakes due to human error.

Experience has taught me that technology, paired with a strong understanding of business, creates tremendous opportunities. People shouldn’t have to spend hours gathering data or repeating tasks that can be automated. Their time is better spent understanding what the data means and deciding what to do with it.

For me, automation is about creating room for more intentional thinking.  Having time to analyze, ask better questions, develop hypotheses and test models is what helps a business grow. When we reduce the effort required to prepare information, we give people more time to turn that information into insight.

Keep the Business in the Loop

Everything starts with understanding the business requirements. If we miss that foundation, even a technically sound report can deliver the wrong answer.

Data quality checks are essential, but those checks must reflect how the business operates. We need to understand how source systems are used, what the expected business logic is and how relationships between data sets should work.

At Alterman, our team supports a diverse enterprise across markets and verticals, from operations to the back office. To build those business partnerships, I established a federated approach to data, identifying data stewards within every department to partner with our team in developing data products, including automations. These stewards bring an understanding of their applications, processes and business logic. The initiative has helped us gain traction by keeping business expertise close to the work and ensuring that the problem drives the solution, including whether automation is the right choice.

Keeping those partners involved throughout development helps us catch misunderstandings early and avoid overdeveloped reports or applications that look great but have no impact on the business needs. Documentation also matters at every stage. We capture decisions and lessons learned, including what didn’t go as planned, so each project strengthens the next.

  We’re using AI to support quality assurance and code reviews, explore alternatives and challenge our thinking. I see it as another colleague at the table, one whose work still requires review, oversight and human accountability.  

We schedule all our data products for quarterly reviews. We revisit their scope, confirm that the business logic still applies and check for changes in requirements, processes or use. Our relationships with data stewards help us stay connected to what’s relevant. Accuracy is essential, and continued relevance is what keeps a report useful.

Solve the Problem before Building

I start by understanding the requirements and the underlying problem. Some requests point to a people or process issue that automation alone won’t resolve.

Before we build anything, I ask: Who is requesting this? What business problem are we solving? What question will it answer? Why is it needed? How does the current process work? How much time could automation give back?

Those questions help us determine whether automation is appropriate and where human judgment needs to remain involved. Repetitive, clearly defined steps are often strong candidates. Decisions that require interpretation, exceptions or business context need an accountable person engaged in the process.

My approach has always been business first. I see myself and my team as partners with the business. We need to understand its struggles, goals and expectations before choosing a solution. That partnership is essential to creating data products people will use and find helpful.

Give Leaders Time to Think Deeper

Automated reporting frees up time for deeper analysis and puts information within reach when leaders need it.

I’ve seen how hours of mining, preparing, and reconciling data can drain a person’s energy before the analysis even begins. By the time the information is usable, they may have less time or motivation to explore its deeper meaning. If they discover they need more data, fatigue and deadline pressure can lead to shortcuts.

Automation reduces that burden. It gives people the opportunity to focus on the question, investigate the findings and apply their judgment.

For senior leadership, timing matters. There isn’t always the luxury of waiting two days for an analyst to gather and prepare information. A decision may need to happen during a meeting or while an opportunity is still available. Reliable automated reporting helps leaders evaluate their options sooner, with more time to consider the implications. In a large business, that can have a significant financial impact.

Treat AI as a Colleague, Not an Authority

AI is disrupting how we work, bringing both opportunities and challenges. I view it as another tool, and it has pushed my engineers and analysts to be more creative about how they approach their work.

We’re using AI to support quality assurance and code reviews, explore alternatives and challenge our thinking. I see it as another colleague at the table, one whose work still requires review, oversight and human accountability.

AI is also changing where analysts contribute the most value. With well-modeled data, more routine questions can be answered through self-service tools. That creates an opportunity for analysts to spend more time framing business questions, interpreting results and helping people act on what they learn.

It has also made our engineers more deliberate about how we architect and structure our enterprise data warehouse. We’re building toward what I call “AI-ready” data: models with clear definitions, consistent relationships and enough business context to support AI-enabled tools.

Finally, we’re applying AI within our own data and engineering technology stack to assess how well our processes work and where we can improve. We’re using it to improve our own work while exploring how it can help the business.