The numbers exist. They live in six systems, they disagree with each other, and reconciling them takes a person a week every month. By the time leadership sees a report, the decision window has usually closed. AIS fixes the foundation first, then automates the part that used to take a week.
Data analytics consulting turns scattered data into reporting leadership actually trusts. AIS builds the data foundation first and the business intelligence layer second, because dashboards built on unreliable data get abandoned.
Nobody sat down and decided the numbers should live in six places. It happened one system at a time, and now closing the books on what actually happened takes a person a full week every month, matching records that spell the same customer three different ways.
By the time that report reaches the desk of whoever has to act on it, the decision it was meant to inform has usually already been made some other way.
Mapping source systems, identifying where definitions diverge, and establishing a single version of the numbers the business runs on.
Replacing manual reporting cycles with automated pipelines, so the report is current when someone opens it rather than current as of three weeks ago.
Reporting built for the person who has to make the call, not for the person who built the database.
Reporting built to be examined, with the lineage and audit trail regulated industries require.
Analytics work is the prerequisite for AI work. We build the data foundation that makes an AI initiative something other than a pilot that never ships.
AIS rebuilt CMS reporting for a large research-based pharmaceutical company, replacing a manual, fragmented process with automated reporting and real-time visibility.
Most engagements begin with a conversation about what leadership needs to see and cannot currently get. From there we work backwards into the source systems, resolve the conflicts, and build only as much as the decision actually requires. Big-bang data programs tend to die before they deliver anything, so we sequence for something useful early.
We will tell you honestly whether we are the right fit, and what we would do about it if we are.
Start a conversationDesigning the models, pipelines and reporting that let leaders answer questions directly, without asking someone to build a spreadsheet first.
Almost always because the foundation was skipped. If the same number differs across three systems, no visualization layer will fix it.
Sometimes. It depends on how many systems hold a version of the truth and how often they need reconciling. That is one of the first questions an assessment answers.
Yes, and it should. A reliable data foundation is the prerequisite for anything AI-related that survives past a pilot.
A clear picture of where your data lives, what it can support today, and a prioritized plan for what to fix first.